Overview
Ideally, your data description should embody the comprehensive principles outlined in the "Datasheets for Datasets" by Gebru, Timnit, et al. (2018)[^1]. We highly recommend referring to the original paper for an in-depth exploration of the seven essential components crucial for meticulous dataset documentation.
In the following section, we have replicated these pivotal questions. It's advisable to incorporate them into a readme.txt file alongside your datasets. In the case of derived data, it may suffice to reference a relevant source code file and provide a comprehensive list of variables along with their operational definitions.
You can download a formatted version (.docx) of this template using the button below. Alternatively, you can find a plain text version of it for copy & paste below.
Documentation Template
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D A T A S E T / D A T A B A S E D E S C R I P T I O N
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(template based on https://arxiv.org/abs/1803.09010)
* Name of the dataset/database:
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1. MOTIVATION
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1.1 For what purpose was the dataset created?
Was there a specific task in mind? Was there
a specific gap that needed to be filled?
Please provide a description.
1.2 Who created this dataset
(e.g., which team, research group) and on behalf of
which entity (e.g., company, institution, organization)?
1.3 Who funded the creation of the dataset?
If there is an associated grant, please provide
the name of the grantor and the grant name and number.
1.4 Any other comments?
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2. COMPOSITION
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2.1 What do the instances that comprise the dataset represent
(e.g., documents, photos, people, countries)?
Are there multiple types of instances (e.g., movies,
users, and ratings; people and interactions between them;
nodes and edges)?
Please provide a description.
2.2 How many instances are there in total
(of each type, if appropriate)?
2.3 Does the dataset contain all possible instances or is it a sample
(not necessarily random) of instances from a larger set?
If the dataset is a sample, then what is the larger set?
Is the sample representative of the larger set
(e.g., geographic coverage)? If so, please describe how this
representativeness was validated/verified.
If it is not representative of the larger set, please describe why not
(e.g., to cover a more diverse range of instances, because
instances were withheld or unavailable).
2.4 What data does each instance consist of?
"Raw" data (e.g., unprocessed text or images)
or features? In either case, please provide a description.
2.5 Is there a label or target associated with each instance?
If so, please provide a description.
2.6 Is any information missing from individual instances?
If so, please provide a description, explaining why this information is
missing (e.g., because it was unavailable). This does not include
intentionally removed information, but might include, e.g., redacted text.
2.7 Are relationships between individual instances made
explicit (e.g., users' movie ratings, social network links)?
If so, please describe how these relationships are made explicit.
2.8 Are there recommended data splits (e.g., training, development/validation,
testing)?
If so, please provide a description of these splits, explaining the
rationale behind them.
2.9 Are there any errors, sources of noise, or redundancies in the dataset?
If so, please provide a description.
2.10 Is the dataset self-contained, or does it link to or otherwise rely on
external resources (e.g., websites, tweets, other datasets)?
If it links to or relies on external resources,
a) are there guarantees that they will exist, and remain constant,
over time;
b) are there official archival versions of the complete dataset
(i.e., including the external resources as they existed at the
time the dataset was created);
c) are there any restrictions (e.g., licenses, fees) associated with
any of the external resources that might apply to a future user?
Please provide descriptions of all external resources and any restrictions
associated with them, as well as links or other access points, as
appropriate.
2.11 Does the dataset contain data that might be considered confidential
(e.g., data that is protected by legal privilege or by doctor-patient
confidentiality, data that includes the content of individuals'
non-public communications)?
If so, please provide a description.
2.12 Does the dataset contain data that, if viewed directly, might be offensive,
insulting, threatening, or might otherwise cause anxiety?
If so, please describe why.
2.13 Does the dataset relate to people?
If not, you may skip the remaining questions in this section.
2.14 Does the dataset identify any subpopulations (e.g., by age, gender)?
If so, please describe how these subpopulations are identified and
provide a description of their respective distributions within the dataset.
2.15 Is it possible to identify individuals (i.e., one or more natural persons),
either directly or indirectly (i.e., in combination with other data)
from the dataset?
If so, please describe how.
2.16 Does the dataset contain data that might be considered sensitive in
any way (e.g., data that reveals racial or ethnic origins, sexual
orientations, religious beliefs, political opinions or union memberships,
or locations; financial or health data; biometric or genetic data;
forms of government identification, such as social security numbers;
criminal history)?
If so, please provide a description.
2.17 Any other comments?
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3. COLLECTION PROCESS
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3.1 How was the data associated with each instance acquired?
Was the data directly observable (e.g., raw text, movie ratings),
reported by subjects (e.g., survey responses), or indirectly
inferred/derived from other data (e.g., part-of-speech tags, model-based
guesses for age or language)? If data was reported by subjects or indirectly
inferred/derived from other data, was the data validated/verified?
If so, please describe how.
3.2 What mechanisms or procedures were used to collect the data
(e.g., hardware apparatus or sensor, manual human curation,
software program, software API)?
How were these mechanisms or procedures validated?
3.3 If the dataset is a sample from a larger set, what was the sampling strategy
(e.g., deterministic, probabilistic with specific sampling probabilities)?
3.4 Who was involved in the data collection process (e.g., students,
crowd workers, contractors) and how were they compensated (e.g., how
much were crowd workers paid)?
3.5 Over what timeframe was the data collected? Does this timeframe
match the creation timeframe of the data associated with the
instances (e.g., recent crawl of old news articles)?
If not, please describe the timeframe in which the data associated with the
instances was created.
3.6 Were any ethical review processes conducted (e.g., by an institutional
review board)?
If so, please provide a description of these review processes, including
the outcomes, as well as a link or other access point to any
supporting documentation.
3.7 Does the dataset relate to people?
If not, you may skip the remainder of the questions in this section.
3.8 Did you collect the data from the individuals in question directly,
or obtain it via third parties or other sources (e.g., websites)?
3.9 Were the individuals in question notified about the data collection?
If so, please describe(or show with screenshots or other information) how
notice was provided, and provide a link or other access point to,
or otherwise reproduce, the exact language of the notification itself.
3.10 Did the individuals in question consent to the collection and use of their
data?
If so, please describe (or show with screenshots or other information)
how consent was requested and provided, and provide a link or other access
point to, or otherwise reproduce, the exact language to which the
individuals consented.
3.11 If consent was obtained, were the consenting individuals provided with a
mechanism to revoke their consent in the future or for certain uses?
If so, please provide a description, as well as a link or other access
point to the mechanism (if appropriate).
3.12 Has an analysis of the potential impact of the dataset and its use on data
subjects (e.g., a data protection impact analysis)been conducted?
If so, please provide a description of this analysis, including the
outcomes, as well as a link or other access point to any supporting
documentation.
3.13 Any other comments?
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4. PREPROCESSING/CLEANING/LABELING
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4.1 Was any preprocessing/cleaning/labeling of the data done (e.g.,
discretization or bucketing, tokenization, part-of-speech tagging,
SIFT feature extraction, removal of instances, processing of
missing values)? If so, please provide a description. If not, you may skip
the remainder of the questions in this section.
4.2 Was the "raw" data saved in addition to the
preprocessed/cleaned/labeled data (e.g., to support unanticipated
future uses)? If so, please provide a link or other access point to
the "raw" data.
4.3 Is the software used to preprocess/clean/label the instances available?
If so, please provide a link or other access point.
4.4 Any other comments?
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5. USES
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5.1 Has the dataset been used for any tasks already?
If so, please provide a description.
5.2 Is there a repository that links to any or all papers or systems
that use the dataset?
If so, please provide a link or other access point.
5.3 What (other) tasks could the dataset be used for?
5.4 Is there anything about the composition of the dataset or the way it was
collected and preprocessed/cleaned/labeled that might impact future uses?
For example, is there anything that a future user might need to know to
avoid uses that could result in unfair treatment of individuals or groups
(e.g., stereotyping, quality of service issues) or other undesirable harms
(e.g., financial harms, legal risks) If so, please provide a description.
Is there anything a future user could do to mitigate these undesirable
harms?
5.5 Are there tasks for which the dataset should not be used?
If so, please provide a description.
5.6 Any other comments?
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6. DISTRIBUTION
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6.1 Will the dataset be distributed to third parties outside of the entity
(e.g., company, institution, organization) on behalf of which the
dataset was created?
If so, please provide a description.
6.2 How will the dataset will be distributed(e.g.,tarball on website, API,
GitHub)? Does the dataset have a digital object identifier (DOI)?
6.3 When will the dataset be distributed?
6.4 Will the dataset be distributed under a copyright or other intellectual
property(IP) license, and/or under applicable terms of use (ToU)?
If so, please describe this license and/or ToU, and provide a link or other
access point to, or otherwise reproduce, any relevant licensing terms or
ToU (Terms of Use), as well as any fees associated with these restrictions.
6.5 Have any third parties imposed IP-based or other restrictions on the
data associated with the instances?
If so, please describe these restrictions, and provide a link or other
access point to, or otherwise reproduce, any relevant licensing terms,
as well as any fees associated with these restrictions.
6.6 Do any export controls or other regulatory restrictions apply to the
dataset or to individual instances?
If so, please describe these restrictions, and provide a link or other
access point to, or otherwise reproduce, any supporting documentation.
6.7 Any other comments?
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7. MAINTENANCE
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7.1 Who is supporting/hosting/maintaining the dataset?
7.2 How can the owner/curator/manager of the dataset be contacted
(e.g., email address)?
7.3 Is there an erratum?
If so, please provide a link or other access point.
7.4 Will the dataset be updated (e.g., to correct labeling errors, add
new instances, delete instances)?
If so, please describe how often, by whom, and how updates will
be communicated to users (e.g., mailing list, GitHub)?
7.5 If the dataset relates to people, are there applicable limits on the
retention of the data associated with the instances
(e.g., were individuals in question told that their data would be retained
for a fixed period of time and then deleted)?
If so, please describe these limits and explain how they will be enforced.
7.6 Will older versions of the dataset continue to be
supported/hosted/maintained?
If so, please describe how. If not, please describe how its obsolescence
will be communicated to users.
7.7 If others want to extend/augment/build on/contribute to the dataset,
is there a mechanism for them to do so?
If so, please provide a description. Will these contributions be
validated/verified?
If so, please describe how. If not, why not? Is there a process for
communicating/distributing these contributions to other users?
If so, please provide a description.
7.8 Any other comments?
A Shorter Version
That's a lot of documentation. So - if you don't have time, go with the bigger picture and answer the main questions only.
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D A T A S E T D E S C R I P T I O N
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Name of the dataset:
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1. Motivation of data collection (why was the data collected?)
[...]
2. Composition of dataset (what's in the data?)
[...]
3. Collection process (how was the data collected?)
[...]
4. Preprocessing/cleaning/labeling (how was the data cleaned, if at all?)
[...]
5. Uses (how is the dataset intended to be used?)
[...]
6. Distribution (how will the dataset be made available to others?)
[...]
7. Maintenance (will the dataset be maintained? How? by whom?)
[...]
[^1]: Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2018). Datasheets for datasets. arXiv preprint arXiv:1803.09010.