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Paper Citation Record · LEDGER

Automated Data Readiness for Scientific AI

As of 21 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 1 inbound Pith citation observation for arXiv:2607.02771.

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

pith.paper-citation-record.v1
2607.02771 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T07:09:04.155924Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T07:36:35.530254Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-02T12:16:14.744948Z

Reference resolution

59 of 59 outbound references displayed

  • verified exact14
  • verified fuzzy0
  • unresolved39
  • parse uncertain0
  • malformed identifier5
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cb9fa9d8-2642-4f17-9b6f-f85d366f72fa · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Automated Data Readiness for Scientific AI On the Opportunities and Risks of Foundation Models

Reference 1

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Observation 6f72e071-36a5-465b-8d10-05cd5e7836de · outbound

This paper cites The FAIR guiding principles for scientific data management and stewardship,.

Automated Data Readiness for Scientific AI The FAIR guiding principles for scientific data management and stewardship,

Reference 2

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Observation ca6777e6-5eb7-4bdf-ab9d-f9ce66cac451 · outbound

This paper cites Available: https://doi.org/10.1038/sdata.2016.18.

Automated Data Readiness for Scientific AI Available: https://doi.org/10.1038/sdata.2016.18

Reference 3

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Observation be26fee2-2629-4d64-a5ed-b2cec09d51f4 · outbound

This paper cites AI-readiness for biomedical data: Bridge2AI recommendations,.

Automated Data Readiness for Scientific AI AI-readiness for biomedical data: Bridge2AI recommendations,

Reference 4

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Observation a5b2fd51-5f81-44d8-97d2-4944c083e79b · outbound

This paper cites SetGo: Metadata readiness for scientific AI datasets,.

Automated Data Readiness for Scientific AI SetGo: Metadata readiness for scientific AI datasets,

Reference 5

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Observation 194a2995-faba-4672-ae1b-3cd47a9cbe36 · outbound

This paper cites Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials,.

Automated Data Readiness for Scientific AI Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials,

Reference 6

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Observation 6945522b-1519-4453-84df-556d45813d3f · outbound

This paper cites Data Readiness Levels.

Automated Data Readiness for Scientific AI Data Readiness Levels

Reference 7

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Observation 816aed61-e9c9-4767-9b86-d1664bd8b75f · outbound

This paper cites Washington, DC: National Academies Press, 2026, prepublication copy—uncorrected proofs.

Automated Data Readiness for Scientific AI Washington, DC: National Academies Press, 2026, prepublication copy—uncorrected proofs

Reference 8

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Observation 405d13c2-7265-4a3f-a575-23ecb4ec1f39 · outbound

This paper cites Nvidia physicsnemo: An open-source framework for physics-based deep learning in science and engineering,.

Automated Data Readiness for Scientific AI Nvidia physicsnemo: An open-source framework for physics-based deep learning in science and engineering,

Reference 9

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Observation 7746ea83-7921-4647-903c-5fffe3af745f · outbound

This paper cites AIFS – ECMWF’s data-driven forecasting system,.

Automated Data Readiness for Scientific AI AIFS – ECMWF’s data-driven forecasting system,

Reference 10

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Observation 7c9f6c62-e48a-4b59-ad69-abf187a8a923 · outbound

This paper cites AIFS -- ECMWF's data-driven forecasting system.

Automated Data Readiness for Scientific AI AIFS -- ECMWF's data-driven forecasting system

Reference 11

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Observation 7b82132d-9755-4399-bc6d-f3b196d440c6 · outbound

This paper cites Nature Language Model: Deciphering the Language of Nature for Scientific Discovery.

Automated Data Readiness for Scientific AI Nature Language Model: Deciphering the Language of Nature for Scientific Discovery

Reference 12

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Observation 6bfe6deb-1fca-4501-a009-1e081ac1d4da · outbound

This paper cites On scientific foundation models: Rigorous definitions, key applications, and a comprehensive survey,.

Automated Data Readiness for Scientific AI On scientific foundation models: Rigorous definitions, key applications, and a comprehensive survey,

Reference 13

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Observation 161a633e-9819-4b59-b0bf-b27a26b5b3e4 · outbound

This paper cites Towards a foundation model for partial differential equations across physics domains,.

Automated Data Readiness for Scientific AI Towards a foundation model for partial differential equations across physics domains,

Reference 14

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Observation d70117ab-d0db-401d-8884-d4713356b5c8 · outbound

This paper cites 2023 DOE Public Access Plan,.

Automated Data Readiness for Scientific AI 2023 DOE Public Access Plan,

Reference 15

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Observation c241f5bf-de41-458a-a802-da65fa4b7021 · outbound

This paper cites Applying the FAIR principles to computational workflows,.

Automated Data Readiness for Scientific AI Applying the FAIR principles to computational workflows,

Reference 16

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Observation 136398eb-52ab-458e-88ac-cc4b2a8e4e3e · outbound

This paper cites ClimaX: A foundation model for weather and climate.

Automated Data Readiness for Scientific AI ClimaX: A foundation model for weather and climate

Reference 17

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Observation 411e336c-4625-4567-99c4-c18b2dc3e8ef · outbound

This paper cites Openfold: Retraining alphafold2 yields new insights into its learning mechanisms and capacity for generalization,.

Automated Data Readiness for Scientific AI Openfold: Retraining alphafold2 yields new insights into its learning mechanisms and capacity for generalization,

Reference 18

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Observation 390b989f-3c61-4fb0-9255-0e32270b212c · outbound

This paper cites Hilfer fractional advection-diffusion equations with power-law initial condition; a Numerical study using variational iteration method.

Automated Data Readiness for Scientific AI Hilfer fractional advection-diffusion equations with power-law initial condition; a Numerical study using variational iteration method

Reference 19

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Observation e22ac49d-e1b7-49ef-a965-c7fb394da8b1 · outbound

This paper cites Data readiness for AI: A 360-degree survey,.

Automated Data Readiness for Scientific AI Data readiness for AI: A 360-degree survey,

Reference 20

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Observation 26c0b0ad-987a-48a3-aeda-08fe3887f33f · outbound

This paper cites Available: https://doi.org/10.1145/3722214.

Automated Data Readiness for Scientific AI Available: https://doi.org/10.1145/3722214

Reference 21

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Observation bbfa2b6c-7e41-4176-92cc-8a499734dcbb · outbound

This paper cites Lustre unveiled: Evolution, design, advancements, and current trends,.

Automated Data Readiness for Scientific AI Lustre unveiled: Evolution, design, advancements, and current trends,

Reference 22

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Observation 30d4e4bf-3c9d-4638-aff7-520cad6aa1af · outbound

This paper cites Available: https://doi.org/10.1145/3736583.

Automated Data Readiness for Scientific AI Available: https://doi.org/10.1145/3736583

Reference 23

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Observation 71c49a6b-170a-4751-93aa-2fa5647ec5a3 · outbound

This paper cites An overview of the HDF5 technology suite and its applications,.

Automated Data Readiness for Scientific AI An overview of the HDF5 technology suite and its applications,

Reference 24

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Observation e1c51083-ea28-4ea9-96a3-6a3c0c1d8ab2 · outbound

This paper cites NetCDF: An interface for scientific data access,.

Automated Data Readiness for Scientific AI NetCDF: An interface for scientific data access,

Reference 25

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Observation 069c8158-740b-495c-9d87-e9a991963ed1 · outbound

This paper cites ADIOS 2: The adaptable input output system. a framework for high-performance data management,.

Automated Data Readiness for Scientific AI ADIOS 2: The adaptable input output system. a framework for high-performance data management,

Reference 26

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Observation 1c153908-29f5-4072-bf38-14f3489dc3e5 · outbound

This paper cites Zarr: A cloud-optimized storage for interactive access of large arrays,.

Automated Data Readiness for Scientific AI Zarr: A cloud-optimized storage for interactive access of large arrays,

Reference 27

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Observation 37433122-39c8-4018-a277-dc256a070cec · outbound

This paper cites LMDB: Lightning memory-mapped database,.

Automated Data Readiness for Scientific AI LMDB: Lightning memory-mapped database,

Reference 28

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Observation d953f1fb-81a4-4503-b72c-14c11c3d2bca · outbound

This paper cites Great expectations,.

Automated Data Readiness for Scientific AI Great expectations,

Reference 29

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Observation 7b8e3fc2-e703-4863-9510-9b3262fad9cb · outbound

This paper cites Available: https://doi.org/10.5281/zenodo.5683574.

Automated Data Readiness for Scientific AI Available: https://doi.org/10.5281/zenodo.5683574

Reference 30

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Observation e7e20924-b0e6-415e-b27e-3f7889816ed5 · outbound

This paper cites AI data readiness inspector (aidrin) for quantitative assessment of data readiness for AI,.

Automated Data Readiness for Scientific AI AI data readiness inspector (aidrin) for quantitative assessment of data readiness for AI,

Reference 31

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Observation 9b9da2f0-567d-4556-b722-ca48c4849001 · outbound

This paper cites A terminology for scientific workflow systems,.

Automated Data Readiness for Scientific AI A terminology for scientific workflow systems,

Reference 32

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Observation b0a4be61-3d44-48de-baa8-528c1aedbfef · outbound

This paper cites Nextflow enables reproducible computational workflows,.

Automated Data Readiness for Scientific AI Nextflow enables reproducible computational workflows,

Reference 33

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Observation 5c33c194-bf94-4789-90de-f84b7a347ca5 · outbound

This paper cites Sustainable data analysis with snakemake,.

Automated Data Readiness for Scientific AI Sustainable data analysis with snakemake,

Reference 34

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Observation fd15e8a9-e075-4ff4-a434-a3097b1c45bd · outbound

This paper cites Dask: Parallel computation with blocked algorithms and task scheduling,.

Automated Data Readiness for Scientific AI Dask: Parallel computation with blocked algorithms and task scheduling,

Reference 35

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Observation 44bb3bf0-f0cf-40a7-922e-84cafb0309dd · outbound

This paper cites Ray: A distributed framework for emerging AI applications,.

Automated Data Readiness for Scientific AI Ray: A distributed framework for emerging AI applications,

Reference 36

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Observation 30c7bd0a-106e-422b-8883-d8645e8b818c · outbound

This paper cites Apache Spark: A unified engine for big data processing,.

Automated Data Readiness for Scientific AI Apache Spark: A unified engine for big data processing,

Reference 37

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Observation a3ce5944-c896-4e73-bea3-7efd9619c44f · outbound

This paper cites Parsl: Pervasive parallel programming in Python,.

Automated Data Readiness for Scientific AI Parsl: Pervasive parallel programming in Python,

Reference 38

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Observation d4353910-2158-40e9-b2f2-f2e18573f5c9 · outbound

This paper cites Cloud- native repositories for big scientific data,.

Automated Data Readiness for Scientific AI Cloud- native repositories for big scientific data,

Reference 39

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Observation cf467352-5d75-481b-a7ef-0a5aaddee295 · outbound

This paper cites Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN,.

Automated Data Readiness for Scientific AI Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN,

Reference 40

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Observation 6c80c7a2-b224-4363-a8b3-e9a51ba92085 · outbound

This paper cites Accelerating the machine learning lifecycle with MLflow,.

Automated Data Readiness for Scientific AI Accelerating the machine learning lifecycle with MLflow,

Reference 41

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Observation a1727022-451d-4e73-aa5d-ec002c841b13 · outbound

This paper cites Towards lightweight data integration using multi- workflow provenance and data observability,.

Automated Data Readiness for Scientific AI Towards lightweight data integration using multi- workflow provenance and data observability,

Reference 42

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Observation 6536aca1-411b-4453-9584-f843731df9e3 · outbound

This paper cites SWE-agent: Agent-computer interfaces enable automated software engineering,.

Automated Data Readiness for Scientific AI SWE-agent: Agent-computer interfaces enable automated software engineering,

Reference 43

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Observation f49ddea6-3b13-4279-a2de-05bd7d736b20 · outbound

This paper cites Do large language models speak scientific workflows?.

Automated Data Readiness for Scientific AI Do large language models speak scientific workflows?

Reference 44

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 3b8b9163-24f1-4e47-983e-660a9e9bd25c · outbound

This paper cites Towards generating contracts for scientific data analysis workflows,.

Automated Data Readiness for Scientific AI Towards generating contracts for scientific data analysis workflows,

Reference 45

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Observation 0c93170e-a0ad-42e1-bfb2-02dac2205121 · outbound

This paper cites LLM agents for interactive workflow provenance: Reference architecture and evaluation methodology,.

Automated Data Readiness for Scientific AI LLM agents for interactive workflow provenance: Reference architecture and evaluation methodology,

Reference 46

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation a2749c5f-33b4-4b0c-aee9-0aacd01324db · outbound

This paper cites Leakage in data mining: Formulation, detection, and avoidance,.

Automated Data Readiness for Scientific AI Leakage in data mining: Formulation, detection, and avoidance,

Reference 47

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Observation fddace09-6805-4da9-ae3f-e124f61a4ebb · outbound

This paper cites Enabling low-overhead ht-hpc workflows at extreme scale using gnu parallel,.

Automated Data Readiness for Scientific AI Enabling low-overhead ht-hpc workflows at extreme scale using gnu parallel,

Reference 48

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Observation 7529d298-ffbd-4e22-ad9a-59adfccb25a2 · outbound

This paper cites Whole- volume integrated gyrokinetic simulation of plasma turbulence in realistic diverted-tokamak geometry,.

Automated Data Readiness for Scientific AI Whole- volume integrated gyrokinetic simulation of plasma turbulence in realistic diverted-tokamak geometry,

Reference 49

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation f7b5d707-97eb-42a1-9737-b4266e7b875b · outbound

This paper cites Highly accurate protein structure prediction with AlphaFold,.

Automated Data Readiness for Scientific AI Highly accurate protein structure prediction with AlphaFold,

Reference 50

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Observation 1ad639a5-9fdd-4751-b772-38f7327046c4 · outbound

This paper cites The open catalyst 2020 (oc20) dataset and community challenges,.

Automated Data Readiness for Scientific AI The open catalyst 2020 (oc20) dataset and community challenges,

Reference 51

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Observation 4014e611-ad92-4bfa-b82e-27d855df5dd0 · outbound

This paper cites The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysts,.

Automated Data Readiness for Scientific AI The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysts,

Reference 52

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Observation 93b67020-8d4b-472d-9a20-615d8d9447d0 · outbound

This paper cites A universal graph deep learning interatomic potential for the elements,.

Automated Data Readiness for Scientific AI A universal graph deep learning interatomic potential for the elements,

Reference 53

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Observation c95ab225-896d-48ee-9656-693b7a7d07ee · outbound

This paper cites CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling.

Automated Data Readiness for Scientific AI CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling

Reference 54

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation fc4d8485-8089-4987-a9a6-23e9d29c6812 · outbound

This paper cites The ani-1ccx and ani-1x data sets, coupled-cluster and density functional theory properties for molecules,.

Automated Data Readiness for Scientific AI The ani-1ccx and ani-1x data sets, coupled-cluster and density functional theory properties for molecules,

Reference 55

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source=pdf_text observed=2026-07-12T07:09:04.155924Z digest=sha256:b3b354eb8fd22541a9f866a5c118c0226514c7d5a23889dcce7ab32a1215ff69

Observation e6adac1e-57f4-4c43-aad5-ee8f969c962e · outbound

This paper cites Qm7-x, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules,.

Automated Data Readiness for Scientific AI Qm7-x, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules,

Reference 56

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Observation bacfc776-f4d0-412f-8005-2257dc9d8f81 · outbound

This paper cites A fast low-to-high confinement mode bifurcation dynamics in the boundary- plasma gyrokinetic code xgc1,.

Automated Data Readiness for Scientific AI A fast low-to-high confinement mode bifurcation dynamics in the boundary- plasma gyrokinetic code xgc1,

Reference 57

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source=pdf_text observed=2026-07-12T07:09:04.155924Z digest=sha256:289f5c95d9c6bbf071cc17b68248e233d484cc710b43cd81d9038e600881b87e

Observation e3d386bf-b63c-4bbc-9e6d-518aa6b1f70a · outbound

This paper cites Available: https://doi.org/10.1063/1.5020792.

Automated Data Readiness for Scientific AI Available: https://doi.org/10.1063/1.5020792

Reference 58

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-12T07:09:04.155924Z digest=sha256:c19d016c5d5a60211b69d56e167c1f1be9eaaceb485359fb4d0b3772e184c7b9

Observation 31b569ad-a26d-429f-a121-a9b094fdcc7e · outbound

This paper cites MATEY: multiscale adaptive foundation models for spatiotemporal physical systems.

Automated Data Readiness for Scientific AI MATEY: multiscale adaptive foundation models for spatiotemporal physical systems

Reference 59

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source=pdf_text observed=2026-07-12T07:09:04.155924Z digest=sha256:4fca1137d89463c5578f4d7083abd4310b6850eab702437b102dbc37936a4470

Pith citing papers

Observation 20a77076-34e0-42ea-a0a5-3e38e3ddaf9c · inbound

SetGo: Metadata Readiness for Scientific AI Datasets cites this paper.

SetGo: Metadata Readiness for Scientific AI Datasets Automated Data Readiness for Scientific AI

Reference 18

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local_arxiv, observed 2026-08-02T07:38:23.536770Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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