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

description: A dataset containing 24098 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cardamine heptaphylla (L.) O.E.Schulz",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 24098 records from 131 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0257680-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sn5xeu
title: Occurrence Download

description: A dataset containing 8121 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Parapholis strigosa (Dumort.) C.E.Hubb.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 8121 records from 132 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0297819-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tju8vw
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alchemilla imberbis Juz.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0150890-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sgxmxa
title: Occurrence Download

description: A dataset containing 3140 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Veronica anagalloides Guss.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3140 records from 122 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0257542-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rtfkdh
title: Occurrence Download

description: A dataset containing 88 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Silene fabaria (L.) Coyte",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 88 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0288551-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tfzh4j
title: Occurrence Download

description: A dataset containing 542 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Salvia splendens Sellow ex Nees",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 542 records from 64 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0289555-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sjgeux
title: Occurrence Download

description: A dataset containing 695 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aegilops comosa Sm.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 695 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0150775-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.su8cpa
title: Occurrence Download

description: A dataset containing 8059 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Halimium umbellatum (L.) Spach",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 8059 records from 110 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0262103-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.setwtp
title: Occurrence Download

description: A dataset containing 16246 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Erica vagans L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 16246 records from 177 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0259715-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q3j8h8
title: Occurrence Download

description: A dataset containing 9514 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anchusa azurea Mill.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 9514 records from 205 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0256356-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.th4g4z
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Asperula tournefortii Sieber ex Spreng.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 43 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0288429-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tfjfgj
title: Occurrence Download

description: A dataset containing 157 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dianthus fruticosus L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 157 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0259304-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tm2bqs
title: Occurrence Download

description: A dataset containing 89 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Taraxacum hygrophilum Soest",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 89 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0287678-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sr3wad
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Moehringia villosa (Wulfen) Fenzl",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 7 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0299322-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sp7wzu
title: Occurrence Download

description: A dataset containing 39 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Echinops microcephalus Sibth. & Sm.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 39 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0415870-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t22v2x
title: Occurrence Download

description: A dataset containing 2443 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Carex atherodes Spreng.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2443 records from 116 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0291330-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rt4dzj
title: Occurrence Download

description: A dataset containing 59 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Colchicum trigynum (Steven ex Adams) Stearn",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 59 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0152246-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sh62mr
title: Occurrence Download

description: A dataset containing 810 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Polygala microphylla L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 810 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0293942-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tbzn5a
title: Occurrence Download

description: A dataset containing 3315 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Primula scandinavica Bruun",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3315 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0416464-210914110416597/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.23804517
title: Coordinated Optimization with Integrated Demand Response-Enabled Energy Sharing Techniques Considering Hydrogen Source.

description: Tri-hubs hydrogen based integrated energy system including integrated demand response programming (IDRPs) considering energy sharing and carbon trading.
DOI: 10.15468/dl.rc3ky3
title: Occurrence Download

description: A dataset containing 4074 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Noccaea montana (L.) F.K.Mey.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 4074 records from 89 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0298848-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pve6bf
title: Occurrence Download

description: A dataset containing 76742 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alopecurus myosuroides Huds.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 76742 records from 400 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0287264-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17319941
title: XRD and Crystallographic Data of Electrochemically Textured Silicon (Si) for Porous Surface Formation

description: This dataset provides X-ray diffraction (XRD) data and crystallographic reference parameters for an electrochemically textured silicon (Si) sample. The surface was modified through anodic etching to obtain a porous morphology that enhances light trapping and promotes better lattice matching for subsequent heterostructure formation. The dataset supports investigations into porous silicon as a functional substrate for semiconductor and oxide heterostructures used in photovoltaic and photonic devices.

The dataset includes:



Experimental XRD intensity data (2θ vs. counts) from the etched silicon sample;

Reference stick pattern for cubic silicon (reference code 01-077-2108, ICSD No. 060386)

Structural parameters: a = b = c = 5.4198 Å, α = β = γ = 90°, space group Fd-3m (No. 227), density = 2.34 g cm⁻³, Z = 8


These data are intended to facilitate structural analysis, comparison of porosity-induced strain effects, and the design of Si-based heterostructures in energy conversion and optoelectronic applications.

Funding:Prepared within the project “Design and research of oxide heterostructures for portable solar cells” (Project No. 0124U000223), funded by the National Research Foundation of Ukraine under the Cambridge–NRFU 2022 Individual Grants programme (supported by the University of Cambridge, UK).
DOI: 10.5281/zenodo.17319942
title: XRD and Crystallographic Data of Electrochemically Textured Silicon (Si) for Porous Surface Formation

description: This dataset provides X-ray diffraction (XRD) data and crystallographic reference parameters for an electrochemically textured silicon (Si) sample. The surface was modified through anodic etching to obtain a porous morphology that enhances light trapping and promotes better lattice matching for subsequent heterostructure formation. The dataset supports investigations into porous silicon as a functional substrate for semiconductor and oxide heterostructures used in photovoltaic and photonic devices.

The dataset includes:



Experimental XRD intensity data (2θ vs. counts) from the etched silicon sample;

Reference stick pattern for cubic silicon (reference code 01-077-2108, ICSD No. 060386)

Structural parameters: a = b = c = 5.4198 Å, α = β = γ = 90°, space group Fd-3m (No. 227), density = 2.34 g cm⁻³, Z = 8


These data are intended to facilitate structural analysis, comparison of porosity-induced strain effects, and the design of Si-based heterostructures in energy conversion and optoelectronic applications.

Funding:Prepared within the project “Design and research of oxide heterostructures for portable solar cells” (Project No. 0124U000223), funded by the National Research Foundation of Ukraine under the Cambridge–NRFU 2022 Individual Grants programme (supported by the University of Cambridge, UK).
DOI: 10.6084/m9.figshare.30811091.v2
title: Arithmetic Method Regression (AMR) Real World Data Sources

description: Not Available.
DOI: 10.15468/dl.wd34r8
title: Occurrence Download

description: A dataset containing 132208071 species occurrences available in GBIF matching the query:
{
 "and" : [
 "MediaType is Image",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 132208071 records from 1380 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003099-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pwnmnq
title: Occurrence Download

description: A dataset containing 980 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Taraxacum officinale",
 "HasCoordinate is true",
 "Country is Australia"
 ]
}

 The dataset includes 980 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003165-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18558698
title: When Not to Train: A Methodological Framework for Evaluating Whether AI Models in Health Should Be Built

description: Background: The proliferation of artificial intelligence (AI) models in health has been accompanied by a fundamental but largely unexamined assumption: that the availability of health data and computational capacity is, in itself, sufficient justification for training new models. This assumption has driven an exponential increase in publications reporting AI applications in clinical and public health domains, yet the proportion of these models that demonstrably improve patient outcomes, clinical decision-making, or health system efficiency remains remarkably low [1,2,3]. The field lacks a systematic pre-training evaluation framework that distinguishes between what is technically trainable and what is scientifically and socially necessary.

Objective: This paper proposes a methodological framework for determining when training an AI model in health is scientifically justified and when it is not. We argue that the decision to train should be treated as a consequential resource allocation decision, subject to the same standards of justification applied to any health intervention, rather than as a neutral technical exercise.

Methods: We conducted a conceptual and integrative review of the current AI-in-health literature, examining prevailing training practices, outcome reporting standards, and the gap between predictive performance and clinical impact [4,5]. Drawing on frameworks from health technology assessment, implementation science, and philosophy of science, we synthesized a set of pre-training criteria organized around four dimensions: scientific necessity, clinical relevance, economic justification, and social accountability [10,11,12,20,21,40].

Results: We present the TRAIN-H (Training Rationale Assessment for AI in Health) framework, a structured decision tool comprising seven core questions that must be affirmatively answered before model development is justified. The framework formalizes the principle that prediction without consequence is not impact, and that accuracy without altered conduct, workflow, or cost does not constitute a valid reason to build a model. We identify six explicit conditions under which training should not proceed and discuss implications for research ethics, peer review, funding allocation, and graduate education.

Conclusions: Training AI in health is an investment of computational, human, institutional, and public resources. Like any health intervention, it requires a clear hypothesis, a defined population benefit, and a measurable endpoint. The absence of these elements does not merely weaken a study—it eliminates the justification for the model’s existence. We call for the adoption of pre-training justification standards across research institutions, funding bodies, and editorial boards.
DOI: 10.5281/zenodo.18558697
title: When Not to Train: A Methodological Framework for Evaluating Whether AI Models in Health Should Be Built

description: Background: The proliferation of artificial intelligence (AI) models in health has been accompanied by a fundamental but largely unexamined assumption: that the availability of health data and computational capacity is, in itself, sufficient justification for training new models. This assumption has driven an exponential increase in publications reporting AI applications in clinical and public health domains, yet the proportion of these models that demonstrably improve patient outcomes, clinical decision-making, or health system efficiency remains remarkably low [1,2,3]. The field lacks a systematic pre-training evaluation framework that distinguishes between what is technically trainable and what is scientifically and socially necessary.

Objective: This paper proposes a methodological framework for determining when training an AI model in health is scientifically justified and when it is not. We argue that the decision to train should be treated as a consequential resource allocation decision, subject to the same standards of justification applied to any health intervention, rather than as a neutral technical exercise.

Methods: We conducted a conceptual and integrative review of the current AI-in-health literature, examining prevailing training practices, outcome reporting standards, and the gap between predictive performance and clinical impact [4,5]. Drawing on frameworks from health technology assessment, implementation science, and philosophy of science, we synthesized a set of pre-training criteria organized around four dimensions: scientific necessity, clinical relevance, economic justification, and social accountability [10,11,12,20,21,40].

Results: We present the TRAIN-H (Training Rationale Assessment for AI in Health) framework, a structured decision tool comprising seven core questions that must be affirmatively answered before model development is justified. The framework formalizes the principle that prediction without consequence is not impact, and that accuracy without altered conduct, workflow, or cost does not constitute a valid reason to build a model. We identify six explicit conditions under which training should not proceed and discuss implications for research ethics, peer review, funding allocation, and graduate education.

Conclusions: Training AI in health is an investment of computational, human, institutional, and public resources. Like any health intervention, it requires a clear hypothesis, a defined population benefit, and a measurable endpoint. The absence of these elements does not merely weaken a study—it eliminates the justification for the model’s existence. We call for the adoption of pre-training justification standards across research institutions, funding bodies, and editorial boards.
DOI: 10.15468/dl.4mbba4
title: Occurrence Download

description: A dataset containing 28 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Acourtia simulata",
 "Year 1980-2026"
 ]
}

 The dataset includes 28 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003166-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qmt5xw
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Thysanoptera: Thrips of Guam"
 ]
}

 The dataset includes 31 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003167-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e245011
title: Why railways fail: Colonial railways and economic development in Habsburg Bosnia-Herzegovina

description: Are railways always a harbinger of prosperity? We examine the economic effects of railways in Bosnia-Herzegovina under Habsburg colonial rule. Our novel dataset consistently tracks the non-agricultural population share of over 4500 settlements in Habsburg Bosnia in 1885, 1895, and 1910, based on census records. Applying the inconsequential units approach, with least-cost paths as our instrumental variable, we estimate the effect of railway access on occupational change. In settlements directly connected to imperial railways and competition, non-agricultural activity declined as craftsmen returned to agriculture. By contrast, the new railway network temporarily accelerated non-agricultural activity, primarily by attracting factories and foreign labor. Railway access generated more sustained non-agricultural employment growth in settlements with higher human capital and stronger law enforcement. Overall, our findings suggest that colonial railways did not uniformly promote economic development: while railway access reshaped local occupational structures, lasting positive effects depended on local development preconditions.