cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21233/g2gy-c442
title: Gantt Reservoir diatom surface sample dataset

description: Raw data for the Gantt Reservoir diatom surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/xmax-jw53
title: Ladson Rise [8JE602] vertebrate fauna dataset

description: Raw data for the Ladson Rise [8JE602] vertebrate fauna dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.5281/zenodo.14056014
title: CsSn(Cl/Br/I)3 Perovskite Alloy DFT Dataset for Machine Learning

description: This upload contains density functional theory (DFT) calculations of CsSn(Cl/Br/I)3 perovskite alloy. The calculations were performed for a study, where the DFT data was used to train an energy predicting machine learning model for CsSn(Cl/Br/I)3. The code related to the study is available through GitLab (https://gitlab.com/cest-group/learnsolar-cssnclbri).

The data is divided into four data sets. For each set, the atomic structure data with total energies and forces has been separated into an ASE (Atomic Simulation Environment) extended XYZ file. Additional information on the atomic structures (e.g. space groups) is provided in JSON format. The data sets are:

sp_train_setSingle point DFT calculations of 16 000 algorithmically generated CsSn(Cl/Br/I)3 structures of four different space groups: Pm-3m, P4/mbm, I4/mcm, and Pnma. Lattice parameters and atomic positions are determined through Vegard's law, but random deviations have been added to the atom positions, tilting angles of the Sn coordination octahedra, cell volume, cell height-to-width ratio, and some lattice vector angles. Cl/Br/I configurations are randomized. This data set was used to fit an initial machine learning model. The atomic structures included were selected using a clustering algorithm to accelerate learning.

sp_test_setSingle point DFT calculations of 2 600 atomic structures similar to sp_train_set. The Cl/Br/I compositions are uniformly represented, having two atomic structures per composition and space group. This data was used for testing the machine learning model. 

al_dataDFT relaxation structure snapshots from the active learning run that was performed to improve the machine learning model's structure relaxation accuracy. There are 4230 structure snapshots in total.

relax_test_set100 DFT relaxations used for testing the machine learning relaxation accuracy. There are 2881 structure snapshots in total. Both initial (relax_test_set_initial.xyz) and final (relax_test_set_relaxed.xyz) atomic geometries are included.
DOI: 10.5281/zenodo.14056015
title: CsSn(Cl/Br/I)3 Perovskite Alloy DFT Dataset for Machine Learning

description: This upload contains density functional theory (DFT) calculations of CsSn(Cl/Br/I)3 perovskite alloy. The calculations were performed for a study, where the DFT data was used to train an energy predicting machine learning model for CsSn(Cl/Br/I)3. The code related to the study is available through GitLab (https://gitlab.com/cest-group/learnsolar-cssnclbri).

The data is divided into four data sets. For each set, the atomic structure data with total energies and forces has been separated into an ASE (Atomic Simulation Environment) extended XYZ file. Additional information on the atomic structures (e.g. space groups) is provided in JSON format. The data sets are:

sp_train_setSingle point DFT calculations of 16 000 algorithmically generated CsSn(Cl/Br/I)3 structures of four different space groups: Pm-3m, P4/mbm, I4/mcm, and Pnma. Lattice parameters and atomic positions are determined through Vegard's law, but random deviations have been added to the atom positions, tilting angles of the Sn coordination octahedra, cell volume, cell height-to-width ratio, and some lattice vector angles. Cl/Br/I configurations are randomized. This data set was used to fit an initial machine learning model. The atomic structures included were selected using a clustering algorithm to accelerate learning.

sp_test_setSingle point DFT calculations of 2 600 atomic structures similar to sp_train_set. The Cl/Br/I compositions are uniformly represented, having two atomic structures per composition and space group. This data was used for testing the machine learning model. 

al_dataDFT relaxation structure snapshots from the active learning run that was performed to improve the machine learning model's structure relaxation accuracy. There are 4230 structure snapshots in total.

relax_test_set100 DFT relaxations used for testing the machine learning relaxation accuracy. There are 2881 structure snapshots in total. Both initial (relax_test_set_initial.xyz) and final (relax_test_set_relaxed.xyz) atomic geometries are included.
DOI: 10.5281/zenodo.10614973
title: Datasets and R source code of manuscript "Chronic broadband noise increases the fitness of a laboratory-raised freshwater zooplankton"

description: Experiments : exposition of Daphnia magna to broadband noise or silence along all their life. Measure of survival, clonal reproduction, size and mobility.
DOI: 10.5281/zenodo.14727315
title: Datasets and R source code of manuscript "Chronic broadband noise increases the fitness of a laboratory-raised freshwater zooplankton"

description: Experiments : exposition of Daphnia magna to broadband noise or silence along all their life. Measure of survival, clonal reproduction, size and mobility.
DOI: 10.21233/84ed-sx20
title: NVJR11 pollen surface sample dataset

description: Raw data for the NVJR11 pollen surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/qwgy-4s30
title: Waterhen River ostracode surface sample dataset

description: Raw data for the Waterhen River ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.k6ksg7
title: Occurrence Download

description: A dataset containing 14979 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Petunia Juss."
 ]
}

 The dataset includes 14979 records from 588 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023144-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hkfvxp
title: Occurrence Download

description: A dataset listing the 16 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Papua New Guinea",
 "TaxonKey is Lactarius Pers., 1797"
 ]
}

 The dataset's 16 records were derived from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023137-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/vhh9-hs35
title: LDDsite5511 ostracode surface sample dataset

description: Raw data for the LDDsite5511 ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/2726-6q27
title: Cheng et al. 2020 Site 111 pollen surface sample dataset

description: Raw data for the Cheng et al. 2020 Site 111 pollen surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/pmg2-a420
title: King & Kapp 1963 Site 19 pollen surface sample dataset

description: Raw data for the King & Kapp 1963 Site 19 pollen surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/3r6a-zp02
title: Lake Pleasant diatom surface sample dataset

description: Raw data for the Lake Pleasant diatom surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.5281/zenodo.15373196
title: Words connecting communities. Representing the polysemy of argumentation

description: Final version of the paper to be published on quantiative science studies and original dataset, including also the LDA results and word frequencies by topic.

 

It has been suggested that polysemy in scientific language allows communities to engage in a common research program. However, polysemy might conflict with rules that require words to have a precise meaning. We conceptualize polysemy as a set of relationships between word senses and contexts of usage and we suggest that, to sustain interdisciplinarity, a word should display stable patterns of activation of senses in contexts, and accordingly be both precise and bridging across contexts. We analysed documents in Scopus including the word ‘argumentation’ in title, keywords or abstract. Our analysis displays the layered nature of polysemy, in that there is a set of distinct senses, that also related to specific contexts of usage, such as scientific disciplines, journals and communities; at the same time, we could identify a small set of shared senses that allows bridging between topics. Moreover, we provided evidence that senses are selectively combined within scientific publications and that a significant number of scholars is using the word in different senses in their scientific production. This suggests that the word ‘argumentation’ is able to foster the exchange of concepts and the cooperation of scholars across diverse scientific domains such as philosophy, political sciences and informatics.
DOI: 10.5281/zenodo.15373197
title: Words connecting communities. Representing the polysemy of argumentation

description: Final version of the paper to be published on quantiative science studies and original dataset, including also the LDA results and word frequencies by topic.

 

It has been suggested that polysemy in scientific language allows communities to engage in a common research program. However, polysemy might conflict with rules that require words to have a precise meaning. We conceptualize polysemy as a set of relationships between word senses and contexts of usage and we suggest that, to sustain interdisciplinarity, a word should display stable patterns of activation of senses in contexts, and accordingly be both precise and bridging across contexts. We analysed documents in Scopus including the word ‘argumentation’ in title, keywords or abstract. Our analysis displays the layered nature of polysemy, in that there is a set of distinct senses, that also related to specific contexts of usage, such as scientific disciplines, journals and communities; at the same time, we could identify a small set of shared senses that allows bridging between topics. Moreover, we provided evidence that senses are selectively combined within scientific publications and that a significant number of scholars is using the word in different senses in their scientific production. This suggests that the word ‘argumentation’ is able to foster the exchange of concepts and the cooperation of scholars across diverse scientific domains such as philosophy, political sciences and informatics.
DOI: 10.21233/1m3r-4t18
title: White Lake diatom dataset

description: Raw data for the White Lake diatom dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.75uedn
title: Occurrence Download

description: A dataset containing 687 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Schizocodon soldanelloides Siebold & Zucc.",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2010",
 "HasCoordinate is true"
 ]
}

 The dataset includes 687 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023145-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/6n0h-ra03
title: Whitegrass-Waterhole Creeks Site 9 Reservoir water chemistry dataset

description: Raw data for the Whitegrass-Waterhole Creeks Site 9 Reservoir water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/wt2p-5q61
title: Stoner Lake diatom bottom sample dataset

description: Raw data for the Stoner Lake diatom bottom sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/pvtd-kz35
title: Unnamed Lake diatom bottom sample dataset

description: Raw data for the Unnamed Lake diatom bottom sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/fkz8-5326
title: Witless Bay testate amoebae surface sample dataset

description: Raw data for the Witless Bay testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.9b7tjt
title: Occurrence Download

description: A dataset containing 36859 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Museu Paraense Emilio Goeldi - Araneae Collection"
 ]
}

 The dataset includes 36859 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023147-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w6zyff
title: Occurrence Download

description: A dataset containing 4137 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Periploca laevigata Aiton"
 ]
}

 The dataset includes 4137 records from 132 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023148-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.843q9e
title: Occurrence Download

description: A dataset containing 14979 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Petunia Juss."
 ]
}

 The dataset includes 14979 records from 588 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023146-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/ppbf-1w49
title: Ussurisky Nature Reserve ostracode surface sample dataset

description: Raw data for the Ussurisky Nature Reserve ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/kw2j-kp54
title: Young Lake water chemistry dataset

description: Raw data for the Young Lake water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.25582/data.2024-04.3096730/2562347
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096731/2562348
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096732/2562349
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096733/2562350
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096734/2562351
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096735/2562352
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096736/2562353
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096737/2562354
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096738/2562355
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096739/2562356
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096740/2562357
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096741/2562358
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096742/2562359
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096743/2562360
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096744/2562361
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096745/2562362
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096746/2562363
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096747/2562364
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096748/2562365
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096749/2562366
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096750/2562367
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096751/2562368
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096753/2562369
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096754/2562370
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096755/2562371
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096756/2562372
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096757/2562373
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096758/2562374
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096759/2562375
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096761/2562376
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096762/2562377
title: Data for EMSL Project 51287 from April 2024
DOI: 10.15468/dl.ay56nk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Ambystoma mavortium stebbinsi Lowe, 1954)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002753-250424135625364/datasets/export for details.
DOI: 10.25582/data.2024-04.3096763/2562378
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096767/2562379
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096769/2562380
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096770/2562381
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096771/2562382
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096772/2562383
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096773/2562384
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096774/2562385
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096775/2562386
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096776/2562387
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096777/2562388
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096780/2562389
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096781/2562390
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096782/2562391
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096783/2562392
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096784/2562393
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096787/2562394
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096788/2562395
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096789/2562396
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096790/2562397
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096791/2562398
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096792/2562399
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096794/2562400
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096795/2562401
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096796/2562402
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096797/2562403
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3096798/2562404
title: Data for EMSL Project 51287 from April 2024