cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/sj3-yfl-8kz
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/k6j-1dy-azl
title: †Palorbitolina lenticularis (Blumenbach, 1805)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/z96-br7-jf9
title: Filicea d'Orbigny, 1854

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.5281/zenodo.18250300
title: Monitoreo de lagartijas en Isla Socorro en las temporadas 2012 y 2013

description: Datos que nos enviaron en 2016.

 
description: Esta es la primera versión 0.1.0

Correo de entrega
DOI: 10.5281/zenodo.18221207
title: Misinformation Span Detection - EI22 & BOL4Y

description: BOL4Y & EI22

Description of each data provided in the paper.

 



Contents

The repository includes the following data files:


- dump.csv — Parsed and normalized fact-check metadata extracted from AosFatos HTML pages

- aos_fatos_pages.zip — Raw HTML pages crawled from AosFatos (BOL4Y)

- bol4y.csv.gz — Main BOL4Y dataset (compressed CSV)

- ei22.csv — Main EI22 dataset (CSV)

- bol4y-transcripts.zip — Transcripts associated with BOL4Y

- ei22-transcripts.zip — Transcripts associated with EI22

- Audio data — Distributed as .tar and .zip archives

 


Data

dump.csv

This file contains parsed fact-checking metadata from AosFatos, used to build BOL4Y.Original data is in Brazilian Portuguese; column names were translated to English for usability.

 

 






Column name
Description


title
Title of the fact check


date
Publication date of the fact check


aos_fatos_link
URL of the original AosFatos page


fact_check
Fact-checking paragraph


topic_pt
Topic(s) of the false claim (Portuguese)


source
Origin of the claim (e.g., livestream, speech)


source_urls
URLs to the original content being fact-checked


repetition_count
Number of times the same claim was repeated


year_days_pair
Year, month, and day of each claim occurrence. Includes repeated claims


page
Crawl page index where the fact check was found


fact_check_id
Unique identifier for the fact check (shared with BOL4Y)





BOL4Y (bol4y.csv.gz)

CSV was compressed for easier storage. In it, you will find the following fields:

 






Column name
Description


fact_check_id
Identifier matching dump.csv


file
Source file for the transcript


transcription_source
Transcription system (escriba or whisper)


transcription_index
Segment ID(s) associated with each segment. For some fact-checked segmentes, you'll see more than one id listed in this field, following the concatenation approach discussed in the paper


transcription_text
Claim or transcript segment text


label
1 = misinformation, 0 = non-misinformation


fixed_date
Date of the fact check


unique-check
Unique ID combining file and segment information





 

EI22

Each line of this csv is a segment

 






Column name
Description


file_id
Identifier of the source file


transcript_timestamp_ids
Segment ID(s) associated with each segment. For some fact-checked segmentes, you'll see more than one id listed in this field, following the concatenation approach discussed in the paper


transcript
Full transcript of the file (EI22 only)


minute_timestamp
Minute-level timestamp provided by AosFatos


description
Metadata description from AosFatos


label
1 = misinformation, 0 = non-misinformation


unique-check
Unique ID combining file and segment information


segment_text
Claim or segment text





 



Audio Files


Audio files are distributed as compressed .tar and .zip archives.These files correspond to the source material used to generate transcripts in both datasets.Refer to archive contents and filenames to match audio with transcript files.
DOI: 10.5281/zenodo.18221208
title: Misinformation Span Detection - EI22 & BOL4Y

description: BOL4Y & EI22

Description of each data provided in the paper.

 



Contents

The repository includes the following data files:


- dump.csv — Parsed and normalized fact-check metadata extracted from AosFatos HTML pages

- aos_fatos_pages.zip — Raw HTML pages crawled from AosFatos (BOL4Y)

- bol4y.csv.gz — Main BOL4Y dataset (compressed CSV)

- ei22.csv — Main EI22 dataset (CSV)

- bol4y-transcripts.zip — Transcripts associated with BOL4Y

- ei22-transcripts.zip — Transcripts associated with EI22

- Audio data — Distributed as .tar and .zip archives

 


Data

dump.csv

This file contains parsed fact-checking metadata from AosFatos, used to build BOL4Y.Original data is in Brazilian Portuguese; column names were translated to English for usability.

 

 






Column name
Description


title
Title of the fact check


date
Publication date of the fact check


aos_fatos_link
URL of the original AosFatos page


fact_check
Fact-checking paragraph


topic_pt
Topic(s) of the false claim (Portuguese)


source
Origin of the claim (e.g., livestream, speech)


source_urls
URLs to the original content being fact-checked


repetition_count
Number of times the same claim was repeated


year_days_pair
Year, month, and day of each claim occurrence. Includes repeated claims


page
Crawl page index where the fact check was found


fact_check_id
Unique identifier for the fact check (shared with BOL4Y)





BOL4Y (bol4y.csv.gz)

CSV was compressed for easier storage. In it, you will find the following fields:

 






Column name
Description


fact_check_id
Identifier matching dump.csv


file
Source file for the transcript


transcription_source
Transcription system (escriba or whisper)


transcription_index
Segment ID(s) associated with each segment. For some fact-checked segmentes, you'll see more than one id listed in this field, following the concatenation approach discussed in the paper


transcription_text
Claim or transcript segment text


label
1 = misinformation, 0 = non-misinformation


fixed_date
Date of the fact check


unique-check
Unique ID combining file and segment information





 

EI22

Each line of this csv is a segment

 






Column name
Description


file_id
Identifier of the source file


transcript_timestamp_ids
Segment ID(s) associated with each segment. For some fact-checked segmentes, you'll see more than one id listed in this field, following the concatenation approach discussed in the paper


transcript
Full transcript of the file (EI22 only)


minute_timestamp
Minute-level timestamp provided by AosFatos


description
Metadata description from AosFatos


label
1 = misinformation, 0 = non-misinformation


unique-check
Unique ID combining file and segment information


segment_text
Claim or segment text





 



Audio Files


Audio files are distributed as compressed .tar and .zip archives.These files correspond to the source material used to generate transcripts in both datasets.Refer to archive contents and filenames to match audio with transcript files.
DOI: 10.15468/dl.z3kmxp
title: Occurrence Download

description: A dataset containing 3424 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cupressus arizonica",
 "BasisOfRecord is one of (Human Observation)"
 ]
}

 The dataset includes 3424 records from 102 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012285-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18259866
title: Merseburg, Staatsarchiv (ca. 1947)

description: Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding the history of a German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf. gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope): https://kalliope-verbund.info/DE-611-BF-70963 [Funktion: Im Findbuch anzeigen]Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot be published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch auf Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nicht veröffentlicht werden dürfen).
DOI: 10.5281/zenodo.18259865
title: Merseburg, Staatsarchiv (ca. 1947)

description: Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding the history of a German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf. gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope): https://kalliope-verbund.info/DE-611-BF-70963 [Funktion: Im Findbuch anzeigen]Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot be published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch auf Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nicht veröffentlicht werden dürfen).
DOI: 10.15468/dl.83xj8u
title: Occurrence Download

description: A dataset containing 1161 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((125.81765 34.30736,126.86605 34.30736,126.86605 35.1781,125.81765 35.1781,125.81765 34.30736))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Anas",
 "Year 2020-2026"
 ]
}

 The dataset includes 1161 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012289-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18259953
title: Data from "Ab initio machine-learning simulation of calcium carbonate from aqueous solutions to the solid state"

description: Analysis and input files to reproduce simulations of manuscript "Ab initio machine learning simulation of calcium carbonate from aqueous solutions to the solid state" by Piaggi, Gale, and Raiteri, Proc. Natl. Acad. Sci. 122, e2415663122 (2025) https://doi.org/10.1073/pnas.2415663122
DOI: 10.5281/zenodo.13834649
title: Data from "Ab initio machine-learning simulation of calcium carbonate from aqueous solutions to the solid state"

description: Analysis and input files to reproduce simulations of manuscript "Ab initio machine learning simulation of calcium carbonate from aqueous solutions to the solid state" by Piaggi, Gale, and Raiteri, Proc. Natl. Acad. Sci. 122, e2415663122 (2025) https://doi.org/10.1073/pnas.2415663122
DOI: 10.15468/dl.4vkj9t
title: Occurrence Download

description: A dataset containing 20972 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "BasisOfRecord is Human Observation",
 "TaxonKey is one of (Odocoileus virginianus, Peromyscus leucopus, Peromyscus maniculatus, Ixodes scapularis)",
 "Geometry POLYGON ((-70.77998200260024 47.073845468308974, -82.4440049417868 45.268983141284224, -83.27236307484827 41.91555052968337, -78.76305285986882 42.76217961535124, -74.87245816972946 44.974157001391774, -71.53623351125492 44.94798620574858, -70.44562626500732 45.148881783087035, -69.94333491548616 46.68352262690237, -70.77998200260024 47.073845468308974))"
 ]
}

 The dataset includes 20972 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012288-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rx4dns
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cupressus atlantica",
 "BasisOfRecord is one of (Human Observation)"
 ]
}

 The dataset includes 17 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012290-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cz5t92
title: Occurrence Download

description: A dataset containing 244805 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rutilus rutilus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2010",
 "Year is less than or equal to 2014"
 ]
}

 The dataset includes 244805 records from 220 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012268-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.31073977
title: Spatial Proteomics Dataset

description: This dataset comprises high-plex spatial proteomics data generated to investigate the molecular and cellular basis of alcohol use disorder (AUD) and its frequent comorbidity. The data maps the expression of dozens of proteins across distinct anatomical subregions and cell types within the medial prefrontal cortex (mPFC), a brain area critically involved in both reward processing and emotional regulation.
DOI: 10.6084/m9.figshare.31073977.v1
title: Spatial Proteomics Dataset

description: This dataset comprises high-plex spatial proteomics data generated to investigate the molecular and cellular basis of alcohol use disorder (AUD) and its frequent comorbidity. The data maps the expression of dozens of proteins across distinct anatomical subregions and cell types within the medial prefrontal cortex (mPFC), a brain area critically involved in both reward processing and emotional regulation.
DOI: 10.15468/dl.qea58g
title: Occurrence Download

description: A dataset containing 29552 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Albania",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Animalia, Arthropoda, Insecta, Odonata)"
 ]
}

 The dataset includes 29552 records from 197 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012291-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p13izc92
title: Historical and projected future land change and ecosystem carbon stocks for California

description: This dataset consists of annual raster maps of ecosystem carbon stocks, land use and land cover classes, and transition probabilities for the State of California during historical (1985-2020) and projected future (2021-2100) time periods. Data are simulation model output from the The Land Use and Carbon Simulator (LUCAS; Sleeter et al. 2022) run under different future climate and land management scenarios. LUCAS model simulations were conducted on an annual timestep at 1-km spatial resolution with 40 Monte Carlo realizations per simulation. For the projected future time period, the model was run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios. The climate scenarios were based on downscaled output from four CMIP6 Earth System Models (CESM2-LENS, CNRM-ESM2-1, EC-Earth3, and FGOALS-g3) all run under Shared Socioeconomic Pathway 3 assuming additional radiative forcing of 7 W m-2 by the year 2100 (SSP3-7.0; see Tebaldi et al. 2021). Climate data were downscaled using a hybrid statistical-dynamical downscaling approach (LOCA2-Hybrid) and are freely available to the public from the Cal-Adapt Analytics Engine Data Catalog. The two urbanization scenarios sampled from historical rates of urban development on an annual basis, with one scenario restricting all new urban development to Wildland Urban Interface (WUI) areas while the other urbanization scenario excluded new urban development from WUI areas. The two land management scenarios consisted of a "business as usual" (Low) scenario based on historic rates of tree thinning and prescribed burning, while the second land management scenario (High) implemented forest management treatments to reduce wildfire hazard potential that match area targets from California's Wildfire and Forest Resilience Action Plan (California Forest Management Task Force 2021). Historical land change was based on trends in the National Land Cover Database ((NLCD) and historical climate was based on annual output data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM).
DOI: 10.5281/zenodo.18259789
title: SuperDARN data in netCDF format (1994-Jun)

description: 1994-Jun SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6833142
title: SuperDARN data in netCDF format (1994-Jun)

description: 1994-Jun SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.15468/dl.6gstvc
title: Occurrence Download

description: A dataset containing 1055453 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "StateProvince is Colorado",
 "TaxonKey is Insecta"
 ]
}

 The dataset includes 1055453 records from 377 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012259-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25716/gude.1mj1-4baa
title: Diverging pH dependence and photocycle dynamics across members of the CryoRhodopsin clade

description: A new clade of microbial rhodopsins (MRs) called CryoRhodopsins (CryoRs) was recently discovered and characterized to possess a near-UV-absorbing intermediate populated for minutes at neutral to alkaline conditions. A detailed characterization of one member, CryoR1, showed a strong pH dependence, causing a hindered retinal isomerization and a significant acceleration of the photocycle dynamics at acidic conditions. Here, we present a spectroscopic study on the pH dependence of four other CryoRs (CryoR2-5), revealing strongly deviating photophysical properties within this subfamily under acidic conditions. While all investigated CryoRs possess a minute long photocycle duration at neutral to alkaline conditions, acidification shortens the photocycle to seconds and leads to variation in the observed photocycle schemes. Furthermore, this is not directly reflected in the absorption spectra and initial photoreactions, since CryoR4 and CryoR5 show very weak pH dependence, while CryoR2 and CryoR3 do show pH dependent shifts. In addition, CryoR3 is unique within both CryoRs and MRs in general, since a near-UV-absorbing species is clearly present throughout the whole pH range in the dark spectrum. The observed diversity suggests strong mechanistic divergence within this clade of MRs, a feature that was not observed for other clades of MRs. It also further supports functional relevance of the long-living near-UV-absorbing state of CryoRs under neutral and alkaline conditions.
DOI: 10.5281/zenodo.18259564
title: Machine Learning Pressure Broadening Parameters for Exoplanetary Studies

description: Here is the data and trained model used to produce the multi-perturber machine learning .broad files available on the ExoMol website.  This work is described in full detail in my PhD thesis: "Machine Learning Pressure Broadening Parameters for Exoplanetary Studies", and also in an upcoming paper to follow on from 'Predicting the Rotational Dependence of Line Broadening using Machine Learning'. 

"data_train.csv" is a large csv of compiled broadening data with many active and perturbing species, from many sources, described fully in the paper and thesis.  It is the data used to train the the ML model in "production_model.joblib".  This model can now be used to predict broadening data for unseen active-perturbing species pairs - and has been used to produce the .broad files available on exomol.com.  "production_features.csv" and "production_metadata.json" are what their names suggest.

The code used to produce this data file and ML model will be available on github: https://github.com/erg43/Machine-Leaning-Pressure-Broadening.
DOI: 10.5281/zenodo.18259565
title: Machine Learning Pressure Broadening Parameters for Exoplanetary Studies

description: Here is the data and trained model used to produce the multi-perturber machine learning .broad files available on the ExoMol website.  This work is described in full detail in my PhD thesis: "Machine Learning Pressure Broadening Parameters for Exoplanetary Studies", and also in an upcoming paper to follow on from 'Predicting the Rotational Dependence of Line Broadening using Machine Learning'. 

"data_train.csv" is a large csv of compiled broadening data with many active and perturbing species, from many sources, described fully in the paper and thesis.  It is the data used to train the the ML model in "production_model.joblib".  This model can now be used to predict broadening data for unseen active-perturbing species pairs - and has been used to produce the .broad files available on exomol.com.  "production_features.csv" and "production_metadata.json" are what their names suggest.

The code used to produce this data file and ML model will be available on github: https://github.com/erg43/Machine-Leaning-Pressure-Broadening.
DOI: 10.17632/z87cyxbs4j
title: Exercise-Conditioned Serum Drives Mitochondrial Remodeling in Neurons

description: This dataset includes raw Western blot images with gel loading orders, raw EM images, raw fluorescence imaging data acquired using a Cytation 1 Cell Imaging Multi-Mode Reader (BioTek), and raw confocal images acquired using a CSU-W1 SoRa spinning-disk confocal microscope that support the analyses presented in the associated manuscript.