cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14164688
title: Wirtschaftszahlen der GJAE-Ausgabe 3/2024 (aktualisierter Stand: 15.11.2024)

description: Wirtschaftszahlen: In dieser Datenbank finden Sie allgemeine Daten zur Entwicklung der landwirtschaftlichen Erzeuger- und Betriebsmittelpreise, des Verbraucherpreisindexes und des Außenhandels mit Agrar- und Ernährungsgütern auf monatlicher Basis für die jeweils vergangenen 24 Monate. Darüber hinaus enthält diese Datenbank wichtige Mengen- und Preisentwicklungen auf nationalen und internationalen Märkten für ausgewählte Produktgruppen landwirtschaftlicher Erzeugnisse wie Getreide, Vieh, Fleisch, Milch und Milchprodukte sowie Gemüse, Obst und Zucker. Diese Datei ist nur in deutscher Sprache verfügbar.
DOI: 10.5281/zenodo.14967000
title: Wirtschaftszahlen der GJAE-Ausgabe 3/2024 (aktualisierter Stand: 15.11.2024)

description: Wirtschaftszahlen: In dieser Datenbank finden Sie allgemeine Daten zur Entwicklung der landwirtschaftlichen Erzeuger- und Betriebsmittelpreise, des Verbraucherpreisindexes und des Außenhandels mit Agrar- und Ernährungsgütern auf monatlicher Basis für die jeweils vergangenen 24 Monate. Darüber hinaus enthält diese Datenbank wichtige Mengen- und Preisentwicklungen auf nationalen und internationalen Märkten für ausgewählte Produktgruppen landwirtschaftlicher Erzeugnisse wie Getreide, Vieh, Fleisch, Milch und Milchprodukte sowie Gemüse, Obst und Zucker. Diese Datei ist nur in deutscher Sprache verfügbar.
DOI: 10.5281/zenodo.14967013
title: Dataset of the report: A.2.2.7 Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for H2, CO, N2 and O2

description: TUBITAK, NPL and CMI will each validate the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for hydrogen, carbon monoxide, oxygen and nitrogen using a gas chromatography thermal conductivity detector (GC-TCD). TUBITAK and CMI will use the static standard produced in A1.1.5 and NPL will use the dynamic system from A1.2.1. A short written report will be produced by TUBITAK, with input from NPL and CMI.
DOI: 10.5281/zenodo.14967014
title: Dataset of the report: A.2.2.7 Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for H2, CO, N2 and O2

description: TUBITAK, NPL and CMI will each validate the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for hydrogen, carbon monoxide, oxygen and nitrogen using a gas chromatography thermal conductivity detector (GC-TCD). TUBITAK and CMI will use the static standard produced in A1.1.5 and NPL will use the dynamic system from A1.2.1. A short written report will be produced by TUBITAK, with input from NPL and CMI.
DOI: 10.15468/dl.bvdt64
title: Occurrence Download

description: A dataset containing 520 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lepus callotis Wagler, 1830"
 ]
}

 The dataset includes 520 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005243-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jnmf96
title: Occurrence Download

description: A dataset containing 18529 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Orthotrichum lyellii Hook. & Taylor"
 ]
}

 The dataset includes 18529 records from 308 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005244-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11207994
title: Wirtschaftszahlen der GJAE-Ausgabe 2/2024 (Stand: 15.05.2024)

description: Wirtschaftszahlen: In dieser Datenbank finden Sie allgemeine Daten zur Entwicklung der landwirtschaftlichen Erzeuger- und Betriebsmittelpreise, des Verbraucherpreisindexes und des Außenhandels mit Agrar- und Ernährungsgütern auf monatlicher Basis für die jeweils vergangenen 24 Monate. Darüber hinaus enthält diese Datenbank wichtige Mengen- und Preisentwicklungen auf nationalen und internationalen Märkten für ausgewählte Produktgruppen landwirtschaftlicher Erzeugnisse wie Getreide, Vieh, Fleisch, Milch und Milchprodukte sowie Gemüse, Obst und Zucker. Diese Datei ist nur in deutscher Sprache verfügbar.
DOI: 10.5281/zenodo.14966967
title: Wirtschaftszahlen der GJAE-Ausgabe 2/2024 (Stand: 15.05.2024)

description: Wirtschaftszahlen: In dieser Datenbank finden Sie allgemeine Daten zur Entwicklung der landwirtschaftlichen Erzeuger- und Betriebsmittelpreise, des Verbraucherpreisindexes und des Außenhandels mit Agrar- und Ernährungsgütern auf monatlicher Basis für die jeweils vergangenen 24 Monate. Darüber hinaus enthält diese Datenbank wichtige Mengen- und Preisentwicklungen auf nationalen und internationalen Märkten für ausgewählte Produktgruppen landwirtschaftlicher Erzeugnisse wie Getreide, Vieh, Fleisch, Milch und Milchprodukte sowie Gemüse, Obst und Zucker. Diese Datei ist nur in deutscher Sprache verfügbar.
DOI: 10.15468/dl.9urmbm
title: Occurrence Download

description: A dataset containing 3516 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pectinatella magnifica (Leidy, 1851)"
 ]
}

 The dataset includes 3516 records from 69 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005245-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zdrbmz
title: Occurrence Download

description: A dataset containing 3516 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pectinatella magnifica (Leidy, 1851)"
 ]
}

 The dataset includes 3516 records from 69 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005237-250227182400228/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.25201391
title: 18S_V9_otu_table.csv

description: Read counts table of V9 18S rDNA representative sequences (OTU table) in csv format.
DOI: 10.6084/m9.figshare.25201391.v1
title: 18S_V9_otu_table.csv

description: Read counts table of V9 18S rDNA representative sequences (OTU table) in csv format.
DOI: 10.6084/m9.figshare.25201394
title: 16S_V4_representative_sequences.fa

description: V4 16S rDNA representative sequences (ASVs) in fasta format from the dimictic freshwater lake Roś, Masurian Lakeland, Poland.
DOI: 10.6084/m9.figshare.25201397
title: 16S_V4_otu_table.csv

description: Read counts table of V4 16S rDNA representative sequences (OTU table) in csv format.
DOI: 10.6084/m9.figshare.25201397.v1
title: 16S_V4_otu_table.csv

description: Read counts table of V4 16S rDNA representative sequences (OTU table) in csv format.
DOI: 10.6084/m9.figshare.25201400
title: 18S_V9_representative_sequences.fa

description: V9 18S rDNA representative sequences (ASVs) in fasta format from the dimictic freshwater lake Roś, Masurian Lakeland, Poland.
DOI: 10.6084/m9.figshare.25162649.v1
title: Readme.txt

description: Readme file of the collection.
DOI: 10.6084/m9.figshare.25162829
title: Data_associated_with_rmarkdown_V9_18S_rDNA.zip

description: R markdown scripts for 18S rDNA part and all necessary data to reproduce figures presented in the paper titled: Spatio-temporal changes of small protist and free-living bacterial communities in a temperate dimictic lake: insights from metabarcoding and machine learning.
DOI: 10.6084/m9.figshare.25162832
title: Supplementary Materials.zip

description: Supplementary materials belonging to Spatio-temporal changes of small protist and free-living bacterial communities in a temperate dimictic lake: insights from metabarcoding and machine learning research article.
DOI: 10.6084/m9.figshare.25162832.v1
title: Supplementary Materials.zip

description: Supplementary materials belonging to Spatio-temporal changes of small protist and free-living bacterial communities in a temperate dimictic lake: insights from metabarcoding and machine learning research article.
DOI: 10.6084/m9.figshare.25162835
title: Qiime2_files.zip

description: QIIME2 files produced during the preprocessing of metabarcoding data.
DOI: 10.6084/m9.figshare.25162835.v1
title: Qiime2_files.zip

description: QIIME2 files produced during the preprocessing of metabarcoding data.
DOI: 10.6084/m9.figshare.25162838
title: Usearch_global_annotation_18S_V9.zip

description: Raw results of the 18S rDNA annotation using usearch global approach and pr2 database.
DOI: 10.6084/m9.figshare.25162649
title: Readme.txt

description: Readme file of the collection.
DOI: 10.6084/m9.figshare.25162802
title: Data_associated_with_rmarkdown_V4_16S_rDNA.zip

description: R markdown scripts for 16S rDNA part and all necessary data to reproduce figures presented in the paper titled: Spatio-temporal changes of small protist and free-living bacterial communities in a temperate dimictic lake: insights from metabarcoding and machine learning.
DOI: 10.6084/m9.figshare.25162838.v1
title: Usearch_global_annotation_18S_V9.zip

description: Raw results of the 18S rDNA annotation using usearch global approach and pr2 database.
DOI: 10.15468/dl.5cvxgy
title: Occurrence Download

description: A dataset containing 10612 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Asclepias incarnata L."
 ]
}

 The dataset includes 10612 records from 255 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005246-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14966120
title: PAN'25 Generative AI Detection (Task 2): Human-AI Collaborative Text Classification

description: Dataset for the Generative AI Detection Task (Subtask 2) @ PAN 2025.

As large language models (LLMs) like GPT-4o, Claude 3.5, and Gemini 1.5-pro become increasingly accessible, machine-generated content is proliferating across diverse domains, including news, social media, education, and academia. These models produce highly fluent and coherent text, making them valuable for automating various writing tasks. However, their widespread use also raises concerns about misinformation, academic integrity, and content authenticity. Identifying the degree of human and machine involvement in text creation is crucial for addressing these challenges.

In this shared task, we focus on Human-AI Collaborative Text Classification, where the goal is to categorize documents that have been co-authored by humans and LLMs. Specifically, we aim to classify texts into six distinct categories based on the nature of human and machine contributions:



Fully human-written: The document is entirely authored by a human without any AI assistance.

Human-initiated, then machine-continued: A human starts writing, and an AI model completes the text.

Human-written, then machine-polished: The text is initially written by a human but later refined or edited by an AI model.

Machine-written, then machine-humanized (obfuscated): An AI generates the text, which is later modified to obscure its machine origin.

Machine-written, then human-edited: The content is generated by an AI but subsequently edited or refined by a human.

Deeply-mixed text: The document contains interwoven sections written by both humans and AI, without a clear separation.


Label Distribution:




Label Category
Train
Dev




Machine-written, then machine-humanized
91,232
10,137


Human-written, then machine-polished
95,398
12,289


Fully human-written
75,270
12,330


Human-initiated, then machine-continued
10,740
37,170


Deeply-mixed text (human + machine parts)
14,910
225


Machine-written, then human-edited
1,368
510


Total
288,918
72,661
DOI: 10.5281/zenodo.14966981
title: PAN'25 Generative AI Detection (Task 2): Human-AI Collaborative Text Classification

description: Dataset for the Generative AI Detection Task (Subtask 2) @ PAN 2025.

As large language models (LLMs) like GPT-4o, Claude 3.5, and Gemini 1.5-pro become increasingly accessible, machine-generated content is proliferating across diverse domains, including news, social media, education, and academia. These models produce highly fluent and coherent text, making them valuable for automating various writing tasks. However, their widespread use also raises concerns about misinformation, academic integrity, and content authenticity. Identifying the degree of human and machine involvement in text creation is crucial for addressing these challenges.

In this shared task, we focus on Human-AI Collaborative Text Classification, where the goal is to categorize documents that have been co-authored by humans and LLMs. Specifically, we aim to classify texts into six distinct categories based on the nature of human and machine contributions:



Fully human-written: The document is entirely authored by a human without any AI assistance.

Human-initiated, then machine-continued: A human starts writing, and an AI model completes the text.

Human-written, then machine-polished: The text is initially written by a human but later refined or edited by an AI model.

Machine-written, then machine-humanized (obfuscated): An AI generates the text, which is later modified to obscure its machine origin.

Machine-written, then human-edited: The content is generated by an AI but subsequently edited or refined by a human.

Deeply-mixed text: The document contains interwoven sections written by both humans and AI, without a clear separation.


Label Distribution:




Label Category
Train
Dev




Machine-written, then machine-humanized
91,232
10,137


Human-written, then machine-polished
95,398
12,289


Fully human-written
75,270
12,330


Human-initiated, then machine-continued
10,740
37,170


Deeply-mixed text (human + machine parts)
14,910
225


Machine-written, then human-edited
1,368
510


Total
288,918
72,661
DOI: 10.5281/zenodo.13326344
title: Wirtschaftszahlen der GJAE-Ausgabe 3/2024 (Stand: 15.08.2024)

description: Wirtschaftszahlen: In dieser Datenbank finden Sie allgemeine Daten zur Entwicklung der landwirtschaftlichen Erzeuger- und Betriebsmittelpreise, des Verbraucherpreisindexes und des Außenhandels mit Agrar- und Ernährungsgütern auf monatlicher Basis für die jeweils vergangenen 24 Monate. Darüber hinaus enthält diese Datenbank wichtige Mengen- und Preisentwicklungen auf nationalen und internationalen Märkten für ausgewählte Produktgruppen landwirtschaftlicher Erzeugnisse wie Getreide, Vieh, Fleisch, Milch und Milchprodukte sowie Gemüse, Obst und Zucker. Diese Datei ist nur in deutscher Sprache verfügbar.
DOI: 10.5281/zenodo.13326345
title: Wirtschaftszahlen der GJAE-Ausgabe 3/2024 (Stand: 15.08.2024)

description: Wirtschaftszahlen: In dieser Datenbank finden Sie allgemeine Daten zur Entwicklung der landwirtschaftlichen Erzeuger- und Betriebsmittelpreise, des Verbraucherpreisindexes und des Außenhandels mit Agrar- und Ernährungsgütern auf monatlicher Basis für die jeweils vergangenen 24 Monate. Darüber hinaus enthält diese Datenbank wichtige Mengen- und Preisentwicklungen auf nationalen und internationalen Märkten für ausgewählte Produktgruppen landwirtschaftlicher Erzeugnisse wie Getreide, Vieh, Fleisch, Milch und Milchprodukte sowie Gemüse, Obst und Zucker. Diese Datei ist nur in deutscher Sprache verfügbar.
DOI: 10.5281/zenodo.14966991
title: Wirtschaftszahlen der GJAE-Ausgabe 3/2024 (Stand: 15.08.2024)

description: Wirtschaftszahlen: In dieser Datenbank finden Sie allgemeine Daten zur Entwicklung der landwirtschaftlichen Erzeuger- und Betriebsmittelpreise, des Verbraucherpreisindexes und des Außenhandels mit Agrar- und Ernährungsgütern auf monatlicher Basis für die jeweils vergangenen 24 Monate. Darüber hinaus enthält diese Datenbank wichtige Mengen- und Preisentwicklungen auf nationalen und internationalen Märkten für ausgewählte Produktgruppen landwirtschaftlicher Erzeugnisse wie Getreide, Vieh, Fleisch, Milch und Milchprodukte sowie Gemüse, Obst und Zucker. Diese Datei ist nur in deutscher Sprache verfügbar.
DOI: 10.5281/zenodo.14966992
title: Dataset of the report: A.2.2.5 Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for sulphur compounds

description: BFKH, CMI and NPL will each validate the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for sulphur compounds (i.e. H2S, COS, CH3SH, C2H5SH, (CH3)2S, (C2H5)2S, CH3SC2H5) using gas chromatography sulphur chemiluminescence detector flame ionization detectors (GC-SCD-FID). BFKH, CMI and NPL will use the gravimetrically prepared static standards produced in A1.1.4. A short-written report will be produced by BFKH, with input from CMI and NPL.
DOI: 10.5281/zenodo.14966993
title: Dataset of the report: A.2.2.5 Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for sulphur compounds

description: BFKH, CMI and NPL will each validate the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for sulphur compounds (i.e. H2S, COS, CH3SH, C2H5SH, (CH3)2S, (C2H5)2S, CH3SC2H5) using gas chromatography sulphur chemiluminescence detector flame ionization detectors (GC-SCD-FID). BFKH, CMI and NPL will use the gravimetrically prepared static standards produced in A1.1.4. A short-written report will be produced by BFKH, with input from CMI and NPL.
DOI: 10.5281/zenodo.14964849
title: Wave parameters at the offshore boundary of Burgas Bay

description: Hourly wave parameters (significant wave height, peak wave period and mean wave direction) at 396 boundary points of Burgas Bay are provided by the Hemholtz Center Hereon in Germany. Historical waves during 1985-2014 and projection waves under SSP245 and SSP585 during 2070-2100 were simulated using high resolution wind data from CNRM (CNRM-CM6-1-HR, 50 km, 3 hourly) as driving forcing.  

The dataset includes the following files as,



BurgasBay_Boundary_Points.txt: locations of bounday points and their file names

CMIP6_HIST.zip: historical wave data (1985-2014)

CMIP6_SSP245.zip: projection wave data (2070 - 2100) under SSP245

CMIP6_SSP585.zip: projection wave data (2070 - 2100) under SSP585
DOI: 10.5281/zenodo.14964850
title: Wave parameters at the offshore boundary of Burgas Bay

description: Hourly wave parameters (significant wave height, peak wave period and mean wave direction) at 396 boundary points of Burgas Bay are provided by the Hemholtz Center Hereon in Germany. Historical waves during 1985-2014 and projection waves under SSP245 and SSP585 during 2070-2100 were simulated using high resolution wind data from CNRM (CNRM-CM6-1-HR, 50 km, 3 hourly) as driving forcing.  

The dataset includes the following files as,



BurgasBay_Boundary_Points.txt: locations of bounday points and their file names

CMIP6_HIST.zip: historical wave data (1985-2014)

CMIP6_SSP245.zip: projection wave data (2070 - 2100) under SSP245

CMIP6_SSP585.zip: projection wave data (2070 - 2100) under SSP585
DOI: 10.13023/2024.alum.133
title: Black Tea Infusion

description: Includes data on the aluminum content of black tea infusion
DOI: 10.15468/dl.vcspyb
title: Occurrence Download

description: A dataset containing 48 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "TaxonKey is Priodontes maximus (Kerr, 1792)"
 ]
}

 The dataset includes 48 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005247-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p1p7c4of
title: Vegetation and soil data from reclaimed oil and gas well pads managed by the Bureau of Land Management, Carlsbad Field Office, New Mexico

description: These data were compiled to support a study of how environmental setting affects the success of well pad reclamation within the region managed by the Bureau of Land Management, Carlsbad Field Office in southeastern New Mexico, USA. The data were collected in 2022 and 2023 and represent vegetation, soil, and climate conditions at 70 reclaimed well pads from 4 to 24 years after the wells were plugged and reclaimed. The data were collected by the U.S. Geological Survey, Southwest Biological Science Center, Moab, UT, Research Station, using field observations and some remotely sensed or mapped products (gridded climate and soil property maps). The data can be used to represent the environmental condition of the well pads at the time of collection, the elapsed time between when wells were plugged (a proxy for the start of the reclamation process) and when data were collected, and the climate at each well pad as represented by the global aridity index. For some data, samples were taken both on well pads and adjacent to them in relatively undisturbed areas and so may be used to compare the condition of pads with adjacent reference conditions.
DOI: 10.34740/kaggle/dsv/10921634
title: Heart Attack Risk Dataset of China
DOI: 10.25318/33100935-eng
title: Business or organization plans to apply for debt financing, first quarter of 2025

description: Business or organization plans to apply for debt financing, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, first quarter of 2025.
DOI: 10.5281/zenodo.13881893
title: Post-treatment for 3D printed moulds

description: The replication of a mould into a PDMS (silicone) replicate, called soft-lithography, is a usual step in micro-nanofabrication. However, when a 3D printed mould is used (produced from stereolithography), the resin blocks the curing of the PDMS. This inhibition can be eliminate by treating the moulds with various steps (UV post-curing, baking, immersion in solvents...). 

In this database, the post-treatments described in the litterature are gathered and can be sorted by different entries: 



method

author

printer model

resin
DOI: 10.5281/zenodo.13881894
title: Post-treatment for 3D printed moulds

description: The replication of a mould into a PDMS (silicone) replicate, called soft-lithography, is a usual step in micro-nanofabrication. However, when a 3D printed mould is used (produced from stereolithography), the resin blocks the curing of the PDMS. This inhibition can be eliminate by treating the moulds with various steps (UV post-curing, baking, immersion in solvents...). 

In this database, the post-treatments described in the litterature are gathered and can be sorted by different entries: 



method

author

printer model

resin
DOI: 10.5281/zenodo.14966960
title: Post-treatment for 3D printed moulds

description: The replication of a mould into a PDMS (silicone) replicate, called soft-lithography, is a usual step in micro-nanofabrication. However, when a 3D printed mould is used (produced from stereolithography), the resin blocks the curing of the PDMS. This inhibition can be eliminate by treating the moulds with various steps (UV post-curing, baking, immersion in solvents...). 

In this database, the post-treatments described in the litterature are gathered and can be sorted by different entries: 



method

author

printer model

resin