The Open Data Commons (ODC) is a cloud-based community-governed repository to store, share, and publish research data on Spinal Cord Injury (odc-sci.org) and Traumatic Brain Injury (odc-tbi.org).

WHY share data?

There are several challenges to scientific reproducibility and bench-to-bedside translation. For example, only research and published data are disseminated, a phenomenon known as publication bias. Published research reflects only a tiny fraction of all data collected. Data that do not lead to publication are largely ignored, hidden in filing cabinets and hard drives. This results in an abundance of inaccessible scientific data known as “dark data.” When research is disseminated, it is usually in summary reports of aggregated data (e.g., averages across individual subjects) such as scientific articles.

WHY the ODCs?

The SCI community created the ODC-SCI to mitigate dark data in SCI research. This was followed by the creation of the ODC-TBI repository. The ODC aims to increase transparency with individual-level data, enhance collaboration, facilitate analytics, and conform to increasing mandates by funders and publishers to make data accessible. Members of the ODCI have access to a private digital lab space managed by the PI or multi-PIs for dataset storage and sharing. The PIs can share their labs’ datasets with the registered members of the ODC community and make their datasets public and citable. The ODC implements stewardship principles that scientific data be made FAIR (Findable, Accessible, Interoperable, and Reusable) and has been widely adopted by the international SCI and TBI research community.

Guides: Jump right in

Follow our handy guides to get started on the basics as quickly as possible:

Fundamentals: Dive a little deeper

Learn the fundamentals of ODC and FAIR:

The spirit of the ODC is to promote the open exchange of data, tools, and ideas to accelerate treatments and cures for spinal cord and traumatic brain injuries

As far as possible, we adhere to Open Science Principles*:

• Research data, data sets, databases, and protocols should be in the public domain.

• This status ensures the ability to freely distribute, copy, re-format, and integrate data from research into new research, ensuring that as new technologies are developed that researchers can apply those technologies without legal barriers.

• Scientific citation, attribution, and acknowledgment traditions should be cultivated in norms.

*These principles were drafted by Science Commons and presented at Policy and Technology for e-Science, a satellite workshop in conjunction with the Euroscience Open Forum (ESOF) 2008.

This documentation has been created to guide you on how to work with the ODC. You can navigate the main topics, such as tutorials and guides, using the left navigation bar or search for topics on the search box.

Usuful sections to get help are:

• FAQs

• Glossary

• How to contact us for help

What are the different account types on the ODC and why are they needed?

We have several different authentication steps and account types in the ODC to help protect unpublished data. You gain more access and functions as you are approved for the next account type. Get more information here

What is FAIR data?

FAIR stands for Findable, Accessible, Interoperable and Reusable. Learn more about FAIR data

Is there a data quality check process?

Yes, in the ODC, the dataset, dataset metadata, and data dictionary undergo quality checks for proper formatting and completeness. The checks ensure that the data is Interoperable and Reusable. Some quality checks are performed during the upload of the dataset, ensuring a minimal level of quality to all private and public datasets. The check during the upload process is automatic without human oversight since the upload is handled privately within the user’s account. When data is released to the Community data space or submitted for publication, further checks will be conducted by the ODC Data Team to ensure that the released or published dataset meets FAIR standards.

How to cite my dataset?

Citations in Text/Talks:

• always use the full http address with the dataset DOI number when referring to your dataset, for example:

http://doi.org/10.34945/F5XS31

Citations in Methods:

• insert the following dataset citation statement in the methods and data availability statement of your manuscript:

The dataset supporting this article is available at the Open Data Commons for

Spinal Cord Injury (odc-sci.org), http://doi.org/10.7295/W97942VQ.

Citation in Bibliography:

• insert the full dataset citation in the reference section of your manuscript, you will find this full dataset citation on the public dataset page, below the title of your dataset, for example:

Schmidt E. K., Raposo P. J F., Vavrek R., Fouad K. (2021) Lipopolysaccharide treatment in the subacute stage of cervical spinal cord injury enhances motor recovery and increases anxiety-like behavior in female rats. Open Data Commons for Spinal Cord Injury. ODC-SCI:459. http://doi.org/10.34945/F5FW2B

How to cite ODC?

Please cite the publications below in manuscripts using Open Data Commons for SCI or TBI for data collection and management. We recommend the following language:

Study data were deposited to ODC-SCI hosted at University of California, San Diego. ODC-SCI/TBI is a secure, cloud-based repository platform designed to share research data.

ODC-TBI:

Chou A, Torres-Espín A, Huie JR, Krukowski K, Lee S, Nolan A, Guglielmetti C, Hawkins BE, Chaumeil MM, Manley GT, Beattie MS, Bresnahan JC, Martone ME, Grethe JS, Rosi S, Ferguson AR. Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research. Neurotrauma Rep. 2022 Apr 5;3(1):139-157. doi: 10.1089/neur.2021.0061. PMID: 35403104; PMCID: PMC8985540. Grant support: NIH U24NS122732 For ODC-TBI: RRID:SCR_021736

ODC-SCI:

Torres-Espín A, Almeida CA, Chou A, Huie JR, Chiu M, Vavrek R, Sacramento J, Orr MB, Gensel JC, Grethe JS, Martone ME, Fouad K, Ferguson AR; STREET-FAIR Workshop Participants. Promoting FAIR Data Through Community-driven Agile Design: the Open Data Commons for Spinal Cord Injury (odc-sci.org). Neuroinformatics. 2022 Jan;20(1):203-219. doi: 10.1007/s12021-021-09533-8. Epub 2021 Aug 4. PMID: 34347243; PMCID: PMC9537193.For ODC-SCI: RRID:SCR_016673Grant support: WfL & CHNF, NIH U24NS122732

Link to articles:

• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8985540/

• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9537193/

Who can upload data on ODC?

Any registered lab member can upload data to the ODC lab they belong in. The PI or any lab members they designate (as lab managers on the ODC) can share, release, and publish datasets. For more information, see “What are the different account types on the ODC-SCI?”

What type of data is accepted?

Any Spinal Cord Injury or Traumatic Brain Injury associated data that can be disseminated in a spreadsheet (csv) format is accepted. This includes in vivo and in vitro data. Human data must be de-identified prior to uploading to the ODC.

Should I upload and publish raw data or analyzed data?

We encourage publishing primary data: minimally processed data that provides the most flexibility and usefulness for additional analysis. Importantly, primary data is not always raw data but may have some minimal transformation to make the data more directly usable.

If the data has been processed, we recommend explaining the methodology in a dataset-associated methodology document which you can upload alongside the dataset.

Do I have to format my data for upload?

Yes. Before uploading a dataset to the ODC, your dataset must be formatted based on ODC specifications. See Getting your data ready for more information.

What is a Data Dictionary?

A data dictionary is a file containing information about each variable (i.e. Column) in the dataset. The data dictionary provides critical information for the interpretability and reusability of the dataset. Importantly, the data dictionary helps other users understand what each of your variables is and any important details you include. We encourage you to submit a data dictionary with your dataset, even if you do not plan to publish the data. Check this to know how to get the data dictionary ready

What is a CSV file format?

“CSV” (or ".csv") is a widely-used file format for spreadsheet-style datasets and stands for “comma-separated values”. In brief, a CSV is a delimited text file where each value (i.e. cell of the spreadsheet) is separated by a comma.

We require datasets and data dictionaries to be CSV files when you are uploading to the ODC-SCI. You can easily convert excel (e.g. ".xls", ".xlsx") files to “.csv” files through spreadsheet programs like Excel by saving as a ".csv".

Importantly, the process will only save the ACTIVE spreadsheet in your excel file. The process will exclude any graphs or graphics since the CSV file will only include the values in the spreadsheet cells. For more information about how to format your dataset, see the Getting your data ready

Can I deny the use of my dataset in someone else’s publication?

Only if the dataset has not been published with an assigned DOI. Once a dataset has a DOI and has been published (i.e. moved to the Public Space), the use of the dataset falls under the Creative Commons Attribution License (CC-BY v4.0), which allows anyone with access to use the contents of the dataset but sets the legal obligation of giving appropriate credit to the authors of the data.

What if I need to make changes to a published dataset?

PIs can request permission to make changes/updates to published datasets from their lab. In order to maintain proper data provenance, if a published dataset needs to be edited, please contact the ODC Data Team (data@odc-sci.org) with an explanation of the changes you wish to make. The Data Team will assess the proposed changes on a case-by-case basis to inform you of how the changes will be applied and guide you through the process.

For more information, refer to the ODC Version Control Policy.

Can I delete my published dataset?

Once a DOI and landing page have been published to the public on the ODC, we will not delete the information. However, PIs can ask for the dataset and associated data dictionary and supplementary files to be retracted. To initiate the process, please contact the ODC Data Team (data@odc-sci.org) with an explanation of why the dataset needs to be retracted. The Data Team will assess the request and help you through the process.

For more information, refer to the ODC-SCI Version Control Policy.

Do I need programming skills to submit, share, or publish the datasets?

No. No programming experience is required in order to upload your dataset. You can format your dataset in any spreadsheet software before uploading, and all the steps of the process are handled directly on the website.

In case you need assistance with anything during the process, you can contact us via the Help Desk button on the bottom right of every page or by emailing us (info@odc-sci.org).

What happens to my dataset if I am no longer part of the lab/change labs?

The dataset that you upload is the property of the lab, and the PI of the original lab will maintain full control of the dataset on the ODC-SCI.

I have missing scroll bars while using the platform

If you are using a Mac, some of the scroll bars on the platform (e.g., in windows listing available datasets or lab members) might not show up for you because of your computer settings. For example:

To fix this issue:

1. Go to the General settings of your Mac.

2. Find the "Show scroll bars" option.

3. Change the setting to "Always".

Help Desk

If you can’t find a relevant help section for the page you’re on or need to report a bug, you can contact our help desk via the “Contact help desk” button at the bottom of every page. The button will automatically inform us which page you are on when contacting the help desk.

Contact Help Desk is at the bottom right of every page.

If you are reporting a bug, please also include the following information:

• Operating System

• Browser/Version

• Steps you took leading up to error

Please allow 2-3 business days for a response.

General Contact

If you cannot find a relevant help or FAQ section for your question, you can email us at: info@odc-sci.org.

Please allow 2-3 business days for a response.

Signing up

ODC was designed to allow you to share data privately with your lab mates only, colleagues in the ODC community and publicly (with DOI) with the scientific community at large. Registering for an account is simple, but access to data upload and sharing functions requires additional authorization by Community Moderators. Data sharing is managed at the level of the laboratory, so you must be part of a lab for full privileges. The authorization process was designed in response to community input and involves a 3 tier approval process for full privileges.

Register as a basic member (Basic Access)

If you want to become an ODC user, the first thing is to register and create an account with the ODC. You can do this from the main homepage, odc-sci.org or odc-tbi.org, by clicking on the register link on the top right corner of the page. You can also follow these links to register to ODC-SCI or register to ODC-TBI.

Become a Community Member (Community Access)

In most cases, registering and becoming a Community Member (Community Access) is done at the same step during creating an account through the ODC "Create Account". This step requires the registration process described above plus an approval by the ODC Community Moderators. This usually takes less than 24h.

Become a Full Member (Full Access)

To gain Full Access of the ODC you need to have an account as a Community Member first. Then you can become a Full Member by Joining an existing lab and/or Registering a new lab through your dashboard. ODC Labs are the private virtual spaces where your data is uploaded to and stored.

Join a Lab

1. To join a Lab, once logged into ODC, click on the Register/Join a Lab link on the left navigation bar (under My Labs) or the blue button displaying Register/Join in the middle of the screen.

2. Navigate through all the available labs at the right side of the screen.

3. Request to Join a Lab of your choice. The PI or manager of the lab will get an e-mail notification of your request and will have to approve you by following the provided link. This approval is PI dependent. If you do not receive approval, contact the PI directly. In case the PI did not receive the notification email, please have them check their junk email folder before contacting us at info@odc-sci.org

Register a new Lab

To register a new lab, once logged into ODC, click on the Register/Join a Lab link on the left navigation bar (under My Labs) or the blue button displaying Register a new lab in the middle of the screen. Once you submit your information by clicking on the Register a New Lab link the Community Moderators will have to approve your request. This usually takes less than 24h. After that you will have access to the lab you just created which will allow you to upload data.

The ODC is organized by virtual laboratory spaces. We have created a laboratory space for anyone that wants to try the ODC functionalities. Visit the ODC demonstration lab at the ODC-SCI demo lab or the ODC-TBI demo lab. You can request to join the demonstration lab once you register for an account with the ODC and play!

A demo dataset

You can use this pre-clinical demo dataset and data dictionary to try and play with the ODC functionalities in the demonstration lab.

ODC-TBI demo dataset

ODC-SCI demo dataset

For advanced users

The demo dataset is great to get started, but your data is probably more complex. Below we direct you to public datasets that are a great example of how to get your data ready for sharing! You will need to log in to your account to download them.

• Chou A., Krukowski K., Morganti J. M., Riparip L. K., Rosi S. (2022) Peripheral monocyte and resident microglia number and cytokine expression at subchronic timepoints after controlled cortical impact TBI in adult and aged mice. ODC-TBI:376 http://doi.org/10.34945/F5T595

• Vonder Haar C., Martens K. M., Frankot M. A. (2022) Combined dataset of Rodent Gambling Task in rats after brain injury. ODC-TBI:703 http://doi.org/10.34945/F5Q597

• Mifflin K. A., Brennan F. H., Guan Z., Kigerl K. A., Filous A. R., Mo X., Schwab J. M., Popovich P. G. (2022) Lung immunity after complete thoracic spinal cord transection in female mice. ODC-SCI:735 http://doi.org/10.34945/F52G69

To upload data to the ODC we recommend you gather the information you need first! You will also be able to change things as you go, but having everything ready can help make the process easier. This page would guide you through the process of preparing your data.

What you need:

• An ODC Full member account. If you don't have one you can start here

• A dataset file in .csv format (comma separated values) containing the values of your data. Check Data format.

• (Optional but highly recommended) A data dictionary file in .csv containing the data dictionary. Learn more about data dictionaries and how to prepare them.

• (Optional but highly recommended) Test your data and data dictionary files for formatting using this ODC-SCI developed tool: https://atpspin.shinyapps.io/Data-quality-app/

NOTES:

ODC-SCI Minimum Variables (CoDEs) - UPDATED!

current version of Community Data Elements (CoDEs) published 02/11/2025

• SubjectID: Unique identifiers for each subject in the dataset

  • formerly Subject_ID

• SpeciesTyp: Species of the subject

  • formerly Species

• SpeciesStrainTyp: Strain of the subject

  • formerly Strain

• Animal_origin: Vendor or origin of the animal

• AgeVal: Age of the subject at start of experiment. If age is available at different timepoints, age is provided at the corresponding time in a corresponding time/timepoint variable

  • formerly Age

• *BodyWgtMeasrVal: Weight of the subject at start of experiment. If weight is available at different timepoints, weight is provided at the corresponding time in a corresponding time/timepoint variable

  • formerly Weight

• *SexTyp: Sex of the subject

  • formerly Sex

• InjGroupAssignTyp: Name or identifier of the experimental group at which the subject was included if any

  • formerly Group

• Laboratory: Name of laboratory, usually the PI

• StudyLeader: Name of person responsible for overseeing project

• Exclusion_in_origin_study: Whether the subject was included in the study that originated the data. 'Total exclusion" if excluded from the entire study, otherwise, specify experiment or measures of which the animal was excluded if any. For example: animals that were run in behavior but maybe tissue is loss and excluded from histological analyses. Reasons for exclusion might be specify in the exclusion_reason variable.

• Exclusion_reason: Reason by which the subject was excluded from the study that originated the data as specified in the Exclusion_in_origin_study variable

• Cause_of_Death: Cause of death (e.g. perfusion/necropsy, died during surgery, euthanized for health reasons, etc)

• Injury_type: Type or model of injury used in the subject (e.g. contusion, complete transaction, partial section)

• Injury_device: Name of the device used for the injury

• Injury_level: Spinal cord level at which the injury was performed including segment (e.g. cervical; C) and number (e.g. C5)

• Injury_details: Other details referent to the injury that might be relevant to understand the severity and type of injury performed

ODC-TBI Minimum Variables - NEW!

The ODC-TBI variables are aligned with the Common Data Elements (CDEs) created by the PRECISE-TBI project (https://www.precise-tbi.org/).

• SubjectID: The subject ID used to uniquely identify an subject within a study

• SpeciesTyp: Type of organism species for a particular subject

• SpeciesStrainTyp: Type of the animal strain (for mice and rat) for a each subject

• *AgeVal: Age of the animal at the time of procedure in weeks. If additional units are used, please include the appropriate Unit of Measurement companion CDE.

• *BodyWgtMeasrVal: Value of measurement of animal weight; use the appropriate companion Unit of Measurement CDE.

• SexTyp: Type of organism sex as determined by observation

• InjGroupAssignTyp: Type of group assignment for an individual subject

• TBIModelTyp: Type of traumatic brain injury (TBI) model(s) used to induce the mechanism of TBI.

• InjElapsedTime: The elapsed time from the time of injury. If additional units are used, please include the appropriate Unit of Measurement companion CDE.

*Ideally, both age and weight are requested; however, it is understood that weight is commonly recorded as a proxy for age in preclinical subjects.

ODC supports uploading spreadsheet data in .csv (comma-separated values) file format. You can save your data to .csv format using the most common spreadsheet software, such as Excel.

The tidy format

ODC uses tidy formatting of a spreadsheet or tabular data. The basics are simple: each row is an observation, and each column is a measure, field, or variable. A tidy data format is a great way to make data shareable and understandable by humans and machines!

The following two images illustrate how to create a .csv file with data organized in the tidy format.

General considerations when formatting your data for ODC

• Unique subject ID column: ODC is organized around subjects. One of your dataset columns must contain the subject or animal identifier (e.g. Subject_ID). This identifier should be unique for each subject. If subject_1 represents two different animals present in two different experiments, the identifier is not unique.

• Columns as Variables: Each column represents a study parameter, outcome measure, field or variable

• First row lists the Variable names: The first row of the dataset contains the name of the columns.

• Subsequent rows as observations: Each row represents a single observation for a single subject.

  • A subject could have multiple rows. For example, your dataset might include multiple timepoints for each subject, in which case each row might represent a unique observation of a subject at a specific time point. The Subject ID column will help identify all the data for each specific subject in the dataset.

• Column/Variable name requirements (based on best practices):

  • Keep variable names short. Avoid variable names longer than 60 characters.

  • Variable names must start with a letter.

  • Variable names should be intuitive (e.g. use “Date_Birth” instead of “DB”).

  • Avoid spaces in variable names. Use underscore (“_”) instead.

  • Avoid special characters except underscores (“_”) and periods (“.”). If you must use special characters, verify the corresponding Variable and data are uploaded correctly.

• No duplicated Variable names: Every column header must have a unique name. If two of your columns have the same name, you will receive an error during data upload.

• Avoid the use commas: CSV files use commas to separate the contents of one cell from another. If you use commas in a cell, it may be read as a delimiter character (i.e. cell separator) which can lead to errors in data upload. If you must use commas, always double check that your data is uploaded correctly. Microsoft Excel can also save csv's in such a way to prevent misinterpretation of commas in your data. However, we generally recommend avoiding the use of commas in your dataset altogether.

A data dictionary, also known as a codebook, provides information about the dataset variables. It is one of the most important pieces of information to include with a dataset for anyone who wants to interpret and reuse the data. Even if you are not planning on releasing your data, it is encouraged and of good data management practice to have data dictionaries for your datasets. You may know now what a variable name means in your spreadsheet (e.g., jtemp_6), but will your PI or colleagues know when you leave the lab? Will you know if you try to reuse the data two years from now? A data dictionary is a critical lab asset that ensures the data that have taken great effort and resources to acquire will not go to waste in the future due to poor documentation.

The ODC data dictionary

The data dictionary used by the ODC is a .csv file (a comma separated value file). Learn more about .csv files here.

The data dictionary file must contain the following column names in the first row:

• VariableName: * Variables (i.e. column headers) that appear in the dataset. You must include all of your dataset variables in the data dictionary. Tip: Select your variable row in your dataset file and Copy, in the data dictionary file in cell A2, Paste Special>Transpose, all your variable names should be pasted into the first column of your data dictionary file now.

• Title: * Title is the full name of the variable when the VariableName contains abbreviations or shorthand. If the VariableName is already a complete name, you can copy and paste the VariableName into the Title entry.

• Unit_of_Measure: Units for the variable (if applicable).

• Description: * Definitions and descriptions of the variable. The description should explain what the variable represents in enough detail such that a reader can understand the contents of the column in the dataset.

• DataType: Specify whether the variable specifically contains Numeric, Categorical, Ordinal, Date, or Free Text data.

• PermittedValues: If the variable is not numeric or free text, list all possible values here (e.g. "Male, Female" for the variable "Sex"). If the variable is numeric or free text, can leave this blank (use MinimumValue and MaximumValue columns).

• MinimumValue: If the variable is numeric, list the Minimum possible value. For example, if you expect a variable to be between 0-100, write 0 for MinimumValue. If there is no minimum value, leave this blank.

• MaximumValue: If the variable is numeric, list the Maximum possible value. For example, if you expect a variable to be between 0-100, write 100 for MaximumValue. If there is no maximum value, leave this blank.

• Comments: Additional notes such as exclusion criteria, reasons for special values, etc.

In ODC, the dataset and the data dictionary undergo quality checks for proper formatting (based on goodTables framework). These checks ensure that the data is Interoperable and Reusable with other datasets. Some of the quality checks are performed during the uploading of datasets, ensuring a minimal level of quality to all private and public datasets in the ODC-SCI. The check during the upload process is automatic without human oversight since data upload is handled privately within the account of the data owner. When data is released to the Community data space or submitted for publication, further checks will be conducted to ensure that the released or published dataset meets FAIR standards:

• Source errors (Checked at upload): ODC-SCI can not read the data file. Possible reasons include:

  • The data file is not a *.csv. The ODC only accepts upload of *.csv data files.

  • Reserved special characters were used in the column headers (first row with the variable names). Check our recommendations for How to upload data.

• Structure errors:

  • Blank-header (Checked at upload): There is a blank variable name. All cells in the header row (first row) must have a value.

  • Duplicate-header (Checked at upload): There are multiple columns with the same name. All column names must be unique.

  • Blank-row (Checked at upload): Rows must have at least one non-blank cell.

  • Duplicate-row: Rows can not be duplicated.

• Schema errors: In ODC-SCI the schema is marked by the data dictionary. These errors reflect conflicts between the data dictionary and the dataset.

  • Extra-header: The dataset contains at least one variable name not defined in the data dictionary.

  • Missing-header: The dataset is missing at least one variable name defined in the data dictionary.

  • Missing-definition: The definition of a variable in the data dictionary is missing.

  • Required-constraint (Checked at upload): A required field for the dataset contains no values or is not assigned on the dataset. Currently the only required value in the datasets is the subject identifier. As ODC-SCI develops additional data standards, it is possible that more variables will be required on all datasets.

  • Value-constraint: The values of a variable should be equal to one of the permitted values enumerated in the data dictionary, or within the limits of the permitted values.

There are three different types of files that you can upload to the ODC:

• The dataset file in .csv format

• A data dictionary file in .csv format

• Supplementary files in .doc or .pdf

ODC is organized around datasets. So, we need to start by uploading a new dataset

What you need

• An ODC Full member account. If you don't have one you can start here

• A dataset file in .csv format (comma separated values) containing the values of your data. Check Data format.

• (Optional) A data dictionary file in .csv containing the data dictionary. Learn more about data dictionaries and how to prepare them.

Step-by-Step

1. Log in ODC

2. Go to Upload a New Dataset in the left navigation bar

3. Verify that you are in the Lab that you want to be and click Next

4. Select your .csv file and enter the dataset information. The dataset name is an internal name for you and your lab to remember what the dataset is about! The dataset description helps to describe the content of the dataset

5. Preview the dataset and select subject ID column. The ODC organizes the data around subjects! Make sure you include a unique subject ID column

6. Upload!

You can upload a data dictionary in ODC format any time while you are working with your data. The steps are simple!

Step-by-Step

1. Log into ODC, in your dashboard scroll down to the list of datasets. Identify the dataset you want to upload the data dictionary for and click on it

2. In the dataset view page, select "Upload Files", and click on Data Dictionary

3. In case the upload fails, please ensure that the dataset variable names exactly match (spelling, caps) the variable names in the data dictionary, see more common errors.

3. In the Data dictionary and methodology page you can follow the instructions and select the .csv file to upload your data dictionary

After uploading a data dictionary or methodology file, you can track the associated files to a dataset

Methodology files are great to attach narrative and protocol descriptions associated to a dataset. You can upload a methodology file in either .doc or .pdf form following the same process described to upload a data dictionary

Currently, the ODC do not require methodology documents for you to upload and publish your datasets. However, we encourage you to include them as they improve the interpretability and reusability of your data.

Some recommendations for compiling a methodology document:

• If the dataset comes from a published paper (and hence has the methodology already published), you can provide the paper citation and link as part of the Methodology doc/pdf. Due to potential copyright issues, do not copy/paste the methods from any published papers.

• If you have multiple protocols you wish to upload for the to the same dataset, we recommend combining them all into a single word document or pdf. The ODC only allows a single Methodology file to be attached to each dataset.

Supplementary files contain extra information that you may want to attach to a dataset. For instance, you may want to have an image of a graph saved in association with your data.

File Formats: .pdf, .csv, or images (.jpg, .png and .gif)

How: You can upload supplementary files following the same procedure for uploading a data dictionary

Dataset Upload Errors

A few common errors are flagged during the data upload process. If you hit an error on the data preview page after selecting your datafile, check your dataset for the following errors:

• Your datafile is not a .csv file. For more information, see our FAQs

• Your datafile contains a column with an empty header (i.e. missing a value in the first row). You might have forgotten to type a name for a column or have an empty column in the middle of your dataset. Make sure every column of your dataset has a column name in the first row.

• Your datafile contains duplicate header names. Make sure every column must have a unique column header.

• One of your column headers has a variable name that is longer than 64 characters. There is a 64 character limit for the column headers; make sure every header is 64 characters or less.

• You used a comma in your datafile, and the .csv file is reading the comma as a separator between one entry and the next. This can be a source of error for header names and data entries. If this is the error, you will find rows that have different numbers of columns and/or entries that have been shifted to other columns.

  • You can fix this error by removing commas from the entries in your datafile.

  • A second way to fix this is to open your .csv in a spreadsheet software like Excel, correct for any shifted cells, and save as a new .csv through the program. Many software programs will naturally treat cells with commas as a fixed sequence of characters and won't treat those within-cell commas as separators between cells. This treatment will be maintained when you upload the new .csv to the ODC.

• Your datafile might be too large. If your dataset is larger than 100Mb or has a total number of cells larger than 3,000,000, see the section "What if my dataset is too large" below.

For more information about how to prepare your data for upload, see the getting your data ready.

Data Dictionary Upload Errors

When uploading a data dictionary file, there are a few possible errors that are flagged during the data dictionary upload process. You will be notified directly on the upload page; as a reference, the possible errors include:

• Your data dictionary is not a .csv file. The data dictionary must be a .csv file for upload.

• Your data dictionary is missing a column. The data dictionary must include all 9 required columns with exact spelling.

• Your data dictionary does not include an entry for every column of your dataset. Every Variable (i.e. column header) in your dataset must have a respective row in your data dictionary. Please ensure that the dataset variable names exactly match (spelling, caps) the variable names in the data dictionary.

• While not an error, if you have rows in your data dictionary that are missing values under Title or Description, this will flag a warning. This is not required for initial data dictionary upload, but during DOI request/dataset publication, we require that every row in your data dictionary have at least VariableName, Title, and Description columns filled out.

For more information on how to prepare your data dictionary for upload, see the data dictionary section.

What if my dataset is too large

If your dataset is too large (e.g., your dataset is larger than 100Mb or has a total number of cells larger than 3,000,000), it can cause an error during the data upload process. The error can also happen when you try to replace your dataset using the update a dataset function. In both cases, we recommend splitting up your dataset-to-be-uploaded into chunks with fewer rows and utilizing the append data option to add your dataset piece by piece.

Importantly, every chunk of your dataset must have the same column headers in the first row of each .csv file. Make sure you (1) split your dataset along the rows and not along the columns and (2) include the column headers in every file.

Once a dataset has been uploaded to ODC, you can manage the dataset by:

• Update a dataset: If new data has become available and you want to add it to the same dataset.

• Add metadata: Metadata form is a useful space to provide extra information about your dataset (required for a DOI request)

• Share data: If you want to share data with others at ODC

You can update any dataset under you control. That is, any dataset you have uploaded or any dataset in your lab space if you have Manager or PI status.

ODC provides two options to update a dataset in the system:

• Upload a new version: This option will remove all the data in your dataset and will replace it with new data. The current data will be deleted

• Appending new rows: This option will add new data rows to the dataset. The current data entries will not be changed. Note: if your dataset was too large to upload or replace, you should split your data file into smaller files and use the Append Rows workflow to upload each piece.

ODC offers a metadata form where you can provide information about the dataset. You can access the Metadata Editor for each dataset from a dataset view page. This information is unique to each dataset.

A quick video

The Metadata Editor

Information you can edit in the form

• Title: Title that will be displayed on the dataset citation. Note that this will not change the title of the dataset visible within the ODC itself. Please include the species, sex, lesion type and area in your title.

• Abstract: The Abstract includes 3 fields: Study Purpose, Data Collected, Data Usage Notes.

  • Study Purpose: Short description of the overall study purpose that resulted in the dataset.

  • Data Collected: Summary of what kind of data is included in the dataset and how the data was collected. Please include important experiment parameters (such as experimental model and injury severity) and critical outcome measures.

  • Conclusions: Summary of conclusions (if any) made with the dataset at the time of dataset publication.

• Keywords: Keywords can be added to allow search engines to locate the DOI and dataset citation once the dataset is published. You can add your own keyword or start typing to see the ones ODC has already registered. You can reorder the keywords after they are added by dragging/dropping them in the list.

• Provenance / Originating Publication: This section allows for entering publications that are related to the dataset. You can either import the information automatically or introduce it manually.

  • Import from existing publication: enter the DOI or PMID. Note that we can only import information from some preprint articles. Check the “Import authors as contributors” checkbox to import the publication’s author list automatically as contributors of the dataset (you will have to assign the dataset author and contact author labels to the respective entries after importing). After import, choose to edit the publication entry and fill out the remaining fields: Citation Relevance.

  • Manual: If you want to enter information manually, you can create a blank entry by leaving the DOI/PMID field blank and hitting “Import/Add Publication.” Choose to edit the new entry and fill out the appropriate fields: DOI, PMID, Citation, Citation Relevance.

• Relevant links: This section allows for adding links to external resources that are relevant to the dataset. For example, if omics data associated to the dataset have been deposited in another repository, the link can be provided here. This section can also be used to link the current dataset to a published dataset in ODC.

• Notes: The Notes section should be used to provide important guidance for others on using your data. Relevant information may include technical issues during the experiment that may require data exclusion, specifics about the techniques that may prevent merging with other datasets, and so on. The goal is to provide information useful to data re-users and prevent data misuse.

• Funding and Acknowledgements: The Funding and Acknowledgements section requires 2 fields for each entry: Funding Agency, Funding Identifier and PI Initials. You can reorder the entries after they are added by dragging/dropping them in the list.

  • Funding Agency: Name of funding agency.

  • Funding Identifier and PI Initials: Respective funding ID (e.g. grant number) and PI Initials in parenthesis. For example: 4F0887Z (AC).

• Contributors / Authors: ODC considers any Author of a dataset a Contributor. Authors will have their names attached to the citation of a dataset if published. Contributors that are not authors are other persons that do not constitute an author but you want acknowledge their contribution to the dataset. Each Contributor/Author has 5 fields to fill out. Each entry is added as a contributor to the dataset by default; if you check the options to include an entry as an author or contact author, the appropriate label will be applied to the contributor/author entry. Each entry includes: First Name, Middle Initial, Last Name, ORCID, Affiliation, and Contact Email (if contact author). Data that was imported might be incomplete and will need to be edited.

  • First Name: Person’s first name.

  • Middle Initial: Person’s middle initial (if relevant).

  • Last Name: Person’s last name.

  • ORCID: Person’s associated ORCID.

  • Affiliation: Person’s associated affiliation at time of publishing the dataset.

  • Contact Email: Person's contact email (field appears only if they are a contact author)

Information that we automatically provided for you

• DOI: This is provided by ODC once you go through the DOI request process

• Dataset Citation: This field provides you with a look at how the citation of the dataset will look like if released to the public. It is constructed automatically from the provided list of authors and the tile. You can see the changes as you change those two pieces of information!

• Dataset Info: Dataset info is automatically populated from the other sections. The section includes: Contact Author information, Lab, ODC Accession Number, Number of Records in Dataset, Fields per Record, number of associated Files.

• License: The License is automatically assigned once and if the dataset is published. All datasets published on the ODC will be under the Creative Commons Attribution License (CC-BY 4.0).

Once a dataset is first uploaded to ODC, only the uploader has access to it. It is located in the Personal Space. Sharing data is the process of moving a dataset from the personal space to a place in the ODC where others can see and download the data (Community Data Space and Public Data Space).

Step-by-Step Data Sharing (for uploaders)

1. Log in to ODC and navigate to the specific dataset that you want to share with others and click on it to open the Dataset page

2. Click "Change" link on the Data Space information located on the top right corner

3. A pop-up window will open, select the space that you want to move your dataset to (ie. Lab Space) and click update. After a moment, the data space status should update to the Lab Space. Now, everyone that is a member of that lab can see and access your dataset.

4. If you want to move the dataset back to the Personal space, use the “Change” link again.

Step-by-Step (for PI and Managers)

1. Make sure you are logged in as the PI or Lab Manager for the ODC lab and check that your Current Lab is correct. If you need to switch labs, use the “Switch Lab” option on the navigation menu.

2. Select “Current Lab” on the navigation menu as the PI or Lab Manager.

3. In the Dataset window, find the dataset of interest. Click on the dataset’s status to open the Change Dataset Status window.

4. Select the corresponding option and select "Update Status". The dataset will be assigned to the selected data space. Check How does privacy work on the ODC to know which users can access data in each data space.

You can get a digital object identifier (DOI) for your dataset when you are ready to release your data to the public. DOIs are persistent identifiers that help to make your dataset findable and for other to cite your work!

Issuing a DOI for your data requires some extra information beyond getting your data ready. You will need:

• A dataset in the ODC that is properly formatted. Check how to prepare your dataset.

• A data dictionary for your dataset in the proper format. Check how to prepare your data dictionary.

• A completed metadata form.

Only PI and Managers can request DOIs since at the end of the process the data will be ready to be released to the public.

Step-by-Step (for PI and Managers)

1. Make sure you are logged in as the PI or Lab Manager for the ODC lab and check that your Current Lab is correct. If you need to switch labs, use the “Switch Lab” option on the navigation menu.

2. Select “Current Lab” on the navigation menu as the PI or Lab Manager.

3. In the Dataset window, find the dataset of interest. Click the "Request DOI" button. Fill out the form, confirm you understand that the Data Team will review your dataset, and submit the DOI request. This will initiate the publication and DOI request process.

In ODC, the dataset and the data dictionary undergo quality checks for proper formatting. These checks ensure that the data is Interoperable and Reusable with other datasets. Some quality checks are performed during uploading datasets, ensuring minimal quality to all private and public datasets in the ODC. The check during the upload process is automatic without human oversight since data upload is handled privately within the account of the data owner. When data is released for publication, further checks will be conducted to ensure that the released dataset meets FAIR standards:

Source checks

(Checked at upload): ODC can not read the data file. Possible reasons include:

• The data file is not a *.csv. The ODC only accepts the upload of *.csv data files.

• Reserved special characters were used in the column headers (first row with the variable names). Check our recommendations for How to upload data.

Structure checks

• Blank-header (Checked at upload): There is a blank variable name. All cells in the header row (first row) must have a value.

• Duplicate-header (Checked at upload): Multiple columns with the same name. All column names must be unique.

• Blank-row (Checked at upload): Rows must have at least one non-blank cell.

• Duplicate-row: Rows can not be duplicated.

Schema checks

In ODC, the schema is marked by the data dictionary. These errors reflect conflicts between the data dictionary and the dataset.

• Extra-header: The dataset contains at least one variable name not defined in the data dictionary.

• Missing-header: The dataset is missing at least one variable name defined in the data dictionary.

• Missing-definition: The definition of a variable in the data dictionary is missing.

• Required-constraint (Checked at upload): A required field for the dataset contains no values or is not assigned to the dataset. Currently, the only required value in the datasets is the subject identifier. As ODC develops additional data standards, more variables may be required on all datasets.

• Value-constraint: The values of a variable should be equal to one of the permitted values enumerated in the data dictionary or within the limits of the permitted values.

The ODC-SCI community has established a unique process and requirements to release a dataset to the public with a DOI. The process may take a few days, but ensures higher quality of the shared data and that data is FAIR.

The final publishing step involves a two-step review process by the ODC-SCI Editorial Board and Data Team. The length of the review process depends on various factors including:

• Whether the Dataset is within the scope of ODC-SCI

• Dataset formatting

• Completeness of the dataset metadata

• Completeness of dataset-associated documents (i.e. data dictionary).

Editorial Review

Your dataset will first be sent to ODC-SCI editors to determine whether the content is appropriate and within the scope of ODC-SCI. You will be contacted by the Editor in Chief within 3-5 business days if the editors have any concerns regarding your dataset.

If the Editorial Board decides that your dataset fits within the scope of the ODC-SCI, the dataset and any comments from the editors will be sent to the ODC-SCI Data Team for the dataset review process.

Dataset Review

The ODC-SCI Data Team will review your dataset, metadata, and data dictionary to ensure the formatting and contents conform to the ODC-SCI data structure and meet the minimum dataset standards for publication. The ODC-SCI Data Team will will contact you if any revisions are required.

The full dataset publication process can take a few weeks depending on the revision process. You can track any changes to the status of your publication/DOI request on the Current Lab page as a PI/Lab Manager.

Final PI Approval

Once the dataset is fully approved by both the Editorial Board and Data Team, the DOI will be reserved and the corresponding PI will be notified in an email. The PI has control over the last step of the publication process: final approval for publishing the dataset and the associated landing page. Once the PI approves, the landing page and dataset will be made public and accessible to all ODC-SCI registered users within 24 hours. The dataset DOI will be accessible on the published landing page.

The PI can find the final approval button on the Current Lab page in the Dataset window once the dataset has completed the ODC-SCI review process.

Releasing your data through the ODC-TBI requires a DOI request. Publication in ODC-TBI is overseen by and Editorial Board and the ODC Data Team. The Editorial Board reviews each dataset to determine if:

• The dataset is relevant to TBI

• The metadata are complete, clear and promote FAIR

• The data dictionary is complete and helps a user to understand the data

The ODC Data Team performs various data quality checks for DOI and works with the submitters to resolve any issues.

The ODC-TBI has adopted the same review process as the ODC-SCI. The process is collegial and designed to increase the Findability, Accessibility, Interoperability and Reusability (FAIR) of each dataset. Briefly, the steps involve:

• Initial checks for completeness of files by the Managing Editor

• In depth curatorial review for quality checks and data formatting

• Review of metadata by 2 Editorial Board members for relevance, completeness, FAIRness and clarity.

• Feedback to the submitter on any required or recommended changes.

There are two ways to add an experimental protocol to your dataset.

1) Upload a text file describing your methodology

2) Add a link to a published protocol. We highly recommend using Protocols.io to share your protocols, but there are other platforms as well.

Uploading a methodology text file

1. Select the data file from your dashboard to which you want to add a methods file

2. Expand the pull down menu under "Upload file" in the menu bar

3. Select "Methodology file"

4. Follow upload instructions

Adding a link to a published protocol

1. Select the data file from your dashboard to which you want to add a methods file

2. Open the "Metadata Editor" from the menu bar

3. Navigate to the "Relevant links" metadata field and click on "Add link"

4. Provide the title of the protocol, the URL and a brief description

5. Click "Add"

Citing the ODCs

Please cite the Open Data Commons in study manuscripts (methods) if you have used the Open Data Commons for SCI or TBI for data collection, management, publication, or download. We recommend the following language:

For ODC-SCI

Study data were deposited at the Open Data Commons for Spinal Cord Injury (ODC-SCI; RRID:SCR_016673). The ODC-SCI is a secure, cloud-based repository platform designed to share research data (Torres-Espin et al., 2022).

Torres-Espín A, Almeida CA, Chou A, Huie JR, Chiu M, Vavrek R, Sacramento J, Orr MB, Gensel JC, Grethe JS, Martone ME, Fouad K, Ferguson AR; STREET-FAIR Workshop Participants. Promoting FAIR Data Through Community-driven Agile Design: the Open Data Commons for Spinal Cord Injury (odc-sci.org). Neuroinformatics. 2022 Jan;20(1):203-219. doi: 10.1007/s12021-021-09533-8. Epub 2021 Aug 4. PMID: 34347243; PMCID: PMC9537193.For ODC-SCI: RRID:SCR_016673Grant support: WfL & CHNF, NIH U24NS122732

For ODC-TBI

Study data were deposited at the Open Data Commons for Traumatic Brain Injury (ODC-TBI; RRID:SCR_021736). The ODC-TBI is a secure, cloud-based repository platform designed to share research data (Chou et al., 2022).

Chou A, Torres-Espín A, Huie JR, Krukowski K, Lee S, Nolan A, Guglielmetti C, Hawkins BE, Chaumeil MM, Manley GT, Beattie MS, Bresnahan JC, Martone ME, Grethe JS, Rosi S, Ferguson AR. Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research. Neurotrauma Rep. 2022 Apr 5;3(1):139-157. doi: 10.1089/neur.2021.0061. PMID: 35403104; PMCID: PMC8985540. Grant support: NIH U24NS122732 For ODC-TBI: RRID:SCR_021736

Citing a dataset

ODC-SCI: Study data [INSERT REFERENCES FOR DATA UTILIZED] were downloaded from the Open Data Commons for Spinal Cord Injury (ODC-SCI; RRID:SCR_016673). The ODC-SCI/TBI is a secure, cloud-based repository platform designed to share research data (Torres-Espin et al., 2022).

ODC-TBI: Study data [INSERT REFERENCES FOR DATA UTILIZED] were downloaded from the Open Data Commons for Traumatic Brain Injury (ODC-TBI; RRID:SCR_021736). The ODC-TBI is a secure, cloud-based repository platform designed to share research data (Chou et al., 2022).

Insert the full dataset citation in the reference section of your manuscript; you will find this full dataset citation on the public dataset page, below the title of your dataset, for example:

Schmidt E. K., Raposo P. J F., Vavrek R., Fouad K. (2021) Lipopolysaccharide treatment in the subacute stage of cervical spinal cord injury enhances motor recovery and increases anxiety-like behavior in female rats. Open Data Commons for Spinal Cord Injury. ODC-SCI:459. https://doi.org/10.34945/F5FW2B

Always use the full https address with the dataset DOI number when referring to your dataset, for example:

https://doi.org/10.34945/F5XS31

Use of Reference Managers

ODC DOIs are compatible with some of the most common reference managers, including:

• Paperpile: Click the Paperpile button in your browser toolbar to import the ODC dataset citation. Dataset url or dataset DOI can also be used to directly import the dataset reference in your folder. For more information on how to use paperpile visit Paperpile guide page

• Zotero: By clicking on the Zotero Connector directly on your browser when visiting a specific public ODC-SCI dataset page you will add the identifying information to your Zotero library.

To add it to Zotero library via “Add Item by Identifier” button () at the top of the center column of the Zotero pane, type or paste in the dataset DOI, and press Enter/Return. You can also paste a list of multiple identifiers (each on a separate line), then press Shift+Enter/Return to finish.

• Mendeley: Save references from the web directly to your Mendeley library with the Mendeley Web Importer or manually import files using the dataset DOI. This will look up the item in the Mendeley Catalog or if the reference is completely new to Mendeley, the details will be retrieved directly from the ODC public page. The Mendeley citation plugin automatically generates your bibliography.

How to approve and promote lab members

Caution: By approving a user to join your lab, you are approving them to access all the datasets in your Lab data space and all the datasets in the Community data space. Please make sure you know who the user is before approving them. If you have any concerns, please contact us at info@odc-sci.org.

Similarly, please be aware that a Manager level user shares many of the same authorizations as the lab PI on the ODC-SCI. This includes sharing/releasing/initiating publication of datasets and approving/removing users from the lab.

1. Make sure you are logged in as the PI or Lab Manager for the ODC lab. On the navigation menu, select “Current Lab”. If the Current Lab is not the lab you want to approve/promote lab members in, you can hover over the "Switch Lab" link on the navigation menu and select the correct lab.

2. In the top right corner, locate the Users window and find the user whose level you want to change by scrolling down. You will see the following buttons:

To approve/reject a user awaiting approval to join:

To promote/demote/remove an existing user:

User type and roles:

A review token allows confidential access to a private ODC dataset that has already been accepted for DOI release. This allows for temporary private sharing of the dataset before public release. For example, these tokens can be provided during submission to a peer-reviewed journal to provide reviewers and editors with access to the data.

Once a dataset submission for DOI is completed and has been approved by curators, the authors are able to generate the private access token - this is a very quick process: Home/Lab (PI name)/Manage Lab:

Start by logging into your ODC account (either SCI or TBI) and navigate as follows:

1. Go to “Home Dashboard”

2. Select the lab from “Lab Membership box”

3. Once in the desired lab, scroll down to “Datasets list in Lab” and find the specific dataset that requires a token.

4. The 'Tokens' request button is located to the right of that dataset title. By clicking on this Token access button, the author will receive the token information via email.

Using the token for providing reviewer access

Authors are instructed to include this token with their manuscript when they send it to the journal for review. The journal editors should ensure that reviewers receive the token. The reviewers then can use the provided link to get access to the specific dataset.

Investigators may include costs involved managing and sharing data according to their DMS. Currently, there is no fee for sharing data through the Open Data Commons for data under 30 Gb in size.

Guidance from NIH NOT-OD-21-015 on allowable costs: Reasonable, allowable costs may be included in NIH budget requests when associated with:

1. Curating data and developing supporting documentation, including formatting data according to accepted community standards; de-identifying data; preparing metadata to foster discoverability, interpretation, and reuse; and formatting data for transmission to and storage at a selected repository for long-term preservation and access.

2. Local data management considerations, such as unique and specialized information infrastructure necessary to provide local management and preservation (e.g., before deposit into an established repository).

3. Preserving and sharing data through established repositories, such as data deposit fees necessary for making data available and accessible. For example, if a Data Management and Sharing Plan proposes preserving and sharing scientific data for 10 years in an established repository with a deposition fee, the cost for the entire 10-year period must be paid prior to the end of the period of performance. If the Plan proposes deposition to multiple repositories, costs associated with each proposed repository may be included.

The question is, how to estimate these costs? In our experience, researchers tend to underestimate the amount of time and effort involved in managing and sharing data. Depending on the institution and the size, nature and complexity of the data, the major costs are usually not storage or access, but rather personnel. If you do not have a dedicated data steward in your lab, you will have to ensure that you budget the required personnel to manage and share the data.

Some resources that can help:

Cost drivers for data adapted from the National Academies of Science report on Lifecycle Decisions for Biomedical Data

• Utrecht University Data Management Cost Estimation Tool: (of course, costs at your institution will be different, but it is a pretty complete guide to factors that should be considered and some cost saving tips).

• NIMH Data Archive (NDA) cost estimation tool: Tool for estimating data submission costs. Some aspects are specific for this repository but many aspects are generic and can apply to sharing through any repository.

• Estimating costs for using commercial clouds. Some good resources and advice can be found here: https://training.incf.org/cloud-based-computer-matrix/costs. Additional resources can be found under “Resources and Tools”.

• Guidance for data management and sharing costs on NIH budget requests, adapted from materials developed by UCSF

Here is a downloadable DMS plan with sample language and recommendations for creating your own DMS plan using the ODC repositories.

Please note that we provide language only for those elements related to ODC. Additional information may be required. Please consult with guidance materials prepared by the NIH when preparing your DMS.

ODC-TBI Sample DMS

See ODC-TBI for additional information about data-sharing policies and how we support them.

ODC-SCI Sample DMS

The ODC has a set of standards designed to ensure data is FAIR (Findable, Accessible, Interoperable, and Reusable). These standards make the data more useful for everyone, including your future self!

We can divide the standards into three types:

• Data Formatting specifications. What type of files to use, and how is data organized in these files?

• Common terminology. A set of terms (e.g., variables) that are pre-defined and allow for all shared data to have a common language. For example, this may include minimal required variables or common data elements.

• Metadata standards. Associated information about a dataset is needed to make the data FAIR. This includes a formatted data dictionary, information to generate digital object identifiers (DOI)

ODC supports uploading spreadsheet data in .csv (comma-separated values) file format. You can save your data to .csv format using the most common spreadsheet software, such as Excel.

The tidy format

ODC uses tidy formatting of a spreadsheet or tabular data. The basics are simple: each row is an observation, and each column is a measure, field, or variable. A tidy data format is a great way to make data shareable and understandable by humans and machines!

The following two images illustrate how to create a .csv file with data organized in the tidy format

General considerations when formatting your data for ODC

• Unique subject ID column: ODC is organized around subjects. One of your dataset columns must contain the subject or animal identifier (e.g. Subject_ID). This identifier should be unique for each subject. If subject_1 represents two different animals present in two different experiments, the identifier is not unique.

• Columns as Variables: Each column represents a study parameter, outcome measure, field or variable

• First row lists the Variable names: The first row of the dataset contains the name of the columns.

• Subsequent rows as observations: Each row represents a single observation for a single subject.

  • A subject could have multiple rows. For example, your dataset might include multiple timepoints for each subject, in which case each row might represent a unique observation of a subject at a specific time point. The Subject ID column will help identify all the data for each specific subject in the dataset.

• Column/Variable name requirements (based on best practices):

  • Keep variable names short. Avoid variable names longer than 64 characters.

  • Variable names must start with a letter.

  • Variable names should be intuitive (e.g. use “Date_Birth” instead of “DB”).

  • Avoid spaces in variable names. Use underscore (“_”) instead.

  • Avoid special characters except underscores (“_”) and periods (“.”). If you must use special characters, verify the corresponding Variable and data are uploaded correctly.

• No duplicated Variable names: Every column header must have a unique name. If two of your columns have the same name, you will receive an error during data upload.

• Avoid the use commas: CSV files use commas to separate the contents of one cell from another. If you use commas in a cell, it may be read as a delimiter character (i.e. cell separator) which can lead to errors in data upload. If you must use commas, always double check that your data is uploaded correctly. Microsoft Excel can also save csv's in such a way to prevent misinterpretation of commas in your data. However, we generally recommend avoiding the use of commas in your dataset altogether.

A crucial aspect of making data interoperable and reusable is using common definitions for the same things, such that data collected in one study is comparable to the data collected by others. For instance, what one researcher defines as "injury severity" is the same across the research community. However, this is extremely challenging in practice because there is generally not only a single way to define what we do in the laboratory. A solution can be common terminologies that serve as reference models and standards for defining data variables (also known as data elements). These provide information on how to name variables, and their definitions and, in some instances, define how the variables need to be collected or measured to fulfill those definitions.

The ODC uses different sets of common terminologies depending on the community and the projects supported.

ODC-SCI supported common terminology

ODC-SCI community data elements (CoDEs). The ODC-SCI has a set of data elements endorsed by the community board that serves as the minimal required variables necessary for making data public through the ODC-SCI.

ODC-TBI supported common terminology

There are currently several federally-supported efforts to develop and update common data elements for TBI. Prominent examples are:

• NINDS pre-clinical TBI common data elements (CDEs).

• PRECISE-TBI CDEs. The PRE Clinical Interagency reSearch resourcE-TBI (PRECISE-TBI) project uses the ODC-TBI as a data-sharing platform. PRECISE-TBI is developing a set of CDEs for pre-clinical TBI research. Those CDEs will be available for their use as common terminology for data shared through the ODC-TBI.

• TOP-NT TBI CDEs. The Translational Outcomes Project In Neurotrauma (TOP-NT) is a consortium for developing and validating clinically relevant biomarkers for traumatic brain injury (TBI).

The ODC-SCI community board has approved the definition of a set of community data elements or CoDEs and established them as a minimal set of variables required for any dataset to be published through the ODC-SCI with a DOI.

The list below includes the required variable name (in bold font) and the definition for each CoDE. You can download an ODC data dictionary template with the CoDEs

• Subject_ID: Unique identifiers for each subject in the dataset

• Species: Species of the subject

• Strain: Strain of the subject

• Animal_origin: Vendor or origin of the animal

• Age: Age of the subject at start of experiment. If age is available at different timepoints, age is provided at the corresponding time in a corresponding time/timepoint variable

• Weight: Weight of the subject at start of experiment. If weight is available at different timepoints, weight is provided at the corresponding time in a corresponding time/timepoint variable

• Sex: Sex of the subject

• Group: Name or identifier of the experimental group at which the subject was included if any

• Laboratory: Name of laboratory, usually the PI

• StudyLeader: Name of person responsible for overseeing project

• Exclusion_in_origin_study: Whether the subject was included in the study that originated the data. 'Total exclusion" if excluded from the entire study, otherwise, specify experiment or measures of which the animal was excluded if any. For example: animals that were run in behavior but maybe tissue is loss and excluded from histological analyses. Reasons for exclusion might be specify in the exclusion_reason variable.

• Exclusion_reason: Reason by which the subject was excluded from the study that originated the data as specified in the Exclusion_in_origin_study variable

• Cause_of_Death: Cause of death (e.g. perfusion/necropsy, died during surgery, euthanized for health reasons, etc)

• Injury_type: Type or model of injury used in the subject (e.g. contusion, complete transaction, partial section)

• Injury_device: Name of the device used for the injury

• Injury_level: Spinal cord level at which the injury was performed including segment (e.g. cervical; C) and number (e.g. C5)

• Injury_details: Other details referent to the injury that might be relevant to understand the severity and type of injury performed

Metadata refers to "data about the data" or information that may not constitute the data itself but provides an understanding of different aspects of the data. For instance, keywords associated with a dataset or the date on which a dataset was uploaded to a repository can be considered part of the metadata along the data. There are different types of metadata depending on their goal. A data dictionary, as described below, can be considered descriptive metadata that provides definitions and other elements for the content of a dataset. The citation of a dataset (similar to the citation of a paper) provides referencing metadata, and a data reuse license may provide legal metadata. Using standardized metadata increases the Findability and Interoperability of the data resources. The ODCs utilize the following standards.

• Data citation and DOI standards. A Digital Object Identifier accompanies datasets published through the ODCs (DOI) generated using the DataCite standards. This allows for cross-referencing and tracking published datasets and provides a standardized way to cite them.

• ORCID. The ODCs support the Open Researcher and Contributor ID or ORCID, a researcher global standard identifier. Users can link their ODCs accounts and profiles to ORCID and use it for identification.

• RRIDs. The ODCs support the use of Research Resource Identifiers or RRIDs, a standard identification number for the catalog of scientific tools and resources.

  • ODC-SCI: SCR_016673

  • ODC-TBI: SCR_021736

• Creative Commons License. All datasets published on the ODC are under the Creative Commons Attribution License (CC-BY 4.0).

• ODC Data dictionary. A data dictionary or codebook provides information about the dataset variables. It is one of the most important pieces of information to include with a dataset for anyone who wants to interpret and reuse the data.

• ODC narrative summary (abstract). ODC offers a metadata narrative and summary where data owners can provide information about the dataset. This information is unique to each dataset and is essential for archiving, interpretability, and reuse.

A data dictionary, also known as a codebook, provides information about the dataset variables. It is one of the most important pieces of information to include with a dataset for anyone who wants to interpret and reuse the data. Even if you are not planning on releasing your data, it is encouraged and of good data management practice to have data dictionaries for your datasets. You may know now what a variable name means in your spreadsheet (e.g., jtemp_6), but will your PI or colleagues know when you leave the lab? Will you know if you try to reuse the data two years from now? A data dictionary is a critical lab asset that ensures the data that have taken great effort and resources to acquire will not go to waste in the future due to poor documentation.

The ODC data dictionary

The data dictionary used by the ODC is a .csv file (a comma-separated value file). Learn more about .csv files here.

The file must contain the following column names in the first row:

• VariableName: * Variables (i.e. column headers) that appear in the dataset. You must include all of your dataset variables in the data dictionary.

• Title: * Title is the full name of the variable when the VariableName contains abbreviations or shorthand. If the VariableName is already a complete name, you can copy and paste the VariableName into the Title entry.

• Unit_of_Measure: Units for the variable (if applicable).

• Description: * Definitions and descriptions of the variable. The description should explain what the variable represents in enough detail such that a reader can understand the contents of the column in the dataset.

• DataType: Specify whether the variable specifically contains Numeric, Categorical, Ordinal, Date, or Free Text data.

• PermittedValues: If the variable is not numeric or free text, list all possible values here (e.g. "Male, Female" for the variable "Sex"). If the variable is numeric or free text, can leave this blank (use MinimumValue and MaximumValue columns).

• MinimumValue: If the variable is numeric, list the Minimum possible value. For example, if you expect a variable to be between 0-100, write 0 for MinimumValue. If there is no minimum value, leave this blank.

• MaximumValue: If the variable is numeric, list the Maximum possible value. For example, if you expect a variable to be between 0-100, write 100 for MaximumValue. If there is no maximum value, leave this blank.

• Comments: Additional notes such as exclusion criteria, reasons for special values, etc.

ODC offers a metadata form where you can provide information about the dataset in a standardized way. You can access the Metadata Editor for each dataset from a dataset view page. This information is unique to each dataset and helps with the interpretability and reuse of the data.

The Metadata Editor

Information you can edit in the form

• Title: Title that will be displayed on the dataset citation. Note that this will not change the title of the dataset visible within the ODC itself. Please include the species, sex, lesion type and area in your title.

• Abstract: The Abstract includes 3 fields: Study Purpose, Data Collected, Data Usage Notes.

  • Study Purpose: Short description of the overall study purpose that resulted in the dataset.

  • Data Collected: Summary of what kind of data is included in the dataset and how the data was collected. Please include important experiment parameters (such as experimental model and injury severity) and critical outcome measures.

  • Conclusions: Summary of conclusions (if any) made with the dataset at the time of dataset publication.

• Keywords: Keywords can be added to allow search engines to locate the DOI and dataset citation once the dataset is published. You can add your own keyword or start typing to see the ones ODC has already registered. You can reorder the keywords after they are added by dragging/dropping them in the list.

• Provenance / Originating Publication: This section allows for entering publications that are related to the dataset. You can either import the information automatically or introduce it manually.

  • Import from existing publication: enter the DOI or PMID. Note that we can only import information from some preprint articles. Check the “Import authors as contributors” checkbox to import the publication’s author list automatically as contributors of the dataset (you will have to assign the dataset author and contact author labels to the respective entries after importing). After import, choose to edit the publication entry and fill out the remaining fields: Citation Relevance.

  • Manual: If you want to enter information manually, you can create a blank entry by leaving the DOI/PMID field blank and hitting “Import/Add Publication.” Choose to edit the new entry and fill out the appropriate fields: DOI, PMID, Citation, Citation Relevance.

• Relevant links: This section allows for adding links to external resources that are relevant to the dataset. For example, if omics data associated to the dataset have been deposited in another repository, the link can be provided here. This section can also be used to link the current dataset to a published dataset in ODC.

• Notes: The Notes section should be used to provide important guidance for others on using your data. Relevant information may include technical issues during the experiment that may require data exclusion, specifics about the techniques that may prevent merging with other datasets, and so on. The goal is to provide information useful to data re-users and prevent data misuse.

• Funding and Acknowledgements: The Funding and Acknowledgements section requires 2 fields for each entry: Funding Agency, Funding Identifier and PI Initials. You can reorder the entries after they are added by dragging/dropping them in the list.

  • Funding Agency: Name of funding agency.

  • Funding Identifier and PI Initials: Respective funding ID (e.g. grant number) and PI Initials in parenthesis. For example: 4F0887Z (AC).

• Contributors / Authors: ODC considers any Author of a dataset a Contributor. Authors will have their names attached to the citation of a dataset if published. Contributors that are not authors are other persons that do not constitute an author but you want acknowledge their contribution to the dataset. Each Contributor/Author has 5 fields to fill out. Each entry is added as a contributor to the dataset by default; if you check the options to include an entry as an author or contact author, the appropriate label will be applied to the contributor/author entry. Each entry includes: First Name, Middle Initial, Last Name, ORCID, Affiliation, and Contact Email (if contact author). Data that was imported might be incomplete and will need to be edited.

  • First Name: Person’s first name.

  • Middle Initial: Person’s middle initial (if relevant).

  • Last Name: Person’s last name.

  • ORCID: Person’s associated ORCID.

  • Affiliation: Person’s associated affiliation at time of publishing the dataset.

  • Contact Email: Person's contact email (field appears only if they are a contact author)

Information that we automatically provided for you

• DOI: This is provided by ODC once you go through the DOI request process

• Dataset Citation: This field provides you with a look at how the citation of the dataset will look like if released to the public. It is constructed automatically from the provided list of authors and the tile. You can see the changes as you change those two pieces of information!

• Dataset Info: Dataset info is automatically populated from the other sections. The section includes: Contact Author information, Lab, ODC-SCI Accession Number, Number of Records in Dataset, Fields per Record, number of associated Files.

• License: The License is automatically assigned once and if the dataset is published. All datasets published on the ODC-SCI will be under the Creative Commons Attribution License (CC-BY 4.0).

List of content

C

Citation (of a dataset)

Similar to scientific publications, when a dataset is made public with a DOI, a citation including the author names, title, year of publication and ODC information is created. Dataset citation with a DOI is a common way to reference the use of public data.

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Common Data Elements (CDEs):

Data elements that have been recommended or required by NIH Institutes and Centers and other organizations for use in research and for other purposes. A data element is a basic unit of information (i.e., a variable) collected within a dataset that has a unique meaning and subcategories (data items) of distinct value. Examples of data elements include sex, race, and geographic location. ODC is implementing NIH’s CDEs where appropriate to aid in standardization and harmonization across different datasets.

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Community Board (CB)

Governance body comprising up to 12 members representing the different stakeholder groups served by the ODC who advise the Leadership Team in setting priorities and strategic directions in establishment of the ODC, the development of a sustainability model, to review and assist with decision making process, and to assist in the development of the curation process. The board will be engaged to keep a close link between the ODC and the SCI community . Membership in the CB shall be determined by the leadership team and the Executive Board.

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Community Moderators

Governance body comprising members of the ODC Leadership Team or their designees who approve requests to join the ODC as either a general or full member.

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Community Data Space

Semi-private data space accessible to members with Full Access of the ODC where members can share unpublished or pre-published data with other full members. All members agree to abide by the data sharing policies governing use of unpublished datasets shared in the Community Space.

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Creative Commons Attribution License (CC-BY v4.0)

Open source license allows anyone with access to use the content of the dataset but sets the legal obligation of giving appropriate credit to the authors of the data.

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CSV (or csv) format

CSV, “comma-separated values”, is a widely-used file format for spreadsheet-style datasets. In brief, a CSV is a delimited text file where each value (i.e. cell of the spreadsheet) is separated by a comma. CSV can be read by spreadsheet programs such as Excel, Google Sheets and Numbers.

ODC requires datasets and data dictionaries to be in CSV format when you are uploading to the ODC. You can easily convert excel (e.g. ".xls", ".xlsx") files to “.csv” files through spreadsheet programs like Excel by saving as a ".csv". For more details you can follow our tutorial

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D

Data dictionary

A data dictionary is a file containing information about each Variable (i.e. Column) in the dataset. The data dictionary provides critical information for the interpretability and reusability of the dataset. Importantly, the data dictionary helps other users understand what each of your Variables are and any important details you include. Data dictionaries are required when publishing data with a DOI. We encourage you to submit a data dictionary with your dataset, even if you do not plan to publish the data.

For more details and a downloadable Data Dictionary template, please see Data Dictionary Guidelines.

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Data Spaces

See ODC Data Spaces

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Data team

Governance body responsible for curation and Quality Control (QC) of all datasets that will be released to the public space.

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DOI or Digital Object Identifier.

A DOI is a globally unique, persistent identifier that uniquely identifies a digital object such as an article, data set or protocol. ODC uses DOIs to identify datasets. The DOI of a dataset is the standard way to reference datasets in publications.

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E

Editorial Board

Governance body overseeing the types of datasets published by the ODC, ensuring that they are consistent with the mission of the ODC and are accompanied by high quality and complete metadata. The Editorial Board is invited to serve by the Editor in Chief.

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Executive Board

Governance body comprising senior members of the ODC community, responsible for steering the ODC. Works with the Leadership Team to set high level policy and strategy for the governing and long term sustainability of the ODC. members of the Executive board shall initially be appointed at the discretion of the Leadership Team, however once established it will determine the method of selecting its own successors. Membership on this board will be over 1-2 terms of 3 years per term, with call meetings being held every other month.

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F

FAIR Data Principles

High level principles designed to make data Findable, Accessible, Interoperable and Reusable for both humans and machines (Wilkinson et al., 2016). The principles encompass 15 guidelines designed to improve the usability of digital data. More details can be found at the GO-FAIR initiative. ODC is adopting these principles, e.g., the use of persistent identifiers, FAIR vocabularies and community standards to ensure that ODC data is FAIR. You can read more about how ODC is adopting FAIR at Fouad et al., 2019 and Torres-Espin et al., 2021

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Full member of ODC (full access):

Membership level granted to general members (community access) when they successfully apply to start or join a lab in the ODC. Full access members can upload data, share with their lab members and other full members via the Commons. They may share individual datasets with general members on a peer to peer basis. Full access members may also publish datasets to the public space with a DOI.

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G

General member of ODC (community access)

Membership level granted when a user registers for an account and is approved by ODC Community Moderators. As a general member, other ODC members can directly share their datasets with you, including their unpublished datasets (feature under development). General membership requires that the individual is affiliated with a recognized institution subject to institutional oversight. General members agree to abide by the data use agreement regarding any datasets shared with them.

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L

Leadership Team

Governance body that oversees the establishment of the portal, outreach and integration of the community into decision making, and the development of the curation process. Current members comprise the PIs of current grants supporting the ODC, the head of the Community Board and other key personnel.

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M

Metadata

Accompanying information and documentation that provides details about your dataset. The data dictionary can be considered part of the metadata, as well as other information such as abstract, author list, associated methodology. A minimal metadata is required for publishing a dataset with DOI, and must be completed when requesting. You can read more about ODC metadata in our metadata tutorial.

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O

ODC Data Spaces

Virtual spaces in the portal determining levels of sharing permissions within ODC. ODC currently defines 4 different data spaces or levels of data sharing: 1) Personal (account owner); 2) Lab Space: Shared with lab members; 3) Community Space (aka the Commons): Shared with ODC Full Access Members; 4) Public: Shared with public with a DOI. Complete metadata is viewable for all public data via the ODC public portal but download of datasets requires an account at the registered user level (basic access).

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ODC Lab

Basic organizational unit on the ODC for data upload and includes Personal and Lab data spaces comprising a Principal Investigator (PI) and individuals invited to join their lab. The PI has sole discretion as to who is admitted to their lab. Requests for establishing an ODC Lab will undergo a manual review by the ODC Community Moderators or other authorized individuals to ensure that they meet the criteria for membership in the ODC:

• The user making the request is verified as a PI belonging to a recognized institution

• The Laboratory has agreed to operate according to the principles of the ODC, and abide by the ODC Terms and Conditions and Data Usage Agreement

Individuals joining a lab must be approved by the PI or their designee.

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ORCID ID

Open Researcher and Contributor ID is a stable, unique identifier assigned to individual researchers to connect researchers to their intellectual products and affiliations. The ORCID is a digital handle that unambiguously identifies an individual researcher and is maintained independently of a particular institution. Two researchers with the same name will have unique ORCIDs and unlike an email, the ORCID does not change when a researcher changes institutions. ODC requires that the PI and the contact person provide their ORCID IDs as part of the dataset metadata, and encourages provisioning of ORCIDs for all contributors so that researchers can get credit for datasets published. ODC now allows researchers to create an account in ODC using their ORCIDs which automatically associates any datasets authored in ODC with their ORCID ID. ODC also allows and encourages Researchers can register for a ORCID at orcid.org.

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P

Principal Investigator (PI)

Head of a laboratory or research group at a recognized institution. The PI is the individual within the laboratory or group that has the authority and rights to make s final decisions about what, when and with whom to share data using the ODC. Within ODC, the PI is considered the owner and responsible party for all data uploaded by members of the PI’s lab and must sign off on all datasets shared to the Commons or the Public spaces.

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Public dataset

Dataset that has been made public with a DOI through the ODC publication process. Registered users as well as ODC members have access to the public data space, where all public datasets can be found.

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R

Registered user of the ODC (basic access)

An individual who has signed up for an account and verified their email address. Registration requires no additional approval and allows the account holder to download public data only.

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Request DOI

Process by which members of the ODC can submit their datasets for quality control and generate a DOI. The final product of this process is a public dataset with a citation and a DOI. To know more about what you need to get your dataset ready for publication and request a DOI, check our tutorials.

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S

Scicrunch

Software platform on which ODC is developed. Scicrunch is developed and maintained by the FAIR Data Informatics Lab at UCSD for providing unified search across independently maintained databases and other data resources. The platform includes data ingestion, curation tools and vocabulary services.

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U

Unpublished dataset

A dataset that has not been published to the ODC Public Data Space with a DOI. Unpublished data may be private to an individual, shared within a lab or shared in the Community Data Space.

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V

Verified Lab

An ODC Lab that has undergone review by the ODC to ensure that they meet requirements for participating in the ODC:

• The lab head is a PI belonging to a recognized institution

• The Laboratory has agreed to operate according to the principles of the ODC, and abide by the ODC Terms and Conditions and Data Usage Agreement.

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The ODCs constantly develop new tools and functionalities based on user and community feedback and needs. In addition, it supports extensions through an Application Programming Interface (API) that developers can use to generate new tools integrating with the ODCs.

The Tool Sandbox provides a playground for users and developers to test the new tools and functions under development. The following is a list of tools that we encourage you to use.

Application Programming Interface (API)

A set of API tools for interacting with ODCs and development.

• Access: check the For developers page

The Quality Control and Data Exploration Tool (ODC QC-EDA app)

This web application, developed by members of the ODC data team, allows users to test for the data and data dictionary formatting checks required for publishing pre-clinical data with the ODCs and explore the dataset's content using Exploratory Data Analysis (EDA) principles.

• Access: https://atpspin.shinyapps.io/Data-quality-app/

• How to use

• Provide feedback. If you try the app, help us to improve it by providing feedback

The Quality Control and Data Exploration Tool (ODC QC-EDA app). This web application, developed by members of the ODC data team, allows users to test for the data and data dictionary formatting checks required for publishing data with the ODCs and explore the dataset's content using Exploratory Data Analysis (EDA) principles.

• Access: https://atpspin.shinyapps.io/Data-quality-app/

• Provide feedback. If you try the app, help us to improve it by providing feedback.

How to use the ODC QC-EDA app

Requirements

The first step is to access the app (link above) and have your dataset and data dictionary ready. Check the Getting your data ready tutorial to learn more about preparing your data.

1. Access the app and go to start

The app can be accessed using any web browser from anywhere.

2. Select the files to upload

To perform the quality checks on your data, you will need to upload a dataset and a data dictionary in .csv format.

4. Explore your data

The two tabs "Dataset" and "Data dictionary" provide interactive tables to visualize and explore your uploaded files.

5. Run the quality checks

Go to the "ODC data checks" tab and push the check button. It will create a table report with all the checks and some helpful information.

The quality check report table provides information about each check. You can learn more about the app's checks by going to the "About the ODC and this app" information tab.

• Pass. The dataset and data dictionary comply with ODC standards

• Warning. This might not be a problem for being compliance with ODC standards, but the user may want to double-check the data

• Fail. The dataset and data dictionary need revision on those elements

You can also click on any of the rows of the table and a definition of the check and tip on how to improve the data will pop-up.

Learn more about the checks

The ODCs are build as instances of the SciCrunch® Infrastructure (scicrunch.org), an open-source platform designed to create data sharing communities created by the FAIR Data Informatics (FDI) Laboratory at the University of California, San Diego.

If you are interested on developing applications that uses ODCs, check our minimal API (in development)

API

• API endpoints for SciCrunch

• API-python interface (in development)

• API-R interface (in development)

Sharing research is critical for scientific progress. Current approaches to data sharing in scientific communities primarily include direct sharing (i.e. via email) between individuals, upload of data as supplementary materials in a publication, or minimally-regulated sharing through personal websites or social media platforms. However, these options do not make the shared data FAIR: data is not readily findable, not broadly accessible, and almost never interoperable and reusable. The best way to publish data is using data repositories which offer capabilities specifically oriented to the goal of sharing data. The ODC is currently the only community-driven data repository for SCI and TBI research. This specific focus allows us to align the repository with FAIR data principles and the SCI and TBI community’s needs. Sharing data through the ODC unlocks latent potential in research data through FAIR principles, enabling the SCI and TBI communities to better tackle its many challenges.

We have several different authentication steps and account types in the ODC to help protect unpublished data. You gain more access and functions as you are approved to the next account type.

Basic Member

Requirement: Email verification upon signing up

Functions: As a registered user, you have Basic Access and can only explore and download published datasets.

Community Member

Requirement:

1. Be a Registered User

2. Approval from ODC oversight committee

Functions: As a community member, you have Community Access where the other ODC members can directly share their datasets with you, including their unpublished datasets (feature under development).

Full Member

Requirement:

1. Be a Community Member

2. Join/Create a lab - requires approval by the Principal Investigator (PI) of the lab on ODC OR requires approval from the ODC oversight committee if creating a new lab

Functions: As a full access member, you can upload, share, release, and publish your datasets. Additionally, you can explore and access unpublished datasets released into the Community data space.

There are several authentication steps and account types on the ODC to help protect unpublished data (see "What are the different account types on the ODC?" section). Additionally, the ODC is organized into different spaces that a dataset can belong. Each space offers different levels of user access, and the movement of data from one space to another is always at the discretion of the lab’s PI. The general flow of data and user access is as follows:

Personal

A dataset is initially uploaded into the Personal space. Though the dataset must be uploaded into a specific lab, the dataset will only be visible to the original uploader and the lab’s PI.

Lab Space

The PI can choose to Share a dataset with the rest of the Lab. Doing so moves a dataset from the Personal space to the Lab Space. Any dataset in the Lab space can be found and accessed by other Lab Members belonging to the same lab that the dataset was uploaded to.

Community

The PI can also choose to Release a dataset to the ODC Community space. Any dataset in the Community space can be found and accessed by other Community Members of the ODC, even if they do not belong to the same lab where the dataset was uploaded to. If you want to use a dataset in the Community space, you must adhere to the terms in the data use agreement.

Public

The PI can initiate a DOI submission process to Publish a dataset in the Public space. If the submission is approved, a DOI will be generated for the dataset, and the dataset will be accessible to anyone with a registered account (basic access) with the ODC. Datasets are published with an open source license - the Creative Commons Attribution License (CC-BY v4.0) - which allows anyone with basic access to use the contents of the dataset but sets the legal obligation of giving appropriate credit to the authors of the data. While only Registered Users of the ODC can access a published dataset, the metadata of the dataset will be findable and visible to anyone, even those without an account. Datasets in the Public space are fully considered accessible to the general public.

PIs control the full process

On the ODC, the PI has full control of their dataset up to the point of publication. The PI can move the dataset from Personal, Lab, and Community spaces as they wish. They can also publish a dataset from any data space at any time. Once a dataset is published and in the Public space, however, the published dataset can only be removed if the DOI is rescinded which will require an appeal to the ODC oversight committee.

FAIR stands for Findable, Accessible, Interoperable, and Reusable. FAIR establishes a framework for data sharing and defines a set of recommendations developed by FORCE11 (The Future of Research Communications and e-Scholarship) for successful data dissemination. FAIR data encompasses the principles of:

• Findable: data should be able to be found with enough explicit metadata to be searchable;

• Accessible: data should be accessible to others in some form;

• Interoperable: data should be able to integrate with other datasets of the same nature using structured formats and standard definitions such as common data elements;

• Reusable: data should include sufficient documentation and meet community standards in order to enable subsequent reuse of the data by others.

The ODC has been developed to follow the FAIR principles with tools and functionalities designed for the SCI and TBI community’s needs to ensure the success of data sharing with the ODC-SCI and ODC-TBI.

The ODC team is constituted of a multidisciplinary group of individuals highly committed to Open Science.

About the ODC-SCI

About the ODC-TBI

Welcome message

Dear Colleague, Welcome to the Open Data Commons for Spinal Cord Injury (ODC-SCI)! The ODC-SCI was established in 2017 as a dedicated data sharing portal and repository for the field of SCI. Through the ODC-SCI, you can share data with your colleagues in a protected space and publish data to the public with a DOI. The ODC-SCI complies with the FAIR data principles to ensure that SCI data is Findable, Accessible, Interoperable and Reusable. With the growth of this community we hope to expand the amount of data sharing and promote transparency and reproducibility for our common goal to find a cure for SCI, and provide a means for the SCI community to meet increasing funder and journal data sharing requirements. We cordially invite you to share this community with your colleagues and friends involved or interested in SCI research. Become a member of the ODC-SCI and start sharing your data now. For security reasons, ODC-SCI is only open to those affiliated with a recognized institution or organization with appropriate oversight protections in place. However, we welcome everyone to explore and use our public data. If you have questions, please contact us at info@odc-sci.org. To apply for membership, click here. Sincerely, The ODC-SCI community

The ODC-SCI team and committees

Leadership Team

• Karim Fouad, PhD (Co-chair) University of Alberta

• Adam R. Ferguson, PhD (Co-chair) University of California, San Francisco

• Jeffrey S. Grethe, PhD University of California, San Diego

• Maryann E. Martone, PhD University of California, San Diego

Community Board

• John C. Gensel, PhD (Chair) University of Kentucky

• Alina Garbuzov, PhD University of California, San Diego

• Antje Kroner-Milsch, MD, PhD Medical College of Wisconsin

• Audrey Kusiak, PhD Department of Veteran’s Affairs, Office of Research and Development, Rehabilitation Research and Development Service

• Candace Floyd, PhD University of Utah School of Medicine

• Harvey Sihota International Spinal Research Trust, ISRT

• Jacob Shreckengost, PhD Craig H. Neilsen Foundation

• John “Kip” Kramer, PhD University of British Columbia

• Linda Bambrick, PhD National Institute of Neurological Disorders and Stroke

• Linda Jones, PT, PhD Thomas Jefferson University

• Melisa Miller, PhD, D(ABMM) Department of Defense’s Congressionally Directed Medical Research Programs

• Murray Blackmore, PhD Marquette University

• Samantha Summers Mosaic Biosciences

• Sarah McCann, PhD Charité - Universitätsmedizin Berlin

• Steve Kirshblum, MD Kessler Institute for Rehabilitation

• Vanessa Noonan, PhD International Spinal Research Trust, ISRT

• Verena May, PhD Wings for Life, Austria

• Wolfram Tetzlaff, MD, PhD International Collaboration on Repair Discoveries, ICORD

• Jessica Nielsen, PhD

  University of Minnesota

• John L. Bixby, PhD

  University of Miami

• Vance Lemmon, PhD

  University of Miami

Editorial Board

• Karim Fouad, PhD (Editor in Chief) University of Alberta

• Antje Kroner-Milsch, MD, PhD Medical College of Wisconsin

• Chris West, PhD International Collaboration on Repair Discoveries, ICORD

• David S.K. Magnuson, PhD University of Louisville

• Jae K. Lee, PhD University of Miami

• Joshua Burda, PhD Cedars-Sinai Medical Center

• Veronica Tom, PhD

  Drexel University College of Medicine

• Philippa Warren, PhD

  Kings College London

• Catherine Jutzeler, PhD

  ETH Zurich

Data Team

• Abel Torres Espin, PhD (Team Lead) University of California, San Francisco

  University of Alberta

• Anastasia V. Keller, PhD University of California, San Francisco

• J. Russell Huie, PhD University of California, San Francisco

• Romana Vavrek University of Alberta

Welcome message

Dear Colleague,

Welcome to the Open Data Commons for Traumatic Brain Injury (ODC-TBI)! The ODC-TBI was established as a dedicated data sharing portal and repository for the field of TBI. Through the ODC-TBI, you can share data with your colleagues in a protected space and publish data to the public with a DOI. The ODC-TBI complies with the FAIR data principles and the new NIH Data Sharing Policy to ensure that TBI data is Findable, Accessible, Interoperable, and Reusable. The ODC-TBI currently supports data sharing in the TOP-NT project and is providing the data sharing platform for the new interagency PRECISE: PREClinical Interagency reSearch resourcE for TBI project, and is open to all researchers in TBI. With the growth of this community we hope to expand the amount of data sharing and promote transparency and reproducibility for our common goal to find treatments for TBI, and provide a means for the TBI community to meet increasing funder and journal data sharing requirements. We cordially invite you to share this community with your colleagues and friends involved or interested in TBI research. Become a member of the ODC-TBI and start sharing your data now. For security reasons, ODC-TBI is only open to those affiliated with a recognized institution or organization with appropriate oversight protections in place. However, we welcome everyone to explore and use our public data. If you have questions, please contact us at info@odc-tbi.org.

Sincerely, The ODC-TBI community

The ODC-TBI Team

Adam Ferguson, Ph. D., UCSF PI

Jeffrey Grethe, Ph. D. , UCSD Technical Lead

Maryann Martone, Ph. D. , UCSD, UCSF VA, PRECISE Informatics Core Lead

Anita Bandrowski, Ph. D., UCSD, PRECISE Interoperability Lead

Abel Torres-Espin, Ph. D., UCSF, University of Alberta, Data Science Lead

Russell Huie, Ph. D., UCSD, PRECISE Data Science Lead

Monique Surles-Zeigler, Ph. D., PRECISE Knowledge Base Lead

Mike Chiu, UCSD, lead developer

The ODC would not exist without the support of several institutions. We are thankful for all the support and funding that made this project possible.

ODC-SCI funding

ODC-TBI funding

WEB PORTAL AND DATA PIPELINE DEVELOPMENT FUNDING

• Craig H. Neilsen Foundation

• Wings for Life Foundation

COMMUNITY ENGAGEMENT FUNDING

• Rick Hansen Foundation

• International Spinal Research Trust

• NIH/NINDS

PRIOR NIH FUNDING FOR VISION-SCI SOURCE DATA

Visualized Syndromic Information and Outcomes for Neurotrauma-SCI (VISION-SCI) is a legacy database project that collected and harmonized data from several sources. Data from VISION-SCI was ported to ODC-SCI. Bellow are the NIH awards that funded the research producing the source data for VISION-SCI.

University of California San Francisco

• MECHANISMS OF SECONDARY INJURY AFTER SCI

• RECOVERY OF SACRAL SPINAL REFLEXES AFTER TRANSPLANTATION

• BIOINFORMATICS FOR TRANSLATIONAL SPINAL CORD INJURY RESEARCH

• METAPLASTICITY AND RECOVERY AFTER SPINAL CORD INJURY: CELLULAR MECHANISMS

• CERVICAL SPINAL INJURY AND DEMYELINATION IN AGED RATS

• ROLE OF AMPAR TRAFFICKING IN SPINAL CORD INJURY

• TRANSLATIONAL OUTCOME MEASURES OF CERVICAL SPINAL CORD INJURY

University of California San Diego

• PLASTICITY AND REGENERATION IN THE PRIMATE SPINAL CORD

• COMBINATORIAL APPROACHES TO SCI

Ohio State University

• PLASTICITY IN SACRAL SPINAL REFLEX CIRCUITS

• OLIGODENDROCYTE GENESIS AFTER SPINAL CORD INJURY

• OHIO STATE NEUROSCIENCE CENTER CORE

University of Louisville

• COBRE

• VASCULAR RESPONSES AS THERAPEUTIC TARGETS AFTER SCI

• MECHANISMS OF PLASTICITY AND REPAIR AFTER SCI

• ER STRESS AND OLIGODENDROCYTE SURVIVAL AFTER SPINAL CORD INJURY

• RE-TRAINING LOCOMOTOR CIRCUITRY IN A RAT MODEL OF SPINAL CORD INJURY

• FUNCTIONAL CONSEQUENCES OF SILENCING PROPRIOSPINAL PATHWAYS AFTER SCI IN THE ADULT RAT

University of Kentucky

• MITOCHONDRIAL-TARGETED THERAPEUTICS FOR TREATMENT OF SPINAL CORD INJURY

• UK SPINAL CORD & BRAIN INJURY RESEARCH CENTER CORE GRANT

• MITOCHONDRIAL UNCOUPLING AS A THERAPEUTIC TARGET IN TBI

New York University

• MULTICENTER SPINAL CORD INJURY STUDY

2022

• Chou A, Torres-Espín A, Huie JR, et al. Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research. Neurotrauma Rep. 2022;3(1):139-157. Published 2022 Apr 5. doi:10.1089/neur.2021.0061

2021

• Torres-Espín A, Almeida CA, Chou A, et al. Promoting FAIR Data Through Community-driven Agile Design: the Open Data Commons for Spinal Cord Injury (odc-sci.org) [published online ahead of print, 2021 Aug 4]. Neuroinformatics. 2021;10.1007/s12021-021-09533-8. doi:10.1007/s12021-021-09533-8

2020

• Fouad K, Bixby JL, Callahan A, et al. FAIR SCI Ahead: The Evolution of the Open Data Commons for Pre-Clinical Spinal Cord Injury Research. J Neurotrauma. 2020;37(6):831-838. doi:10.1089/neu.2019.6674

2017

• Callahan A, Anderson KD, Beattie MS, et al. Developing a data sharing community for spinal cord injury research. Exp Neurol. 2017;295:135-143. doi:10.1016/j.expneurol.2017.05.012

2014

• Nielson JL, Guandique CF, Liu AW, et al. Development of a database for translational spinal cord injury research. J Neurotrauma. 2014;31(21):1789-1799. doi:10.1089/neu.2014.3399

• Ferguson AR, Nielson JL, Cragin MH, Bandrowski AE, Martone ME. Big data from small data: data-sharing in the 'long tail' of neuroscience. Nat Neurosci. 2014;17(11):1442-1447. doi:10.1038/nn.3838

ODC-SCI blog

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Meet the ODC-SCI team as they present the latest portal updates at the following conferences:

APR 23-26: ISNR - Stevenson, WA

MAY 16-18: Wings for Life Scientific Meeting - Salzburg, Austria

JUN 24-28: National Neurotrauma Society Symposium - Austin, TX

By using the ODC, you are agreeing to the SciCrunch Infrastructure Terms of Use. In addition, there are terms and policies specific to each community.

ODC-SCI

ODC-SCI Terms and Conditions

ODC-SCI Data Use Agreement

ODC-SCI Non-institutional community membership policy

ODC-SCI Version Control policy

ODC-SCI Editorial board and Data team non-disclosure agreement

ODC-TBI

ODC-TBI Terms and Conditions

ODC-TBI Data Use Agreement