cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.4742aw
title: Occurrence Download

description: A dataset containing 698 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, India, Chinese Taipei, Philippines, Indonesia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Curcuma longa"
 ]
}

 The dataset includes 698 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020468-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zsz8mv
title: Occurrence Download

description: A dataset containing 847 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Echinochloa crus-galli praticola"
 ]
}

 The dataset includes 847 records from 70 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020470-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25921/4n4e-h619
title: Near real-time environmental monitoring in support of Port Everglades dredging projects, Fort Lauderdale, FL (NCEI Accession 0303581)

description: The system records in-situ water quality data at a depth of 26 feet, including temperature, salinity, turbidity, backscatter, PAR (Photosynthetically Active Radiation), and currents, among other parameters. Additionally, climate data is collected at the surface, which includes wind, pressure, and PAR. This data is continuously gathered from a buoy located in Port Everglades, Florida, covering both the sea surface and the seafloor.

The bottom-deployed sensor array is equipped with an Echologger ECT400 altimeter, Aqua TROLL 600 (In Situ), acoustic modem, NexSens data logger, Nortek wave/current meter, an ECO-PAR sensor that measures photosynthetically active radiation–specifically the light levels crucial for photosynthesis, and LISST-ABS acoustic sediment sensor to continuously monitor sediment dynamics.

Data collected by the bottom array is logged by a NexSens logger, and then an acoustic modem transfers the data through the water column to the CB-950 data buoy on the surface that is equipped with another NexSens logger and an Airmar multiparameter weather station and LI-COR PAR sensor to measure climate conditions.

Data from the surface buoy and the bottom sensor package are transferred to the cloud, where the data can be viewed via WQData LIVE.
DOI: 10.17026/ss/vxjxh0
title: Drugs Monologen App Betweterfestival Utrecht

description: Drugs Monologen was een digital storytelling en oral history project van de Open Universiteit (Faculteit Cultuurwetenschappen) en het Poppi Drugs Museum dat liep van oktober 2023 tot oktober 2025. Het project draaide om het bestuderen van de Nederlandse drugsgeschiedenis vanuit het perspectief van mensen die zelf drugs gebruikten in de periode ca. 1960-2000. In dit kader vonden 28 oral history interviews en 5 thematische groepsinterviews plaats. Daarnaast werd een speciale app ontwikkeld waarmee deelnemers op de Drugs Monologen website hun eigen historische ‘monoloog’ konden vertellen en bijdragen aan het onderzoek. Deze laatste methode leverde 51 monologen op. De oral history interviews, groepsinterviews en monologen waarvoor van de verteller toestemming werd verkregen voor gebruik voor toekomstig onderzoek zijn hier bewaard. Bij alle Nederlandstalige items is een transcriptie toegevoegd. Dit item maakt deel uit van deze datacollectie. Vanwege de privacygevoelige aard van de data kunnen deze alleen worden geraadpleegd na via de daartoe bestemde knop toestemming te hebben aangevraagd en verkregen voor toegang tot deze dataset.
description: Deze dataset bestaat uit monologen opgenomen tijdens het Betweterfestival in Tivoli in Utrecht op 26 september 2025. Op dit festival konden bezoekers plaatsnemen in de Drugs Monologen ‘huiskamer’ en de Drugs Monologen app gebruiken op een beeldscherm dat daar was neergezet. Er kon één persoon tegelijk vertellen via een headset, maar vaak namen mensen met z’n tweeën plaats op de bank en dan is soms een tweede persoon hoorbaar in de achtergrond.
DOI: 10.25824/redu/7nknxz
title: Input-aware heuristic with high-level synthesis of approximate hardware

description: This repository provides the experimental dataset and supporting files generated for an input-aware approximate computing framework targeting HLS-based accelerators. The data were produced using an approach that maps accelerator operators, such as adders and multipliers, to a library of precharacterized approximate components in order to reduce hardware resource utilization while controlling output errors. Applications were executed with representative training inputs, and candidate approximate designs were selected using a combined metric of output error and estimated resource usage. The dataset includes results for image processing and CNN workloads, showing that the approach can reduce LUT and FF usage by up to 55% for less than 25% output degradation, and achieve similar savings for a CNN model with less than 0.8% accuracy degradation. The repository contains application source code, configuration files, datasets, Jupyter notebooks, and component characterization data. For each application, the corresponding folder includes setup files, training and testing datasets, error evaluation scripts, hardware source code templates for Vitis, and a CSV file with component features related to error and LUT+FF utilization.
DOI: 10.15468/dl.qnrgw2
title: Occurrence Download

description: A dataset containing 150 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((99.31 8.195097000000001, 99.239018 8.189525, 99.16978899999999 8.172947000000001, 99.104024 8.145771999999999, 99.043345 8.108670999999999, 98.98925 8.06256, 98.94307000000001 8.008578, 98.90594299999999 7.948056, 98.87877899999999 7.882488, 98.86224300000001 7.813488, 98.856737 7.742759, 98.862392 7.672041, 98.879062 7.603076, 98.906333 7.537559, 98.94353 7.477103, 98.989733 7.423194, 99.04380500000001 7.377157, 99.104415 7.340121, 99.170073 7.312998, 99.23916699999999 7.296453, 99.31 7.290893, 99.380833 7.296453, 99.449927 7.312998, 99.515585 7.340121, 99.576195 7.377157, 99.630267 7.423194, 99.67646999999999 7.477103, 99.713667 7.537559, 99.740938 7.603076, 99.757608 7.672041, 99.76326299999999 7.742759, 99.757757 7.813488, 99.741221 7.882488, 99.714057 7.948056, 99.67693 8.008578, 99.63075000000001 8.06256, 99.576655 8.108670999999999, 99.51597599999999 8.145771999999999, 99.450211 8.172947000000001, 99.380982 8.189525, 99.31 8.195097000000001))"},{"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 150 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020471-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ss/lkw4vc
title: Drugs Monologen App Bogota

description: Drugs Monologen was een digital storytelling en oral history project van de Open Universiteit (Faculteit Cultuurwetenschappen) en het Poppi Drugs Museum dat liep van oktober 2023 tot oktober 2025. Het project draaide om het bestuderen van de Nederlandse drugsgeschiedenis vanuit het perspectief van mensen die zelf drugs gebruikten in de periode ca. 1960-2000. In dit kader vonden 28 oral history interviews en 5 thematische groepsinterviews plaats. Daarnaast werd een speciale app ontwikkeld waarmee deelnemers op de Drugs Monologen website hun eigen historische ‘monoloog’ konden vertellen en bijdragen aan het onderzoek. Deze laatste methode leverde 51 monologen op. De oral history interviews, groepsinterviews en monologen waarvoor van de verteller toestemming werd verkregen voor gebruik voor toekomstig onderzoek zijn hier bewaard. Bij alle Nederlandstalige items is een transcriptie toegevoegd. Dit item maakt deel uit van deze datacollectie. Vanwege de privacygevoelige aard van de data kunnen deze alleen worden geraadpleegd na via de daartoe bestemde knop toestemming te hebben aangevraagd en verkregen voor toegang tot deze dataset.
description: Deze dataset bestaat uit acht monologen opgenomen tijdens de Harm Reduction
International Conference in Bogotá, Colombia, op 27-30 april 2025. Op deze conferentie stond een installatie van Drugs Monologen waarin deelnemers aan de conferentie, afkomstig uit allerlei landen, konden plaatsnemen om een eigen monoloog op te nemen. Van deze monologen zijn geen transcripties.
DOI: 10.5281/zenodo.15421638
title: Data and results for "Bridging the gap between the interseismic viscoelastic locking and coseismic slip pattern"

description: Files for the interseismic coupling, coseismic LOS and azimuth displacements, coseismic 3D deformation maps, back-projection results, finite-fault slip model, and relocated background seismicity for the 2025 Mw 7.8 Myanmar earthquake.
DOI: 10.5281/zenodo.16990609
title: Data and results for "Bridging the gap between the interseismic viscoelastic locking and coseismic slip pattern"

description: Files for the interseismic coupling, coseismic LOS and azimuth displacements, coseismic 3D deformation maps, back-projection results, finite-fault slip model, and relocated background seismicity for the 2025 Mw 7.8 Myanmar earthquake.
DOI: 10.5281/zenodo.14289795
title: Non-targeted analysis of lipophilic and hydrophilic metabolites to distinguish between fresh and frozen-thawed fish of certain fish species using comprehensive 1H NMR spectroscopy and multivariate data analysis

description: This dataset is the basis for a publication regarding the differentiation of fresh and frozen-thawed fish via nuclear magnetic resonance spectroscopy (https://doi.org/10.1007/s00217-025-04978-6).

Food fraud along the production chain is a well-known issue that requires an effective authenticity control. For the differentiation of fresh and frozen-thawed fish, 1H nuclear magnetic resonance (NMR) spectroscopy based methods in combination with multivariate data analysis have proven to be suitable in principle. Here, from a total of 317 samples (cod, rainbow trout, mackerel; fresh and frozen-thawed), the lipid and polar fractions of the fish flesh were analyzed, and classification models based on a principal components analysis with linear discriminant analysis (PCA-LDA) including cross-validation were generated. Additionally, data fusions were carried out. The obtained average accuracies of > 90% (94.0% based on the lipid fraction, 92.8% based on the polar fraction) and > 95% (95.6% based on a low-level data fusion,95.5% based on a mid-level data fusion) demonstrated a promising differentiation. Further examinations confirmed that the non-targeted analysis appears to be mandatory as no marker substances were indicated in the loadings plots of the models. To evaluate whether the generated classification models are suitable to be used in a broader manner, they were applied to 13 fresh and 13 frozen-thawed samples from twelve other common edible fish species in a preliminary study. The classificationmodel based on the low-level data fusion gave the best results (84.6% of all 26 samples correctly predicted). Thus, although these models are very suitable for analyzing cod, rainbow trout, or mackerel for a classification as fresh or frozen-thawed, they cannot generally be applied to samples of other fish species.

Datasets containing NMR spectra of samples from the classification models and of samples from other fish species are provided, as well as relevant metadata.
DOI: 10.5281/zenodo.14289794
title: Non-targeted analysis of lipophilic and hydrophilic metabolites to distinguish between fresh and frozen-thawed fish of certain fish species using comprehensive 1H NMR spectroscopy and multivariate data analysis

description: This dataset is the basis for a publication regarding the differentiation of fresh and frozen-thawed fish via nuclear magnetic resonance spectroscopy (https://doi.org/10.1007/s00217-025-04978-6).

Food fraud along the production chain is a well-known issue that requires an effective authenticity control. For the differentiation of fresh and frozen-thawed fish, 1H nuclear magnetic resonance (NMR) spectroscopy based methods in combination with multivariate data analysis have proven to be suitable in principle. Here, from a total of 317 samples (cod, rainbow trout, mackerel; fresh and frozen-thawed), the lipid and polar fractions of the fish flesh were analyzed, and classification models based on a principal components analysis with linear discriminant analysis (PCA-LDA) including cross-validation were generated. Additionally, data fusions were carried out. The obtained average accuracies of > 90% (94.0% based on the lipid fraction, 92.8% based on the polar fraction) and > 95% (95.6% based on a low-level data fusion,95.5% based on a mid-level data fusion) demonstrated a promising differentiation. Further examinations confirmed that the non-targeted analysis appears to be mandatory as no marker substances were indicated in the loadings plots of the models. To evaluate whether the generated classification models are suitable to be used in a broader manner, they were applied to 13 fresh and 13 frozen-thawed samples from twelve other common edible fish species in a preliminary study. The classificationmodel based on the low-level data fusion gave the best results (84.6% of all 26 samples correctly predicted). Thus, although these models are very suitable for analyzing cod, rainbow trout, or mackerel for a classification as fresh or frozen-thawed, they cannot generally be applied to samples of other fish species.

Datasets containing NMR spectra of samples from the classification models and of samples from other fish species are provided, as well as relevant metadata.
DOI: 10.5518/1646
title: Dataset for 'Sustainability in the context of the UK pharmaceutical industry (2000-2020): Media representations and stakeholder perceptions'

description: Set of transcripts of ten semi-structured interviews, conducted in 2023 and 2024 via online meetings, with participants whose knowledge and experience was useful for the research on the doctoral thesis about the concept of sustainability in the context of the UK pharmaceutical industry in the period between 2000 and 2020 and about its media representations and stakeholder perceptions, in particular the research on how social evaluators take on the role of disseminating and evaluating corporate sustainability and examination on how social evaluation and the engagement of social influencers play out in the setting of joint multi-stakeholders sustainability projects, conducted by Dejan Zec between 2020 and 2024, for this doctoral thesis at the School of Earth and Environment, The University of Leeds.
DOI: 10.5281/zenodo.17191757
title: raw-data-gap-acceptance

description: UAV dataset of merging area in City of Zagreb
DOI: 10.5281/zenodo.18299889
title: raw-data-gap-acceptance

description: UAV dataset of merging area in City of Zagreb
DOI: 10.15468/dl.wcea4a
title: Occurrence Download

description: A dataset containing 544 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Austria",
 "GadmGid is AUT.5_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Limax maximus"
 ]
}

 The dataset includes 544 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020473-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4hj938
title: Occurrence Download

description: A dataset containing 404 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Lithuania",
 "DatasetKey is Estonian University of Life Sciences Institute of Agricultural and Environmental Sciences Entomological Collection"
 ]
}

 The dataset includes 404 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020474-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u2fzyt
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-79.42400000000001 -0.743817, -79.494269 -0.749383, -79.562808 -0.765945, -79.627931 -0.793095, -79.688034 -0.830165, -79.74163799999999 -0.876241, -79.78742200000001 -0.930191, -79.82426100000001 -0.990685, -79.851246 -1.056236, -79.86771299999999 -1.125228, -79.873255 -1.195963, -79.867735 -1.2667, -79.85128899999999 -1.335697, -79.82432 -1.401255, -79.787492 -1.46176, -79.741711 -1.515721, -79.688104 -1.561809, -79.62799099999999 -1.598889, -79.56285200000001 -1.626047, -79.494292 -1.642614, -79.42400000000001 -1.648182, -79.353708 -1.642614, -79.28514800000001 -1.626047, -79.220009 -1.598889, -79.159896 -1.561809, -79.106289 -1.515721, -79.060508 -1.46176, -79.02368 -1.401255, -78.996711 -1.335697, -78.980265 -1.2667, -78.974745 -1.195963, -78.980287 -1.125228, -78.996754 -1.056236, -79.02373900000001 -0.990685, -79.06057800000001 -0.930191, -79.106362 -0.876241, -79.159966 -0.830165, -79.220069 -0.793095, -79.285192 -0.765945, -79.353731 -0.749383, -79.42400000000001 -0.743817))"},{"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 5 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020472-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17675255
title: SEMODS: A Validated Dataset of Open-Source Software Engineering Models (November 2025 Snapshot)

description: This dataset accompanies the paper "SEMODS: A Validated Dataset of Open-Source Software Engineering Models", accepted at the 3rd ACM International Conference on AI Foundation Models and Software Engineering (FORGE 2026)

Dataset Description 

SEMODS (Software Engineering MODels) is a validated dataset comprising 3,427 open-source software engineering models hosted on Hugging Face. It provides a structured view of the SE model ecosystem as of November 2025, addressing the lack of SE-specific catalogues in generalist model registries. It offers a standardized representation of model evaluation results and supports multiple applications such as data analysis, model discovery, benchmarking, and model adaptation. 

The dataset is provided as a PostgreSQL dump file. Important: The dump was created using PostgreSQL 17. Users must restore it using PostgreSQL server and client tools version 17 or newer. Dumps created with newer PostgreSQL versions are not backward compatible.

How to load the database locally



Install and start PostgreSQL 17:


brew install postgresql@17

brew services start postgresql@17

     2. Create the database:

/opt/homebrew/opt/postgresql@17/bin/createdb semods

     3. Restore the dump (Replace /path/to/ with the actual path to your downloaded file)

/opt/homebrew/opt/postgresql@17/bin/pg_restore -d semods /path/to/SEMODS_202511.dump

Alternatively, you can use graphical tools such as DBeaver by connecting to a PostgreSQL 17 instance and using the "Restore" option.

Citation

Please cite the associated FORGE 2026 paper when using this dataset:



@article{gonzalez2026semods,  title={SEMODS: A Validated Dataset of Open-Source Software Engineering Models},  author={Gonz{\'a}lez, Alexandra and Franch, Xavier and Mart{\'\i}nez-Fern{\'a}ndez, Silverio},  journal={arXiv preprint arXiv:2601.00635},  year={2026}}
DOI: 10.5281/zenodo.18299542
title: SEMODS: A Validated Dataset of Open-Source Software Engineering Models (November 2025 Snapshot)

description: This dataset accompanies the paper "SEMODS: A Validated Dataset of Open-Source Software Engineering Models", accepted at the 3rd ACM International Conference on AI Foundation Models and Software Engineering (FORGE 2026)

Dataset Description 

SEMODS (Software Engineering MODels) is a validated dataset comprising 3,427 open-source software engineering models hosted on Hugging Face. It provides a structured view of the SE model ecosystem as of November 2025, addressing the lack of SE-specific catalogues in generalist model registries. It offers a standardized representation of model evaluation results and supports multiple applications such as data analysis, model discovery, benchmarking, and model adaptation. 

The dataset is provided as a PostgreSQL dump file. Important: The dump was created using PostgreSQL 17. Users must restore it using PostgreSQL server and client tools version 17 or newer. Dumps created with newer PostgreSQL versions are not backward compatible.

How to load the database locally



Install and start PostgreSQL 17:


brew install postgresql@17

brew services start postgresql@17

     2. Create the database:

/opt/homebrew/opt/postgresql@17/bin/createdb semods

     3. Restore the dump (Replace /path/to/ with the actual path to your downloaded file)

/opt/homebrew/opt/postgresql@17/bin/pg_restore -d semods /path/to/SEMODS_202511.dump

Alternatively, you can use graphical tools such as DBeaver by connecting to a PostgreSQL 17 instance and using the "Restore" option.

Citation

Please cite the associated FORGE 2026 paper when using this dataset:



@article{gonzalez2026semods,  title={SEMODS: A Validated Dataset of Open-Source Software Engineering Models},  author={Gonz{\'a}lez, Alexandra and Franch, Xavier and Mart{\'\i}nez-Fern{\'a}ndez, Silverio},  journal={arXiv preprint arXiv:2601.00635},  year={2026}}