cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1340050.10fu
title: SH1340050.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1340051.10fu
title: SH1340051.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1340052.10fu
title: SH1340052.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1340054.10fu
title: SH1340054.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1340055.10fu
title: SH1340055.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1340056.10fu
title: SH1340056.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1340058.10fu
title: SH1340058.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.y7wneq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"BRA.9.157_2","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004888-260108223611665/datasets/export for details.
DOI: 10.15156/bio/sh1340060.10fu
title: SH1340060.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1340061.10fu
title: SH1340061.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.gnc487
title: Occurrence Download

description: A dataset containing 1644 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Protomegabaria Hutch."
 ]
}

 The dataset includes 1644 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016109-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/11926538
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.79kzyr
title: Occurrence Download

description: A dataset containing 118 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Sus scrofa Linnaeus, 1758, Sphenomorphus decipiens Brown & Alcala, 1980)"
 ]
}

 The dataset includes 118 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016112-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15224451
title: Table Union Search Benchmarks

description: This repository contains the data used to reproduce the results of the paper: "Something's Fishy in the Data Lake: A Critical Re-evaluation of Table Union Search Benchmarks"

Please note that these benchmarks were not created by us. We simply repackaged them into a cleaner and more unified format to ensure compatibility with our code, which is available at: https://github.com/Allaa-boutaleb/fishy-tus. You can download the benchmarks by following the procedures and links in their original repositories:

- SANTOS: https://github.com/northeastern-datalab/santos- TUS-SMALL / TUS-LARGE: https://github.com/RJMillerLab/table-union-search-benchmark- PYLON: https://github.com/superctj/pylon/tree/main- UGEN-V1 / UGEN-V2: https://github.com/northeastern-datalab/gen- LB-OPENDATA / LB-WEBTABLE: https://github.com/RLGen/LakeBench

Each of these benchmarks requires different preprocessing steps before they can be used with our code to reproduce the results.

Inside each .tar.gz file, you will find:- a folder named `datalake/` containing datalake CSV tables- a folder named `query/` containing query CSV tables- a file named `benchmark.pkl`, which represents the benchmark ground truth. The `benchmark.pkl` file is a dictionary where the key is the name of the query table and the value is a list of unionable candidate table names. Example: key: "query.csv", value: ["candidate_1.csv", "candidate_2.csv", ...]

Notes:

- TUS and TUS-LARGE already include the sampled set of queries used for evaluation, located in the `query/` folder. The original full set of queries is also available in the `all_query/` subfolder.- For LakeBench benchmarks (LB-OPENDATA / LB-WEBTABLE), additional preprocessing was done:  a) Removed any table not referenced in the ground truth  b) Removed any ground truth entry pointing to a non-existent CSV  c) Added each query table as a candidate to itself for consistency

- For LB-OpenData, a smaller version was also created, limiting tables to a maximum of 1,000 rows. This was necessary due to the high computational cost of running some methods (e.g., Starmie) on the full version.

LICENSE:

The datasets are shared under the CC BY-SA 4.0 license.

DISCLAIMER:

THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND. WE ARE NOT THE ORIGINAL AUTHORS OF THE BENCHMARKS, AND WE ONLY REDISTRIBUTED THEM IN A MORE ACCESSIBLE FORMAT TO FACILITATE REPRODUCIBILITY.
DOI: 10.5281/zenodo.15224450
title: Table Union Search Benchmarks

description: 🚨V2 UPDATE: LB-OpenData and LB-WebTable have been updated by the original authors as of May 20th, 2025. (https://github.com/RLGen/LakeBench). We've preprocessed (see below) and repackaged them accordingly. We've also added a larger truncated version of LB-OpenData (30k rows max per table). The results in our paper have been updated to reflect these changes. While there are slight variations, they do not affect the core conclusions of our study.

 

🐟"Something's Fishy in the Data Lake: A Critical Re-evaluation of Table Union Search Benchmarks"

Please note that these benchmarks were not created by us. We simply repackaged them into a cleaner and more unified format to ensure compatibility with our code, which is available at: https://github.com/Allaa-boutaleb/fishy-tus. You can download the benchmarks by following the procedures and links in their original repositories:

- SANTOS: https://github.com/northeastern-datalab/santos- TUS-SMALL / TUS-LARGE: https://github.com/RJMillerLab/table-union-search-benchmark- PYLON: https://github.com/superctj/pylon/tree/main- UGEN-V1 / UGEN-V2: https://github.com/northeastern-datalab/gen- LB-OPENDATA / LB-WEBTABLE: https://github.com/RLGen/LakeBench

Each of these benchmarks requires different preprocessing steps before they can be used with our code to reproduce the results.

Inside each .tar.gz file, you will find:- a folder named `datalake/` containing datalake CSV tables- a folder named `query/` containing query CSV tables- a file named `benchmark.pkl`, which represents the benchmark ground truth. The `benchmark.pkl` file is a dictionary where the key is the name of the query table and the value is a list of unionable candidate table names. Example: key: "query.csv", value: ["candidate_1.csv", "candidate_2.csv", ...]

Notes:

- TUS and TUS-LARGE already include the sampled set of queries used for evaluation, located in the `query/` folder. The original full set of queries is also available in the `all_query/` subfolder.- For LakeBench benchmarks (LB-OPENDATA / LB-WEBTABLE), additional preprocessing was done:  a) Removed any table not referenced in the ground truth  b) Removed any ground truth entry pointing to a non-existent CSV  c) Added each query table as a candidate to itself for consistency

- For LB-OpenData, a smaller version was also created, limiting tables to a maximum of 1,000 rows. This was necessary due to the high computational cost of running some methods (e.g., Starmie) on the full version.

LICENSE:

The datasets are shared under the CC BY-SA 4.0 license.

DISCLAIMER:

THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND. WE ARE NOT THE ORIGINAL AUTHORS OF THE BENCHMARKS, AND WE ONLY REDISTRIBUTED THEM IN A MORE ACCESSIBLE FORMAT TO FACILITATE REPRODUCIBILITY.
DOI: 10.5281/zenodo.15499092
title: Table Union Search Benchmarks

description: 🚨V2 UPDATE: LB-OpenData and LB-WebTable have been updated by the original authors as of May 20th, 2025. (https://github.com/RLGen/LakeBench). We've preprocessed (see below) and repackaged them accordingly. We've also added a larger truncated version of LB-OpenData (30k rows max per table). The results in our paper have been updated to reflect these changes. While there are slight variations, they do not affect the core conclusions of our study.

 

🐟"Something's Fishy in the Data Lake: A Critical Re-evaluation of Table Union Search Benchmarks"

Please note that these benchmarks were not created by us. We simply repackaged them into a cleaner and more unified format to ensure compatibility with our code, which is available at: https://github.com/Allaa-boutaleb/fishy-tus. You can download the benchmarks by following the procedures and links in their original repositories:

- SANTOS: https://github.com/northeastern-datalab/santos- TUS-SMALL / TUS-LARGE: https://github.com/RJMillerLab/table-union-search-benchmark- PYLON: https://github.com/superctj/pylon/tree/main- UGEN-V1 / UGEN-V2: https://github.com/northeastern-datalab/gen- LB-OPENDATA / LB-WEBTABLE: https://github.com/RLGen/LakeBench

Each of these benchmarks requires different preprocessing steps before they can be used with our code to reproduce the results.

Inside each .tar.gz file, you will find:- a folder named `datalake/` containing datalake CSV tables- a folder named `query/` containing query CSV tables- a file named `benchmark.pkl`, which represents the benchmark ground truth. The `benchmark.pkl` file is a dictionary where the key is the name of the query table and the value is a list of unionable candidate table names. Example: key: "query.csv", value: ["candidate_1.csv", "candidate_2.csv", ...]

Notes:

- TUS and TUS-LARGE already include the sampled set of queries used for evaluation, located in the `query/` folder. The original full set of queries is also available in the `all_query/` subfolder.- For LakeBench benchmarks (LB-OPENDATA / LB-WEBTABLE), additional preprocessing was done:  a) Removed any table not referenced in the ground truth  b) Removed any ground truth entry pointing to a non-existent CSV  c) Added each query table as a candidate to itself for consistency

- For LB-OpenData, a smaller version was also created, limiting tables to a maximum of 1,000 rows. This was necessary due to the high computational cost of running some methods (e.g., Starmie) on the full version.

LICENSE:

The datasets are shared under the CC BY-SA 4.0 license.

DISCLAIMER:

THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND. WE ARE NOT THE ORIGINAL AUTHORS OF THE BENCHMARKS, AND WE ONLY REDISTRIBUTED THEM IN A MORE ACCESSIBLE FORMAT TO FACILITATE REPRODUCIBILITY.
DOI: 10.5281/zenodo.15499571
title: Data and Code for: "Assessing Contamination Risk Zones with Water Age Modeling in Terminos Lagoon, Southeastern Coastal Mexico"

description: Data and Software Used in the Article: Assessing Contamination Risk Zones with Water Age Modeling in Terminos Lagoon, Southeastern Coastal Mexico (Submitted)



Authors:                                

 José-Gilberto Cardoso-Mohedanoa,*, Eduardo Gómez-de la Peñab,c, Marisol Meneses-Fernándezd, Joan-Albert Sánchez-Cabezae, Ana-Carolina Ruiz-Fernándeze, María Eugenia Allende-Arandíaf, Mario-Alejandro Gómez-Poncea, Carlos Alberto Herrera-Becerrilb, Jorge Feliciano Ontiveros-Cuadrasg

 Affiliations:

 a Estación el Carmen, Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México, Carretera Carmen-Puerto Real km. 9.5, 24157 Ciudad del Carmen, Campeche, México.

b Posgrado en Ciencias de la Tierra, Universidad Nacional Autónoma de México; Av. Universidad 3000, Ciudad Universitaria Coyoacán, C.P. 04510, Ciudad de México, México. 

c School of Environment, The University of Auckland, Tāmaki Makaurau / Auckland, Aotearoa / New Zealand.

d Posgrado en Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Av. Universidad 3000, Ciudad Universitaria Coyoacán, C.P. 04510, Ciudad de México, México. 

e Unidad Académica Mazatlán, Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México, 82040 Mazatlán, Sinaloa, México. 

e Laboratorio de Ingeniería y Procesos Costeros, Instituto de Ingeniería, Universidad Nacional Autónoma de México, Yucatán, 97835, México.

g Unidad Procesos Oceánicos y Costeros. Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Av. Universidad 3000, Ciudad Universitaria Coyoacán, C.P. 04510, Ciudad de México, México.




Corresponding author:

José-Gilberto Cardoso-Mohedano  

Tel: +52-938-1120285 ext. 201  

Email: [gcardoso@cmarl.unam.mx](mailto:gcardoso@cmarl.unam.mx)

 


This release includes the Python scripts, model results, and supporting datasets used to evaluate water age dynamics and contamination risk zones in Terminos Lagoon, southeastern Mexico.

The hydrodynamic simulations were performed using Delft3D, and all post-processing and analyses were implemented in Python.

This version supports the manuscript:



Assessing Contamination Risk Zones with Water Age Modeling in Terminos Lagoon, Southeastern Coastal Mexico (Submitted)


Main components:



Scripts for water age calculation using model outputs.

Evaluation of salinity and temperature model performance against field observations.

Interannual flow analysis for the Candelaria and Palizada Rivers based on official CONAGUA data.

Data products for water age (in days) were derived across key lagoon zones.


This repository aims to provide a reproducible workflow for coastal hydrodynamic and water quality analysis in tropical estuarine systems.
DOI: 10.3886/e230965
title: Gender welfare schemes

description: The dataset has 407 responses related to recently introduced welfare schemes for women in India.
DOI: 10.3886/e230965v1
title: Gender welfare schemes

description: The dataset has 407 responses related to recently introduced welfare schemes for women in India.