cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.zruvf2
title: Occurrence Download

description: A dataset containing 731043 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is FishBase Database"
 ]
}

 The dataset includes 731043 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014850-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2023-07.2838198/2395339
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840586/2395340
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838284/2395341
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838298/2395342
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838204/2395343
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840957/2395344
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838537/2395345
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838589/2395346
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838444/2395347
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840986/2395348
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840779/2395349
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838301/2395350
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838743/2395351
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838246/2395352
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838543/2395353
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840929/2395354
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840515/2395355
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838415/2395356
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838535/2395357
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840940/2395358
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838179/2395359
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840967/2395360
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838336/2395361
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838397/2395362
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840959/2395363
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838296/2395364
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838475/2395365
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840671/2395366
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838294/2395367
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840966/2395368
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838434/2395369
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838491/2395370
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840958/2395371
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838385/2395372
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838471/2395373
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838396/2395374
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840151/2395375
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840993/2395376
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838588/2395377
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838295/2395378
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840150/2395379
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838303/2395380
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840829/2395381
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838517/2395382
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838469/2395383
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838287/2395384
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838521/2395385
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838302/2395386
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840990/2395387
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840106/2395388
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838258/2395389
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838413/2395390
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840647/2395391
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838235/2395392
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840971/2395393
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838540/2395394
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838399/2395395
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2839405/2395396
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838532/2395397
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838268/2395398
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838514/2395399
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838285/2395400
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838455/2395401
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840316/2395402
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838451/2395403
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840983/2395404
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2840452/2395405
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838493/2395406
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838518/2395407
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838278/2395408
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838292/2395409
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2838192/2395410
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2024-07.3167137/2395411
title: Data for EMSL Project 61049 from July 2024
DOI: 10.15468/dl.wvemge
title: Occurrence Download

description: A dataset containing 232872 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Medicago sativa L.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 232872 records from 2121 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014862-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17900247
title: Vers un modèle d'entrepreneuriat agricole responsable fondé sur l'agro-innovation et la durabilité : une revue théorique et conceptuelle

description: Dans un contexte mondial marqué par des crises écologiques, alimentaires et sociales, l’agriculture doit repenser ses modèles de production et de gouvernance. L’agro-innovation apparaît comme un levier stratégique pour promouvoir un entrepreneuriat agricole durable et inclusif. Cet article, de nature théorique et conceptuelle, mobilise une approche analytique fondée sur une revue documentaire systématique et un raisonnement déductif. L’examen croisé de plusieurs cadres théoriques, capital humain, capital social, approche par les capacités et approche systémique de l’innovation, met en lumière leurs apports, limites et complémentarités. 

Les résultats montrent que le capital humain favorise l’apprentissage et l’adoption des pratiques agro-innovantes, tandis que le capital social, basé sur la coopération et les réseaux, stimule l’innovation collective. L’approche par les capacités renforce l’inclusion et l’autonomisation des acteurs, et la perspective systémique souligne l’importance de la gouvernance territoriale. 

L’article propose enfin un modèle conceptuel intégrateur articulant apprentissage, coopération et gouvernance territoriale.
DOI: 10.5281/zenodo.17900246
title: Vers un modèle d'entrepreneuriat agricole responsable fondé sur l'agro-innovation et la durabilité : une revue théorique et conceptuelle

description: Dans un contexte mondial marqué par des crises écologiques, alimentaires et sociales, l’agriculture doit repenser ses modèles de production et de gouvernance. L’agro-innovation apparaît comme un levier stratégique pour promouvoir un entrepreneuriat agricole durable et inclusif. Cet article, de nature théorique et conceptuelle, mobilise une approche analytique fondée sur une revue documentaire systématique et un raisonnement déductif. L’examen croisé de plusieurs cadres théoriques, capital humain, capital social, approche par les capacités et approche systémique de l’innovation, met en lumière leurs apports, limites et complémentarités. 

Les résultats montrent que le capital humain favorise l’apprentissage et l’adoption des pratiques agro-innovantes, tandis que le capital social, basé sur la coopération et les réseaux, stimule l’innovation collective. L’approche par les capacités renforce l’inclusion et l’autonomisation des acteurs, et la perspective systémique souligne l’importance de la gouvernance territoriale. 

L’article propose enfin un modèle conceptuel intégrateur articulant apprentissage, coopération et gouvernance territoriale.
DOI: 10.6084/m9.figshare.29301095
title: The geography of digital and green (twin) firms in Germany

description: The twin transition, which combines green and digital innovation in economic activities, is increasingly central to policy agendas and is also receiving growing attention in regional research. However, accurately mapping green, digital and twin (both green and digital) economic activities across regions remains challenging, particularly due to data constraints. In this study, we advance this research frontier and present a geographic analysis of digital, green and twin economic activities in Germany, using a web-mined dataset of website texts from 678,381 firms, collected through web scraping in 2023. By processing over 44 million text paragraphs from these websites and applying a cosine similarity filter with green and AI-related terms, we filtered firms that are likely engaged in green, digital and twin activities. Based on this subset, 1437 text paragraphs were manually annotated to fine-tune two transformer models within a SetFit framework, accurately classifying firms as green, digital or both. We aggregate this firm-level data into hexagonal cells to reveal the geographic concentration of the twin transition in Germany. The final map shows a higher number of firms involved in green activities, widely spread across Germany, while AI activities are concentrated in urban centres. We identify 23,819 firms engaged in both green and digital activities, with major hubs like Berlin and Munich leading, and peripheral regions potentially being left behind. Our findings offer critical insights into the geography of the twin transition and highlight the need for policies that address potentially induced spatial inequalities.
DOI: 10.6084/m9.figshare.29301095.v1
title: The geography of digital and green (twin) firms in Germany

description: The twin transition, which combines green and digital innovation in economic activities, is increasingly central to policy agendas and is also receiving growing attention in regional research. However, accurately mapping green, digital and twin (both green and digital) economic activities across regions remains challenging, particularly due to data constraints. In this study, we advance this research frontier and present a geographic analysis of digital, green and twin economic activities in Germany, using a web-mined dataset of website texts from 678,381 firms, collected through web scraping in 2023. By processing over 44 million text paragraphs from these websites and applying a cosine similarity filter with green and AI-related terms, we filtered firms that are likely engaged in green, digital and twin activities. Based on this subset, 1437 text paragraphs were manually annotated to fine-tune two transformer models within a SetFit framework, accurately classifying firms as green, digital or both. We aggregate this firm-level data into hexagonal cells to reveal the geographic concentration of the twin transition in Germany. The final map shows a higher number of firms involved in green activities, widely spread across Germany, while AI activities are concentrated in urban centres. We identify 23,819 firms engaged in both green and digital activities, with major hubs like Berlin and Munich leading, and peripheral regions potentially being left behind. Our findings offer critical insights into the geography of the twin transition and highlight the need for policies that address potentially induced spatial inequalities.
DOI: 10.15468/dl.67uq95
title: Occurrence Download

description: A dataset containing 52 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pabstiella lingua"
 ]
}

 The dataset includes 52 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047266-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bnbrdt
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((110.673 -1.271818, 110.60272 -1.277384, 110.534169 -1.293945, 110.469035 -1.321093, 110.40892 -1.35816, 110.355304 -1.404234, 110.309508 -1.458181, 110.272659 -1.518673, 110.245664 -1.584221, 110.229189 -1.653212, 110.22364 -1.723947, 110.229156 -1.794684, 110.245602 -1.863682, 110.272573 -1.929241, 110.309407 -1.989748, 110.355198 -2.043711, 110.408819 -2.089801, 110.468949 -2.126883, 110.534106 -2.154043, 110.602687 -2.170611, 110.673 -2.176179, 110.743313 -2.170611, 110.811894 -2.154043, 110.877051 -2.126883, 110.937181 -2.089801, 110.990802 -2.043711, 111.036593 -1.989748, 111.073427 -1.929241, 111.100398 -1.863682, 111.116844 -1.794684, 111.12236 -1.723947, 111.116811 -1.653212, 111.100336 -1.584221, 111.073341 -1.518673, 111.036492 -1.458181, 110.990696 -1.404234, 110.93708 -1.35816, 110.876965 -1.321093, 110.811831 -1.293945, 110.74328 -1.277384, 110.673 -1.271818))"},{"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/0047268-251120083545085/datasets/export for details.
DOI: 10.15468/5ayd2m
title: A survey of Collophora Richards (Collembola, Collophoridae), with a new species from Brazil and an identification key for the genus

description: This dataset contains the digitized treatments in Plazi based on the original journal article Medeiros, Gleyce da Silva, dos Santos, Manuela Lima Valério, Nunes Godeiro, Nerivânia, Bellini, Bruno Cavalcante (2025): A survey of Collophora Richards (Collembola, Collophoridae), with a new species from Brazil and an identification key for the genus. Deutsche Entomologische Zeitschrift 72 (2): 557-568, DOI: 10.3897/dez.72.165410AbstractKatiannoidea Bretfeld is a widespread superfamily of Symphypleona that holds taxa with a trochanteral organ on leg III. Collophoridae is a monogeneric family of Katiannoidea composed solely of Collophora Richards, and its species are characterized by having 4 + 4 eyes and a modified capitate bothriotrichum D. The genus has a mostly Holotropical distribution, with species found in the Americas, Africa, and Asia. The overall morphology of Collophora remains poorly understood, as most species descriptions are insufficient by current Symphypleona taxonomic standards. In this study, we describe a new species of Collophora from northeastern Brazil, characterized especially by its purple body, the presence of ciliated chaetae on the fourth antennal segment, a manubrium with 4 + 4 chaetae, and a dens with eight dorsal chaetae, four of which are modified into spines. We also provide a bibliographic survey with the diagnoses of all Collophora species, an identification key, and a brief discussion on the validity of the families within Katiannoidea, which have never been properly tested using modern phylogenetic methods.
DOI: 10.15468/e89bmq
title: A new species of Clematis sect. Tubulosae (Ranunculaceae) from Zhejiang, East China

description: This dataset contains the digitized treatments in Plazi based on the original journal article Xu, Pan, Li, Jun-Ping, Wang, Xian-Ting, Zhang, Fen-Yao, Liang, Wei-Qing, Xie, Wen-Yuan, Yu, Li-Peng, Chen, Feng, Lou, Ke-Lang, Zhong, Jian-Ping, Pu, Jin-Bao, Chen, Zheng-Hai (2025): A new species of Clematis sect. Tubulosae (Ranunculaceae) from Zhejiang, East China. PhytoKeys 267: 93-108, DOI: 10.3897/phytokeys.267.158140AbstractClematis liana sp. nov., a new species of Ranunculaceae from Zhejiang and Anhui Provinces in eastern China, is described and illustrated. This species exhibits similarities in morphological features to C. urticifolia, with which it has a close evolutionary relationship. It is distinguishable by several discrete features, such as habit, reproductive strategy, morphology of cauline ridges, leaflets, bracts, and flowers, calycine color, number of carpels and stamens, length and indumentum of filaments, size of anthers and pollen grains, etc. The complete plastid genome sequence of C. liana sp. nov. comprises 159,759 bp, organized into a quadripartite structure containing two inverted repeat (IR: 31,084 bp each) regions, a small single copy (SSC: 18,135 bp) region, and a large single copy (LSC: 79,456 bp) region. It contains 136 functional, comprising 92 protein-coding, 36 tRNA and 8 rRNA genes.
DOI: 10.15468/dl.u754ku
title: Occurrence Download

description: A dataset containing 621 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Rugulopteryx okamurae)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year is not null",
 "BasisOfRecord is one of (Human Observation, Occurrence evidence)",
 "Year is greater than or equal to 2010",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 621 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047271-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nxgqcd
title: Occurrence Download

description: A dataset containing 70913 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "TaxonKey is Episyrphus balteatus"
 ]
}

 The dataset includes 70913 records from 116 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047169-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e9sy3n
title: Occurrence Download

description: A dataset containing 909 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lathyrus splendens"
 ]
}

 The dataset includes 909 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047267-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17630/6f880713-5801-4a24-9231-745632666394
title: One-dimensional heterocyclic carbene–Au metal–organic frameworks bridging ultra-high vacuum models and scalable liquid-phase growth (dataset)

description: The zip file contains three olders: "XPS_ALOISA", "HREELS", "STM".
The "XPS_ALOISA" folder includes other three sub-folders: "Carbene Berkeley_Au_UHV", "Carbene Berkeley_Au_UHV" and "LOGBOOK". The first two folders contain the .Nrm and .PrX files for XPS measurements. The last folder contains .jpg files with the logbook of the related experiments, helpful to understand the dataset. To open these files the suggest software is Igor.
The "HREELS" folder includes a sub-folder "Berk_Au111", that contains the HREELS data (and the DFT simulations) as .txt files. This folder contains all data related to the HREELS characterization. To open these files the suggest software is Origin.
The "STM" folder includes six sub-folders: "Berk_N_1_15h_A","Berk_N_1_15h_B","Berk_N_1_15h_C","Berk_N_1_15h_D", "Berk_old_2_100uM_15h_200C" and "Berk_UHV". The first five folders contain the data obtained via in air STM. The last folder is related to STM acquisition under UHV conditions. All files are in .STP format. To open this file the suggest software is WSxM.
DOI: 10.5281/zenodo.15830207
title: Attributes and wave functions of different solutions of the nanoparticles calculated in the Hartree-Fock approximation

description: Wave functions, eigenenergies, and additional solution attributes from the Hartree–Fock analysis of the finite jellium model for gold nanoparticles with r_s = 3.01 Bohr.
DOI: 10.5281/zenodo.15830209
title: Attributes and wave functions of different solutions of the nanoparticles calculated in the Hartree-Fock approximation

description: Wave functions, eigenenergies, and additional solution attributes from the Hartree–Fock analysis of the finite jellium model for gold nanoparticles with r_s = 3.01 Bohr.
DOI: 10.15468/dl.wshg4n
title: Occurrence Download

description: A dataset listing the 6 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "GadmGid is IDN.31_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Piperaceae"
 ]
}

 The dataset's 6 records were derived from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047237-251120083545085/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.30861413
title: All parameters required for DiffPharm training and sampling.

description: All parameters required for DiffPharm training and sampling.
DOI: 10.6084/m9.figshare.30861413.v1
title: All parameters required for DiffPharm training and sampling.

description: All parameters required for DiffPharm training and sampling.