# References

> Author-year keys match the in-text citations in `01_introduction.md` and
> `02_background.md`. Verification status from the literature research pass is noted:
> **[V]** = a primary source was fetched and confirmed; **[S]** = corroborated from
> search snippets / authoritative secondary pages but not directly fetched (confirm
> DOI and author order before submission); **[org]** = official standards/repository
> site. See `research_notes.md` for fuller provenance notes.
>
> A `.bib` export should be generated before submission; this file is the
> human-readable working bibliography.

## Environmental / Earth-science harmonization, networks, and standards

- **[Dorigo-2011]** Dorigo, W. A., et al. (2011). The International Soil Moisture
  Network: a data hosting facility for global in situ soil moisture measurements.
  *Hydrology and Earth System Sciences* 15:1675–1698. **[S]**
  https://doi.org/10.5194/hess-15-1675-2011
- **[Dorigo-2013]** Dorigo, W. A., et al. (2013). Global Automated Quality Control of
  In Situ Soil Moisture Data from the International Soil Moisture Network. *Vadose
  Zone Journal* 12(3). **[S]** https://doi.org/10.2136/vzj2012.0097
- **[Dorigo-2021]** Dorigo, W., et al. (2021). The International Soil Moisture Network:
  serving Earth system science for over a decade. *Hydrology and Earth System
  Sciences* 25:5749–5804. **[V]** https://doi.org/10.5194/hess-25-5749-2021 ·
  harmonization docs: https://ismn.earth/en/data/harmonization/
- **[Chu-2023]** Chu, H., Christianson, D. S., Cheah, Y.-W., et al. (2023). AmeriFlux
  BASE data pipeline to support network growth and data sharing. *Scientific Data*
  10:614. **[V]** https://doi.org/10.1038/s41597-023-02531-2
- **[Pastorello-2020]** Pastorello, G., et al. (2020). The FLUXNET2015 dataset and the
  ONEFlux processing pipeline for eddy covariance data. *Scientific Data* 7:225. **[S]**
  https://doi.org/10.1038/s41597-020-0534-3
- **[Cooper-2021]** Cooper, H. M., et al. (2021). COSMOS-UK: national soil moisture and
  hydrometeorology data for environmental science research. *Earth System Science
  Data* 13:1737–1757. **[V]** https://doi.org/10.5194/essd-13-1737-2021
- **[Bogena-2022]** Bogena, H. R., et al. (2022). COSMOS-Europe: a European network of
  cosmic-ray neutron soil moisture sensors. *Earth System Science Data*
  14:1125–1151. **[V]** https://doi.org/10.5194/essd-14-1125-2022
- **[NEON-DP1]** NSF National Ecological Observatory Network. Soil water content and
  water salinity (DP1.00094.001). **[S]**
  https://data.neonscience.org/data-products/DP1.00094.001
- **[CEOS-SM]** CEOS Land Product Validation Subgroup (2020). Soil Moisture Validation
  Good Practices Protocol. **[org]**
  https://lpvs.gsfc.nasa.gov/PDF/CEOS_SM_LPV_Protocol_V1_20201027_final.pdf
- **[NIDIS-2024]** NIDIS (2024). Soil Moisture Data Quality Guidance. **[org]**
  https://www.drought.gov/sites/default/files/2024-12/Soil-Moisture-Data-Quality-Guidance-12-09-2024.pdf

### ESS-DIVE and the Watershed Function SFA

- **[ESS-DIVE]** Environmental Systems Science Data Infrastructure for a Virtual
  Ecosystem (ESS-DIVE), LBNL/DOE. **[org]** https://ess-dive.lbl.gov ·
  https://docs.ess-dive.lbl.gov · https://www.re3data.org/repository/r3d100000019
- **[Crystal-Ornelas-2022]** Crystal-Ornelas, R., Varadharajan, C., et al. (2022).
  Enabling FAIR data in Earth and environmental science with community-centric
  (meta)data reporting formats. *Scientific Data* 9:700. **[V]**
  https://doi.org/10.1038/s41597-022-01606-w
- **[Velliquette-2021-CSV]** Velliquette, T., Welch, J., Crow, M., et al. (2021). CSV
  File Structure Reporting Format. ESS-DIVE. **[V]** DOI 10.15485/1734841 ·
  https://github.com/ess-dive-community/essdive-csv-structure
- **[Goldman-2021]** Goldman, A. E., Ren, H., Torgeson, J., Zhou, H. (2021). Hydrologic
  Monitoring Data and Metadata Reporting Format. ESS-DIVE. **[S]**
  https://www.osti.gov/biblio/1822940
- **[Damerow-2021]** Damerow, J. E., Varadharajan, C., et al. (2021). Sample
  Identifiers and Metadata to Support Data Management and Reuse in Multidisciplinary
  Ecosystem Sciences. *Data Science Journal* 20:11. **[V]**
  https://doi.org/10.5334/dsj-2021-011
- **[Crystal-Ornelas-2021]** Crystal-Ornelas, R., et al. (2021). A Guide to Using
  GitHub for Developing and Versioning Data Standards and Reporting Formats. *Earth
  and Space Science* 8:e2021EA001797. **[S]** https://doi.org/10.1029/2021EA001797
- **[Hubbard-2018]** Hubbard, S. S., Williams, K. H., et al. (2018). The East River,
  Colorado, Watershed: A Mountainous Community Testbed... *Vadose Zone Journal*
  17:180061. **[V]** https://doi.org/10.2136/vzj2018.03.0061
- **[Varadharajan-2022]** Varadharajan, C., Hendrix, V. C., et al. (2022). The Colorado
  East River Community Observatory Data Collection. *Hydrological Processes*
  36:e14243. **[S]** https://doi.org/10.1002/hyp.14243

### Information models, ontologies, vocabularies, units, FAIR

- **[Wilkinson-2016]** Wilkinson, M. D., et al. (2016). The FAIR Guiding Principles for
  scientific data management and stewardship. *Scientific Data* 3:160018. **[V]**
  https://doi.org/10.1038/sdata.2016.18
- **[Horsburgh-2016]** Horsburgh, J. S., et al. (2016). Observations Data Model 2
  (ODM2): A community information model for spatially discrete Earth observations.
  *Environmental Modelling & Software* 79:55–74. **[S]**
  https://doi.org/10.1016/j.envsoft.2016.01.010 · https://www.odm2.org/
- **[CUAHSI-HIS]** CUAHSI Hydrologic Information System / WaterML / HydroShare. **[org]**
  https://his.cuahsi.org/ · https://www.hydroshare.org/
- **[OGC-SensorThings]** Open Geospatial Consortium. SensorThings API Part 1: Sensing
  v1.1 (18-088); Observations & Measurements (ISO 19156). **[V]**
  https://docs.ogc.org/is/18-088/18-088.html
- **[Janowicz-2019]** Janowicz, K., Haller, A., Cox, S. J. D., et al. (2019). The
  modular SSN ontology: A joint W3C and OGC standard. *Semantic Web* 10(1). **[V]**
  https://www.w3.org/TR/vocab-ssn/
- **[Buttigieg-2013]** Buttigieg, P. L., Morrison, N., Smith, B., Mungall, C. J., Lewis,
  S. E. (2013). The environment ontology: contextualising biological and biomedical
  entities. *Journal of Biomedical Semantics* 4:43. **[V]**
  https://doi.org/10.1186/2041-1480-4-43
- **[AGROVOC]** FAO. AGROVOC: the linked data concept hub for food and agriculture.
  **[org]** https://www.fao.org/agrovoc/
- **[CF-Conventions]** NetCDF Climate and Forecast (CF) Metadata Conventions and CF
  Standard Name Table. **[V]** https://cfconventions.org/
- **[NERC-NVS]** NERC Vocabulary Server (BODC P01/P06/P07). **[org]**
  https://vocab.nerc.ac.uk/
- **[UCUM]** Schadow, G., McDonald, C. J. Unified Code for Units of Measure
  (Regenstrief Institute). **[org]** https://ucum.org/ · https://ucum.nlm.nih.gov/
- **[QUDT]** QUDT — Quantities, Units, Dimensions, and Types ontologies. **[org]**
  https://qudt.org/
- **[Rijgersberg-2013]** Rijgersberg, H., van Assem, M., Top, J. (2013). Ontology of
  units of measure and related concepts. *Semantic Web* 4(1):3–13. **[V]**
  https://www.semantic-web-journal.net/sites/default/files/swj177.pdf
- **[Wieczorek-2012]** Wieczorek, J., et al. (2012). Darwin Core: An Evolving
  Community-Developed Biodiversity Data Standard. *PLOS ONE* 7(1):e29715. **[V]**
  https://doi.org/10.1371/journal.pone.0029715 · https://dwc.tdwg.org/

## Data heterogeneity, provenance, and the cost of curation

- **[Mars-Orbiter]** NASA Mars Climate Orbiter Mishap Investigation Board (1999).
  Phase I Report. **[V]** https://science.nasa.gov/mission/mars-climate-orbiter/
- **[Pint]** Grecco, H. Pint: a Python units library. **[org]**
  https://github.com/hgrecco/pint
- **[PROJ]** PROJ — coordinate transformation software; EPSG registry. **[org]**
  https://proj.org/ · https://epsg.org/
- **[Zizka-2019]** Zizka, A., Silvestro, D., Andermann, T., et al. (2019).
  CoordinateCleaner: Standardized cleaning of occurrence records from biological
  collection databases. *Methods in Ecology and Evolution* 10(5):744–751. **[S]**
  https://doi.org/10.1111/2041-210X.13152
- **[ISO-8601]** ISO 8601-1:2019, Date and time representations. **[org]**
  https://www.iso.org/iso-8601-date-and-time-format.html
- **[Wickham-2014]** Wickham, H. (2014). Tidy Data. *Journal of Statistical Software*
  59(10):1–23. **[V]** https://doi.org/10.18637/jss.v059.i10
- **[Poggio-2021]** Poggio, L., et al. (2021). SoilGrids 2.0: producing soil
  information for the globe with quantified spatial uncertainty. *SOIL* 7:217–240.
  **[S]** https://doi.org/10.5194/soil-7-217-2021
- **[Yue-2017]** Yue, K., et al. (2017). Experimental and observational studies find
  contrasting responses of soil nutrients to climate change. *eLife* 6:e23255. **[S]**
  https://doi.org/10.7554/eLife.23255
- **[Heidorn-2008]** Heidorn, P. B. (2008). Shedding Light on the Dark Data in the Long
  Tail of Science. *Library Trends* 57(2):280–299. **[S]**
  https://muse.jhu.edu/article/262029
- **[Blagoderov-2012]** Blagoderov, V., et al. (2012). No specimen left behind:
  industrial-scale digitization of natural history collections. *ZooKeys*
  209:133–146. **[S]** https://doi.org/10.3897/zookeys.209.3178
- **[Kandel-2012]** Kandel, S., Paepcke, A., Hellerstein, J., Heer, J. (2012).
  Enterprise Data Analysis and Visualization: An Interview Study. *IEEE VAST*. **[S]**
  http://vis.stanford.edu/papers/enterprise-analysis-interviews
- **[Lohr-2014]** Lohr, S. (2014). For Big-Data Scientists, 'Janitor Work' Is Key
  Hurdle to Insights. *The New York Times*, Aug 18. **[S — claim provenance weak]**
  https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html
- **[CrowdFlower-2016]** CrowdFlower (2016). Data Science Report. **[S]**
  https://visit.figure-eight.com/data-science-report.html
- **[Dodds-2020]** Dodds, L. (2020). Do data scientists spend 80% of their time
  cleaning data? Turns out, no? **[org]**
  https://blog.ldodds.com/2020/01/31/do-data-scientists-spend-80-of-their-time-cleaning-data-turns-out-no/
- **[Fortier-2017]** Fortier, I., et al. (2017). Maelstrom Research guidelines for
  rigorous retrospective data harmonization. *International Journal of Epidemiology*
  46(1):103–105. **[V]** https://doi.org/10.1093/ije/dyw075
- **[OHDSI-OMOP]** Observational Health Data Sciences and Informatics. OMOP Common
  Data Model. **[org]** https://www.ohdsi.org/data-standardization/
- **[Schneider-2020]** Schneider, F. D., et al. (2020). Towards an ecological
  trait-data standard / harmonizing heterogeneous trait data. *Ecological
  Informatics* 60:101206. **[S]**
  https://doi.org/10.1016/j.ecoinf.2020.101206
- **[Dornelas-2018]** Dornelas, M., et al. (2018). BioTIME: A database of biodiversity
  time series for the Anthropocene. *Global Ecology and Biogeography* 27(7):760–786.
  **[S]** https://doi.org/10.1111/geb.12729
- **[PROV-O]** Lebo, T., Sahoo, S., McGuinness, D. (eds.) (2013). PROV-O: The PROV
  Ontology. W3C Recommendation. **[V]** https://www.w3.org/TR/prov-o/
- **[RO-Crate-2022]** Soiland-Reyes, S., et al. (2022). Packaging research artefacts
  with RO-Crate. *Data Science* 5(2):97–138. **[V]**
  https://doi.org/10.3233/DS-210053

## Data cleaning, schema matching, entity resolution (classical)

- **[Raman-2001]** Raman, V., Hellerstein, J. M. (2001). Potter's Wheel: An Interactive
  Data Cleaning System. *VLDB*. **[S]** http://www.vldb.org/conf/2001/P381.pdf
- **[Kandel-2011]** Kandel, S., Paepcke, A., Hellerstein, J., Heer, J. (2011). Wrangler:
  Interactive Visual Specification of Data Transformation Scripts. *ACM CHI*. **[S]**
  https://idl.uw.edu/papers/wrangler
- **[OpenRefine]** OpenRefine (formerly Google Refine). **[org]**
  https://openrefine.org/
- **[Rahm-2001]** Rahm, E., Bernstein, P. A. (2001). A survey of approaches to
  automatic schema matching. *The VLDB Journal* 10(4):334–350. **[V]**
  https://doi.org/10.1007/s007780100057
- **[Bernstein-2011]** Bernstein, P. A., Madhavan, J., Rahm, E. (2011). Generic Schema
  Matching, Ten Years Later. *PVLDB* 4(11):695–701. **[S]**
  https://vldb.org/pvldb/vol4/p695-bernstein_madhavan_rahm.pdf
- **[Ilyas-2019]** Ilyas, I. F., Chu, X. (2019). *Data Cleaning*. ACM Books, Vol. 28.
  **[S]** https://doi.org/10.1145/3310205
- **[Rekatsinas-2017]** Rekatsinas, T., Chu, X., Ilyas, I. F., Ré, C. (2017). HoloClean:
  Holistic Data Repairs with Probabilistic Inference. *PVLDB* 10(11):1190–1201. **[V]**
  https://doi.org/10.14778/3137628.3137631
- **[Fellegi-1969]** Fellegi, I. P., Sunter, A. B. (1969). A Theory for Record Linkage.
  *Journal of the American Statistical Association* 64(328):1183–1210. **[S]**
  https://doi.org/10.1080/01621459.1969.10501049
- **[Konda-2016]** Konda, P., Das, S., Suganthan G.C., P., Doan, A., et al. (2016).
  Magellan: Toward Building Entity Matching Management Systems. *PVLDB*
  9(13):1581–1584. **[V]** https://doi.org/10.14778/2994509.2994535
- **[Gulwani-2011]** Gulwani, S. (2011). Automating String Processing in Spreadsheets
  Using Input-Output Examples (FlashFill). *POPL*. **[S]**
  https://doi.org/10.1145/1926385.1926423

## LLM / foundation-model methods for data work

- **[Li-2020]** Li, Y., Li, J., Suhara, Y., Doan, A., Tan, W.-C. (2020). Deep Entity
  Matching with Pre-Trained Language Models (DITTO). *PVLDB* 13(12). **[S]**
  https://arxiv.org/abs/2004.00584
- **[Narayan-2022]** Narayan, A., Chami, I., Orr, L., Arora, S., Ré, C. (2022). Can
  Foundation Models Wrangle Your Data? *PVLDB* 16(4):738–746. **[V]**
  https://arxiv.org/abs/2205.09911
- **[Li-2023]** Li, P., He, Y., et al. (2023). Table-GPT: Table-tuned GPT for Diverse
  Table Tasks. *arXiv:2310.09263*. **[V]** https://arxiv.org/abs/2310.09263
- **[Parciak-2024]** Parciak, M., et al. (2024). Schema Matching with Large Language
  Models: An Experimental Study. *VLDB Workshops (TaDA)*. **[V]**
  https://arxiv.org/abs/2407.11852
- **[Qi-2024]** Qi, D., Miao, R., Wang, J. (2024). CleanAgent: Automating Data
  Standardization with LLM-based Agents. *arXiv:2403.08291*. **[V]**
  https://arxiv.org/abs/2403.08291
- **[Wang-2025]** Wang, et al. (2025). Dataforge: Agentic Platform for Autonomous Data
  Engineering. *arXiv:2511.06185*. **[V]** https://arxiv.org/abs/2511.06185
- **[Zhang-2024]** Zhang, H., et al. (2024). Jellyfish: A Large Language Model for Data
  Preprocessing. *EMNLP*. **[S]** https://arxiv.org/abs/2312.01678
- **[KcMF-2024]** (2024). KcMF: A Knowledge-compliant Framework for Schema and Entity
  Matching with Fine-tuning-free LLMs. *arXiv:2410.12480*. **[S]**
  https://arxiv.org/abs/2410.12480
- **[TabulaX-2024]** (2024). TabulaX: Leveraging LLMs for Multi-Class Table
  Transformations. *arXiv:2411.17110*. **[S]** https://arxiv.org/abs/2411.17110
- **[Prompt2DAG-2025]** (2025). Prompt2DAG: Modular Methodology for LLM-Based Data
  Enrichment Pipeline Generation. *arXiv:2509.13487*. **[S]**
  https://arxiv.org/abs/2509.13487

## Agentic harmonization, multi-agent systems, and agent evaluation

- **[Santos-2025]** Santos, A., Pena, E., Lopez, R., Freire, J. (2025). Interactive Data
  Harmonization with LLM Agents (Harmonia). *NOVAS'25 (co-located with SIGMOD)*. **[V]**
  https://arxiv.org/abs/2502.07132
- **[Cemri-2025]** Cemri, M., Pan, M., et al. (2025). Why Do Multi-Agent LLM Systems
  Fail? (MAST). *NeurIPS Datasets & Benchmarks*. **[V]**
  https://github.com/multi-agent-systems-failure-taxonomy/MAST
- **[Zhang-2025-DataSci]** Zhang, D., et al. (2025). DataSciBench: An LLM Agent
  Benchmark for Data Science. *arXiv:2502.13897*. **[V]**
  https://arxiv.org/abs/2502.13897
- **[KramaBench-2025]** (2025). KramaBench: A Benchmark for AI Systems on
  Data-to-Insight Pipelines over Data Lakes. *arXiv:2506.06541*. **[V]**
  https://arxiv.org/abs/2506.06541 · https://kramabench.org/
- **[Jing-2024]** Jing, L., et al. (2024). DSBench: How Far Are Data Science Agents from
  Becoming Data Science Experts? *arXiv:2409.07703*. **[V]**
  https://arxiv.org/abs/2409.07703
- **[Hu-2024]** Hu, X., et al. (2024). InfiAgent-DABench: Evaluating Agents on Data
  Analysis Tasks. *ICML*. **[S]** https://arxiv.org/abs/2401.05507
- **[JudgeReliability-2026]** (2026). Judge Reliability Harness: Stress Testing the
  Reliability of LLM Judges. *arXiv:2603.05399*. **[S — 2026 preprint, confirm]**
  https://arxiv.org/abs/2603.05399
- **[MOLE-2025]** Alyafeai, Z., Al-Shaibani, M., Ghanem, B. (2025). MOLE: Metadata
  Extraction and Validation in Scientific Papers Using LLMs. *EMNLP Findings*. **[V]**
  https://arxiv.org/abs/2505.19800
- **[DCAT-LLM-2025]** (2025). Exploring LLM Capabilities in Extracting DCAT-Compatible
  Metadata for Data Cataloging. *arXiv:2507.05282*. **[S]**
  https://arxiv.org/abs/2507.05282
- **[AgentSkills-2025]** Anthropic (2025). Agent Skills. **[org — cite primary
  engineering blog / agentskills.io at submission]** https://www.anthropic.com/

## Benchmarks for cleaning, matching, and table tasks

- **[Abedjan-2016]** Abedjan, Z., et al. (2016). Detecting Data Errors: Where are we and
  what needs to be done? *PVLDB* 9(12):993–1004. **[S]**
  https://doi.org/10.14778/2994509.2994518
- **[CleanML-2021]** Li, P., Rao, X., Blase, J., Zhang, Y., Chu, X., Zhang, C. (2021).
  CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification
  Tasks. *ICDE*. **[V]** https://arxiv.org/abs/1904.09483
- **[Arocena-2015]** Arocena, P. C., Glavic, B., Mecca, G., Miller, R. J., Papotti, P.,
  Santoro, D. (2015). Messing Up with BART: Error Generation for Evaluating
  Data-Cleaning Algorithms. *PVLDB* 9(2):36–47. **[S]**
  http://www.vldb.org/pvldb/vol9/p36-arocena.pdf
- **[Mudgal-2018]** Mudgal, S., et al. (2018). Deep Learning for Entity Matching: A
  Design Space Exploration (DeepMatcher). *SIGMOD*. **[S]**
  https://doi.org/10.1145/3183713.3196926
- **[Peeters-2024]** Peeters, R., Der, R. C., Bizer, C. (2024). WDC Products: A
  Multi-Dimensional Entity Matching Benchmark. *EDBT*. **[S]**
  http://webdatacommons.org/largescaleproductcorpus/v2/
- **[Hulsebos-2019]** Hulsebos, M., et al. (2019). Sherlock: A Deep Learning Approach to
  Semantic Data Type Detection. *KDD*. **[S]** https://doi.org/10.1145/3292500.3330993
- **[SemTab]** Jiménez-Ruiz, E., Hassanzadeh, O., et al. (2019–). Semantic Web Challenge
  on Tabular Data to Knowledge Graph Matching (SemTab). *CEUR-WS*. **[org]**
  https://sem-tab-challenge.github.io/
- **[OAEI]** Ontology Alignment Evaluation Initiative. **[org]**
  http://oaei.ontologymatching.org/