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Paper Citation Record · LEDGER

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation

As of 18 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2503.13463.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2503.13463 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:37:19.094296Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

  • verified exact2
  • verified fuzzy3
  • unresolved17
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation da26ed95-af26-4401-b606-b3fe98759130 · outbound

This paper cites In: 2021 IEEE International Conference on Smart Data Services (SMDS).

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation In: 2021 IEEE International Conference on Smart Data Services (SMDS)

Reference 1

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unresolved
no resolver link, observed 2026-08-08T15:37:19.015171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 444f4c2a-d316-4ae4-bb66-6c73a280b75b · outbound

This paper cites FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity

Reference 2

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unresolved
no resolver link, observed 2026-08-08T15:37:19.019283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T15:37:19.019283Z digest=sha256:32f6251d9bdc47946f5a9e76f6fb586f2d2726eb9500a1c674bf79a840298e6d

Observation 3305dd30-c3d1-4c1d-87a7-15a1b8ea5794 · outbound

This paper cites Transactions of the Association for Computational Linguistics 6, 587–604 (2018).

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Transactions of the Association for Computational Linguistics 6, 587–604 (2018)

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T15:37:19.780102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation cdb6a5a4-0d1d-4f52-a147-a0eef932ee20 · outbound

This paper cites an unresolved cited work.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-08T15:37:19.771791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation de3583ed-9bdb-4fab-9624-77bb09f5619b · outbound

This paper cites Proceedings of the ACM on Human-Computer Interaction 5(CSCW2), 438:1–438:27 (Oct 2021).

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Proceedings of the ACM on Human-Computer Interaction 5(CSCW2), 438:1–438:27 (Oct 2021)

Reference 5

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unresolved
no resolver link, observed 2026-08-08T15:37:19.032297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T15:37:19.032297Z digest=sha256:ff676a667113ba85dc3a882f2981e4db968be8e342bb2d212b21cf99cac26a50

Observation 8bcb1f8d-8b63-4910-a8ed-fe926f6c194c · outbound

This paper cites A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication

Reference 6

Resolution
metadata mismatch
local_arxiv, observed 2026-08-08T15:37:19.543688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T15:37:19.035370Z digest=sha256:3be438700d2a085a2bdf8331ced01fc05860ace935bf7d0ba3b6170df10ab3f3

Observation b2d32d24-fafa-4aed-9cc0-b4a2871ffbdf · outbound

This paper cites an unresolved cited work.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-08T15:37:19.763529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T15:37:19.039357Z digest=sha256:1f76907083e1773ed2a06ce6af4cf7a1032caf93eb83b6f93f34b7451becca52

Observation f381163f-8d83-484c-97fe-bf4a56ba9c2e · outbound

This paper cites Datasheets for Datasets.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Datasheets for Datasets

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-08T15:37:19.042281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T15:37:19.042281Z digest=sha256:b699f9edafcdcf662e0b2627195e719f20b7b55f5ee64fca2f76f0e598d0dd08

Observation 576009b4-e1f9-4c18-ac16-8d3ed74d7cb2 · outbound

This paper cites The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-08T15:37:19.048110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T15:37:19.048110Z digest=sha256:4be787b45fcdf0521ab827eea886f8f7f3c58c741530ec85deb25c9505cf87d6

Observation 2bf06ab7-e34c-4c4f-a582-c0b17ae978aa · outbound

This paper cites In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T15:37:19.753499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T15:37:19.051142Z digest=sha256:ecb071dc02cc019607a4c38ff2795a8df1d6c81303cf5bffd04591ae3d584852

Observation 771eec66-099a-4b86-bc8a-fcec71430dfa · outbound

This paper cites Proceedings of the 2020 Conference on Fairness, Ac- countability, and Transparency pp.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Proceedings of the 2020 Conference on Fairness, Ac- countability, and Transparency pp

Reference 12

Resolution
malformed identifier
no resolver link, observed 2026-08-08T15:37:19.056589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T15:37:19.056589Z digest=sha256:7f663d9abbff793c9aa0efe6354897d0f326d6b40067f0646f46e91b039fc073

Observation a8d5b7b8-ff29-408c-b7c8-10ce0d4fb827 · outbound

This paper cites Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-08T15:37:19.059004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 4fc4d55a-1640-4161-9273-90be90e05c1a · outbound

This paper cites Digital Policy, Regulation and Governance 23(5), 475–488 (Jan 2021).

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Digital Policy, Regulation and Governance 23(5), 475–488 (Jan 2021)

Reference 14

Resolution
verified exact
doi, observed 2026-08-08T15:37:19.153562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 846090d5-c28c-427e-a9e2-5849ec462994 · outbound

This paper cites Proceedings of the Conference on Fairness, Accountability, and Transparency pp.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Proceedings of the Conference on Fairness, Accountability, and Transparency pp

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-08T15:37:19.065072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T15:37:19.065072Z digest=sha256:541da025e039e181745b36774c133a2ba42d1dce3689f3aff2e9cba604dfe98f

Observation da2f533f-a151-4a8d-a0e3-554fe5a542c9 · outbound

This paper cites Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-08T15:37:19.067723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T15:37:19.067723Z digest=sha256:35cd9b4d76e898121e647eacf002eeff0612dbe965eb386dc869c82cfa03414d

Observation 0f5a4603-333c-4861-89cb-4260c95ad7fd · outbound

This paper cites Journal of Statistical Software 90, 1–38 (Jul 2019).

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Journal of Statistical Software 90, 1–38 (Jul 2019)

Reference 17

Resolution
verified exact
doi, observed 2026-08-08T15:37:19.142321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T15:37:19.070876Z digest=sha256:634c42a05b350da0d69b7dd24e226c4a3ffe09fb52b28e4143589adbc8f42b2a

Observation 877b8091-b56f-4846-b035-9c3eaf01f9b6 · outbound

This paper cites A Methodology for Creating AI FactSheets.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation A Methodology for Creating AI FactSheets

Reference 18

Resolution
metadata mismatch
local_arxiv, observed 2026-08-08T15:37:19.349723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T15:37:19.074139Z digest=sha256:760e294e07e9f80789ade4f47994d16bd0cc73f449e8e6bf253725cb7aab6923

Observation 9ba32b69-65a9-47d2-a340-4b3001b6bf88 · outbound

This paper cites Everyone wants to do the model work, not the data work.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Everyone wants to do the model work, not the data work

Reference 19

Resolution
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no resolver link, observed 2026-08-08T15:37:19.077206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T15:37:19.077206Z digest=sha256:d019112890324567113e4fe5b00fb30b8759989047cde47f81b3325ef2782d9a

Observation 98e99221-d636-4ec1-b1a9-4c883ed19973 · outbound

This paper cites Proceedings of the ACM on Human-Computer Interaction 5(CSCW2), 1–37 (Oct 2021).

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Proceedings of the ACM on Human-Computer Interaction 5(CSCW2), 1–37 (Oct 2021)

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T15:37:19.741994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation f4db3860-b61c-4d57-b254-f8f0f90f28ea · outbound

This paper cites Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-08T15:37:19.082506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T15:37:19.082506Z digest=sha256:77f6203fd4e3689cc44ebe7f9240a5ae45aa372ccb8ae042083dcc034ba9b21e

Observation 043a6c78-15ed-4062-8764-0df04e9c7e70 · outbound

This paper cites In: Proceedings of the 28th ACM International Conference on Information and Knowledge Manage- ment.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation In: Proceedings of the 28th ACM International Conference on Information and Knowledge Manage- ment

Reference 22

Resolution
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no resolver link, observed 2026-08-08T15:37:19.086135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T15:37:19.086135Z digest=sha256:99968351cad574ea5ae33b1a7ffdd3d10aff9999090413780e68fe9706d74150

Observation 65912f49-2f89-4762-ad8a-ee4fa11d5387 · outbound

This paper cites Media, Culture & Society p.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Media, Culture & Society p

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-08T15:37:19.088831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T15:37:19.088831Z digest=sha256:9bf1357cbb45f3dfc99263cb363fe4ad91686ac04c62d29f776c5bee901fc535

Observation 5bcec523-b667-457a-870c-520f383ba898 · outbound

This paper cites Proceedings of the 2018 International Conference on Management of Data pp.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Proceedings of the 2018 International Conference on Management of Data pp

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-08T15:37:19.091520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T15:37:19.091520Z digest=sha256:55b244333f1c46f350b0e70f6e9a99c4c34d93be860c839a8f9a4dc33ba9f00d

Observation 6f7e6946-d8b5-4d46-851c-6e13fb6bbceb · outbound

This paper cites Fairness in Ranking: A Survey.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation Fairness in Ranking: A Survey

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-08T15:37:19.094296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T15:37:19.094296Z digest=sha256:9f6a5f72587e1ca57b1b187a6129766bd183dab885b48261f214bb3235beb8bd

Observation 3070b6c1-278c-4adc-8465-640bf32df06e · outbound

This paper cites https://doi.org/10.1145/3442188.3445918.

Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation https://doi.org/10.1145/3442188.3445918

Reference 575

Resolution
unresolved
no resolver link, observed 2026-08-08T15:37:19.053970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T15:37:19.053970Z digest=sha256:03de8f0f25d966ddb7398bddbb1017d30ced0b9020f0981796b72a21d746e17d

Pith citing papers

No inbound Pith citation observations are available.