cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.23728/b2share.566z6-gtm46
title: LegacyMine_HydroGeo: Dataset on geochemical and hydrological dynamics in a historic mine system

description: This dataset presents a comprehensive approximately two-year continuous monitoring effort of subsurface mine water dynamics in the Reiche Zeche mine, a decommissioned silver mining site in the Ore Mountains of Central Europe. This dataset integrates high-resolution geochemical sampling, hydrological in situ sensing, and UV-Vis spectrometry complemented by machine learning modeling to characterize hotspots and hot moments of metal mobilization across stratified and mixed hydrological regimes. Within 42 sampling campaigns (February 2022 - May 2024), water samples were collected from 26 water flowing and dripping sites, with detailed hydrological and automated spectroscopic monitoring at four distinct locations (sites 1, 2, 3A, and 3B). Dissolved metal(loid) and organic carbon concentrations, and stable water isotopes (δ2H and δ18O) were recorded alongside hourly flow rates and optical absorbance spectra. A cubist-based modeling framework was applied to trace metal concentrations from spectral data, with models validated using autosampler measurements. This curated dataset supports the study of contaminant transport under variable flow conditions in legacy mining environments and is intended to facilitate data reuse for hydrogeochemical modeling, machine learning applications, and environmental monitoring research.
DOI: 10.23728/b2share.f0wbq-n8m97
title: LegacyMine_HydroGeo: Dataset on geochemical and hydrological dynamics in a historic mine system

description: This dataset presents a comprehensive approximately two-year continuous monitoring effort of subsurface mine water dynamics in the Reiche Zeche mine, a decommissioned silver mining site in the Ore Mountains of Central Europe. This dataset integrates high-resolution geochemical sampling, hydrological in situ sensing, and UV-Vis spectrometry complemented by machine learning modeling to characterize hotspots and hot moments of metal mobilization across stratified and mixed hydrological regimes. Within 42 sampling campaigns (February 2022 - May 2024), water samples were collected from 26 water flowing and dripping sites, with detailed hydrological and automated spectroscopic monitoring at four distinct locations (sites 1, 2, 3A, and 3B). Dissolved metal(loid) and organic carbon concentrations, and stable water isotopes (δ2H and δ18O) were recorded alongside hourly flow rates and optical absorbance spectra. A cubist-based modeling framework was applied to trace metal concentrations from spectral data, with models validated using autosampler measurements. This curated dataset supports the study of contaminant transport under variable flow conditions in legacy mining environments and is intended to facilitate data reuse for hydrogeochemical modeling, machine learning applications, and environmental monitoring research.
DOI: 10.5281/zenodo.18509414
title: SuperDARN data in netCDF format (2015-Sep)

description: 2015-Sep 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.6425930
title: SuperDARN data in netCDF format (2015-Sep)

description: 2015-Sep 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.8ggwtn
title: Occurrence Download

description: A dataset containing 6472 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Peucedanum oreoselinum, Peucedanum oreoselinum)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 6472 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017248-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2dhqgh
title: Occurrence Download

description: A dataset containing 9414 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Peucedanum palustre, Peucedanum palustre, Peucedanum palustre, Peucedanum palustre, Selinum palustre, Selinum palustre, Selinum palustre)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 9414 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017249-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.79pnve
title: Occurrence Download

description: A dataset containing 89 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (iNaturalist Research-grade Observations, Austin Peay State University Amphibian and Reptile Specimens (Arctos), MSB Amphibian and Reptile Collection (Arctos), Museum of Comparative Zoology, Harvard University, University of Michigan Museum of Zoology, Division of Reptiles & Amphibians, CUMV Amphibian and Reptile Collection, NCSM Herpetology Collection)",
 "GadmGid is USA.43_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Dryophytes avivoca"
 ]
}

 The dataset includes 89 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017250-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14756492
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.18510070
title: THE USE OF INFORMATION TECHNOLOGIES IN TEACHING QUANTITIES TO PRIMARY SCHOOL STUDENTS

description: This article examines the effectiveness of using information technologies in teaching quantities to primary school students. The study focuses on the pedagogical significance of digital tools, multimedia resources, and interactive technologies in developing students’ understanding of quantities, measurement concepts, and numerical relationships. Modern information technologies enhance learners’ cognitive activity, increase motivation, and support the formation of logical thinking and practical skills. The research analyzes methodological approaches to integrating information technologies into the teaching process and highlights their role in improving the quality and efficiency of mathematics education in primary grades. The findings demonstrate that the purposeful use of information technologies contributes to better learning outcomes and helps students master quantitative concepts more effectively.
DOI: 10.5281/zenodo.18510069
title: THE USE OF INFORMATION TECHNOLOGIES IN TEACHING QUANTITIES TO PRIMARY SCHOOL STUDENTS

description: This article examines the effectiveness of using information technologies in teaching quantities to primary school students. The study focuses on the pedagogical significance of digital tools, multimedia resources, and interactive technologies in developing students’ understanding of quantities, measurement concepts, and numerical relationships. Modern information technologies enhance learners’ cognitive activity, increase motivation, and support the formation of logical thinking and practical skills. The research analyzes methodological approaches to integrating information technologies into the teaching process and highlights their role in improving the quality and efficiency of mathematics education in primary grades. The findings demonstrate that the purposeful use of information technologies contributes to better learning outcomes and helps students master quantitative concepts more effectively.
DOI: 10.34770/24d2-ap69
title: Wiki Surveys: Open and Quantifiable Social Data Collection

description: The wiki survey project seeks to develop new online methods of social data
 collection. This data and code release enables others to replicate and
 extend the results in Salganik and Levy (2015).
DOI: 10.15468/dl.nhk6c5
title: Occurrence Download

description: A dataset containing 1088675 records in GBIF matching the query:

 SELECT gbifid, datasetkey, specieskey, kingdom, phylum, class, "order", family, genus, species, scientificname, decimallatitude, decimallongitude, eventdate, institutioncode, "year" AS obs_year, basisofrecord, coordinateuncertaintyinmeters, issue
 FROM occurrence
 WHERE
 decimallatitude IS NOT NULL
 AND decimallongitude IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND coordinateuncertaintyinmeters IS NOT NULL
 AND coordinateuncertaintyinmeters &gt; 0
 AND coordinateuncertaintyinmeters &lt;= 200.0
 AND decimallatitude &gt;= -28.773895969281842
 AND decimallatitude &lt;= -24.552553756517728
 AND decimallongitude &gt;= 148.1212864664451
 AND decimallongitude &lt;= 156.9770257547842
 AND GBIF_Within('POLYGON ((156.977026 -28.773896, 156.977026 -24.552554, 148.121286 -24.552554, 148.121286 -28.773896, 156.977026 -28.773896))', decimallatitude, decimallongitude) = TRUE
 

 The dataset's 1088675 records were derived from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017244-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25740/hb464cm3040
title: Data from: “Biogeographical Studies of Rocky Shores near Point Conception, California”

description: Upon request from a local researcher, we have transcribed data from a 1985 University of California, Santa Barbara dissertation titled, "Biogeographical Studies of Rocky Shores near Point Conception, California” by Richard Foster Doyle. The subject of Doyle's research was an investigation into the biogeographic boundary between Californian and Oregonian Provinces that was generally understood to be around Point Conception, CA. He stated:  

"The prevailing opinion among biogeographers is that the boundary near Pt. Conception is broad and gradual, extending two to five degrees of latitude. I collected gastropods, algae, barnacles, chitons, and anemones from rocky shores at 40 study sites, located along nearly 400 km of coastline around Pt. Conception. I found 21 species whose ranges reportedly ended in the study area. The range boundaries of these species were highly aggregated near Pt. Conception: 11 fell within 10 km of coast extending east (downcoast) from Pt. Conception. A broad "Transition Zone" between provinces is usually attributed to gradients that do not adequately explain the small size of the distributional break indicated by this study. There is evidence that this narrow transition zone coincides with a convergence of coastal water masses. It is unclear whether littoral species indicate the physiological and biological characteristics of the converging waters, their motions, or both." (p. vi-vii)

Regardless of more recent biogeographic research or any specific interest in this topic, the species distributions found in Doyle's study offer an excellent temporal benchmark for the intertidal species he identified in the early 1980s. Transcribed data include gastropods and algae. Descriptions of barnacles, chitons, and anemones are in the text (not included here). See the ReadMe file for more information.
DOI: 10.15468/dl.chmtjv
title: Occurrence Download

description: A dataset containing 248 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aspidoscelis sackii gigas"
 ]
}

 The dataset includes 248 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017251-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uys97g
title: Occurrence Download

description: A dataset containing 11847 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-80.18878 24.43884,-76.43127 29.21797,-77.98091 31.13924,-73.47405 34.66151,-75.93649 41.77577,-87.99373 41.75643,-94.80481 35.29945,-93.5099 27.11834,-80.18878 24.43884))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Hamamelis virginiana"
 ]
}

 The dataset includes 11847 records from 121 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017254-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c9bu49
title: Occurrence Download

description: A dataset containing 271849 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lythrum salicaria",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null",
 "EventDate is 2016-01-01,2025-12-31"
 ]
}

 The dataset includes 271849 records from 1571 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017222-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0160066.10fu
title: SH0160066.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0160067.10fu
title: SH0160067.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0160068.10fu
title: SH0160068.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0160069.10fu
title: SH0160069.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0160070.10fu
title: SH0160070.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0160071.10fu
title: SH0160071.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0160072.10fu
title: SH0160072.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.