cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28107050
title: Replication data for Early Slavic When-clauses in cross-linguistic perspective

description: This repository contains the data associated with the article <i>Early Slavic When-clauses in cross-linguistic perspective</i>, currently under review.The repository also includes the semantic maps for all languages in the parallel dataset (including those not shown in this paper), the Python scripts to reproduce the pipeline in Section \ref{firstsec} (from generating a Hamming distance matrix to applying Ordinary Kriging), and a \texttt{README.md} describing the repository contents, instructions for running the scripts, and the New Testament translation version used for each language.The <code>README.md</code> file (COMING SOON!) contains much of the same content found below, and it is included mainly for exportability, but it also contains more information about the New Testament versions used for each language in the dataset.The following data can be found under <code>/datasets</code>:<code>df_when_no_ngrams.csv</code>: this is the raw aligned data, containing one context per row in which the word <i>when</i> occurs in the English Standard Version of the New Testament, and one column per language in the dataset, with the automatically aligned parallel in the respective row. <code>NOMATCH</code> indicates that the alignment suggests no parallel word could be found. Empty values indicate that the relevant language does not have a translation for the respective Bible verse.<code>df_when_ngrams.csv</code>: this is the parallel data <i>after</i> applying the <code>n</code>-gram search method described in the article. N-gram groups (i.e., possible allomorphs of a morpheme potentially meaning 'when') are labelled as <code>ngram_1</code>, <code>ngram_2</code>, <code>ngram_3</code>, etc. To know what each group correspond to, check the following file (<code>ngram_details.txt</code>). Post-corrected and functionally-labelled data (used in the paper to generate semantic maps), alongside the original one, can also be found in this .csv. Column names with <code>-corrected</code> appended to the language ISO code means that the values were checked, either manually or semi-automatically.<code>ngram_details.txt</code>: breakdown of n-gram groups automatically found for each language in the dataset and what character n-grams they actually refer to.<code>glottolog_withfams.csv</code>: a table based on Glottolog's classification containing a mapping between ISO language codes and language name, language family, and world region in which the language is primarily used. This dataset was used to automatically generate the title of the semantic maps in the paper.The following can be found under <code>scripts/</code>:[CONTENT COMING SOON!]The folder <code>maps/</code> contains all the semantic maps generated based on the data in <code>df_when_ngrams.csv</code>.
DOI: 10.6084/m9.figshare.28107050.v1
title: Replication data for Early Slavic When-clauses in cross-linguistic perspective

description: This repository contains the data associated with the article <i>Early Slavic When-clauses in cross-linguistic perspective</i>, currently under review.The repository also includes the semantic maps for all languages in the parallel dataset (including those not shown in this paper), the Python scripts to reproduce the pipeline in Section \ref{firstsec} (from generating a Hamming distance matrix to applying Ordinary Kriging), and a \texttt{README.md} describing the repository contents, instructions for running the scripts, and the New Testament translation version used for each language.The <code>README.md</code> file (COMING SOON!) contains much of the same content found below, and it is included mainly for exportability, but it also contains more information about the New Testament versions used for each language in the dataset.The following data can be found under <code>/datasets</code>:<code>df_when_no_ngrams.csv</code>: this is the raw aligned data, containing one context per row in which the word <i>when</i> occurs in the English Standard Version of the New Testament, and one column per language in the dataset, with the automatically aligned parallel in the respective row. <code>NOMATCH</code> indicates that the alignment suggests no parallel word could be found. Empty values indicate that the relevant language does not have a translation for the respective Bible verse.<code>df_when_ngrams.csv</code>: this is the parallel data <i>after</i> applying the <code>n</code>-gram search method described in the article. N-gram groups (i.e., possible allomorphs of a morpheme potentially meaning 'when') are labelled as <code>ngram_1</code>, <code>ngram_2</code>, <code>ngram_3</code>, etc. To know what each group correspond to, check the following file (<code>ngram_details.txt</code>). Post-corrected and functionally-labelled data (used in the paper to generate semantic maps), alongside the original one, can also be found in this .csv. Column names with <code>-corrected</code> appended to the language ISO code means that the values were checked, either manually or semi-automatically.<code>ngram_details.txt</code>: breakdown of n-gram groups automatically found for each language in the dataset and what character n-grams they actually refer to.<code>glottolog_withfams.csv</code>: a table based on Glottolog's classification containing a mapping between ISO language codes and language name, language family, and world region in which the language is primarily used. This dataset was used to automatically generate the title of the semantic maps in the paper.The following can be found under <code>scripts/</code>:[CONTENT COMING SOON!]The folder <code>maps/</code> contains all the semantic maps generated based on the data in <code>df_when_ngrams.csv</code>.
DOI: 10.17026/ar/oma2rb
title: Bureauonderzoek Archeologie Landgoed de Wiersse
title: Landgoederenzone Baakse Beek

description: &lt;p&gt;In opdracht van Waterschap Rijn en IJssel heeft Arcadis Nederland B.V. een archeologisch bureauonderzoek uitgevoerd voor Landgoed de Wiersse. Dit landgoed valt binnen de landgoederenzone Baakse Beek, een gebied in de provincie Gelderland nabij Ruurlo, Vorden en Lochem. Het Waterschap Rijn en IJssel wil binnen deze landgoederenzone het watersysteem van de Baakse Beek klimaatbestendig maken.&lt;/p&gt;&lt;p&gt;

De toekomstige ontwikkelingen op het landgoed de Wiersse hebben als doel om het watersysteem beter bestand te maken tegen droogte en natheid. Bij ontwikkelingen in het gebied kunnen mogelijk bekende en verwachte archeologische resten verstoord worden. Dit bureauonderzoek heeft als doel inzicht te geven in de archeologische verwachtingen en bekende archeologische waarden binnen het landgoed. Op basis van deze gegevens wordt vervolgens een gespecificeerde archeologische verwachting opgesteld en een advies voor eventueel vervolgonderzoek geformuleerd.&lt;/p&gt;
DOI: 10.5281/zenodo.18506648
title: MJ Precipitation Reconstruction for 63.0°N, 18.5°E

description: MJ Precipitation Reconstruction 63.0°N, 18.5°EThis dataset contains a reconstruction of May–June (MJ) precipitation for the location 63.0°N, 18.5°E, Sweden, derived from tree ring width measurements of Pinus sylvestris, along with observed precipitation data from Härnösand.
DOI: 10.5281/zenodo.18506649
title: MJ Precipitation Reconstruction for 63.0°N, 18.5°E

description: MJ Precipitation Reconstruction 63.0°N, 18.5°EThis dataset contains a reconstruction of May–June (MJ) precipitation for the location 63.0°N, 18.5°E, Sweden, derived from tree ring width measurements of Pinus sylvestris, along with observed precipitation data from Härnösand.
DOI: 10.5281/zenodo.18506523
title: A deep perceptual metric for 3D point clouds: ICIP2020 Perry Dataset Repack

description: This dataset supplements the publication:

Maurice Quach, Aladine Chetouani, Giuseppe Valenzise, Frederic Dufaux, "A deep perceptual metric for 3D point clouds"  in Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XVIII,  2021,  pp 257-1 - 257-7,  https://doi.org/10.2352/ISSN.2470-1173.2021.9.IQSP-257.

It is a repack of the dataset provided in:

S. Perry et al., “Quality Evaluation Of Static Point Clouds Encoded Using MPEG Codecs,” in 2020 IEEE Int. Conf. on Image Process. (ICIP), Oct. 2020, pp. 3428–3432.
DOI: 10.5281/zenodo.18506522
title: A deep perceptual metric for 3D point clouds: ICIP2020 Perry Dataset Repack

description: This dataset supplements the publication:

Maurice Quach, Aladine Chetouani, Giuseppe Valenzise, Frederic Dufaux, "A deep perceptual metric for 3D point clouds"  in Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XVIII,  2021,  pp 257-1 - 257-7,  https://doi.org/10.2352/ISSN.2470-1173.2021.9.IQSP-257.

It is a repack of the dataset provided in:

S. Perry et al., “Quality Evaluation Of Static Point Clouds Encoded Using MPEG Codecs,” in 2020 IEEE Int. Conf. on Image Process. (ICIP), Oct. 2020, pp. 3428–3432.
DOI: 10.5285/bf48a9a3-7370-4114-9c40-60af57a0c8bc
title: Freshwater pond quality data 2007 [Countryside Survey]

description: This dataset consists of freshwater pond quality data for sites across Great Britain in 2007. Data include macrophyte species records, chemistry and water quality, and environmental variables such as pollution, grazing and management, from ponds surveyed within a set of 591 1km squares across Great Britain (note - not all squares contained ponds). The survey was part of Countryside Survey, a unique study or 'audit' of the natural resources of the UK's countryside, and was carried out by the Centre for Ecology & Hydrology and Pond Conservation. The sample sites are chosen from a stratified random sample, based on a 15 by 15 km grid of GB. The countryside is sampled and surveyed using rigorous scientific methods, allowing us to compare new results with those from previous surveys. In this way we can detect the gradual and subtle changes that occur in the UK's countryside over time. In addition to freshwater habitat data, habitat areas, vegetation species data, soil data and linear habitat data are also gathered by Countryside Survey.
description: The data were recorded on paper in the summer of 2007 by professional surveyors and transferred to digital files by Pond Conservation staff. Chemical analyses were undertaken by UKAS accredited laboratories at CEH Lancaster. Data were recorded from 591 1km squares sited across Britain, selected by random stratified sample.
DOI: 10.48472/deposita/r7zaxt
title: Aderência dos datasets dos repositórios de São Paulo ao FAIR

description: Dados tabulares coletados entre o período de janeiro a março de 2022 com o uso da ferramenta F-UJI (FAIRsFAIR Research Data Object Assessment Service) para avaliar os níveis de aderência ao FAIR dos 264 conjuntos de dados (datasets) depositados nos repositórios de dados de pesquisa de São Paulo indexados no metabuscador da FAPESP.
DOI: 10.15468/dl.jm7hrx
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (6ea87510-0561-11d8-b851-b8a03c50a862, f9b67ad0-9c9b-11d9-b9db-b8a03c50a862, c2ecae57-6701-485e-9ebd-5f87b2945cb2)",
 "TaxonKey is Melanoplus withlacoocheensis",
 "Year 1883-2026"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016567-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18501791
title: Dataset and code for "Self-motion as a structural prior for coherent and robust formation of cognitive maps"
DOI: 10.5281/zenodo.11092908
title: Amazonian manatee critical habitat revealed by artificial intelligence-based passive acoustic techniques

description: These files are part of the publication under the same name. The pkl file contains manatee vocalizations mixed with background sounds that have been used to train a CNN. The resulting model is available in the .tflite file. The Python file can browse the vocalizations that have been made available, visualize the human labels, and if Tensorflow is available classify the samples, providing the classifier output for the manatee class. The corresponding paper should be referred to for thresholds and performance details that were used for field data.
DOI: 10.5281/zenodo.11092907
title: Amazonian manatee critical habitat revealed by artificial intelligence-based passive acoustic techniques

description: These files are part of the publication under the same name. The pkl file contains manatee vocalizations mixed with background sounds that have been used to train a CNN. The resulting model is available in the .tflite file. The Python file can browse the vocalizations that have been made available, visualize the human labels, and if Tensorflow is available classify the samples, providing the classifier output for the manatee class. The corresponding paper should be referred to for thresholds and performance details that were used for field data.
DOI: 10.5281/zenodo.18501790
title: Dataset and code for "Self-motion as a structural prior for coherent and robust formation of cognitive maps"
DOI: 10.25345/c54f1mz0m
title: MassIVE MSV000100747 - Enzymatic and proteic changes in resistant and susceptible cacao clones revela distinct response mechanisms to Phytophthora citrophthora infection
title: This study employed enzymatic analyses and gel-free comparative proteomics to elucidate contrasting defense strategies in cacao cultivars differing in resistance to black pod rot disease. Enzymatic assays indicated that 24 h after inoculation represented the time point with the strongest contrasts in antioxidant responses and was therefore selected for proteomic analysis. The resistant clone displayed extensive proteomic reprogramming, including proteins involved in redox homeostasis, carbohydrate metabolism, photosynthetic adjustment, and stress signaling, reflecting coordinated activation of defense mechanisms. In contrast, the susceptible clone showed limited proteome plasticity, suggesting impaired system-level responses and enhanced cellular vulnerability to infection.
DOI: 10.5285/0cb3ef9d-944d-4169-8bce-bfb53d3a7a63
title: Land Cover Map 2007 (1km percentage target class, GB)

description: Land Cover Map 2007 (LCM2007) is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB) 1990 and LCM2000. Like the earlier 1990 and 2000 products, LCM2007 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors. It also covers Northern Ireland and incorporates information derived from other ancillary datasets. LCM2007 was classified using a hierarchical nomenclature corresponding to Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible, incorporating land cover classes sought by other users. LCM2007 is produced in both vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. Dataset consists of 1km percentages per Target Class. Great Britain only.
description: LCM2007 uses a spatial framework based on OS MasterMap (R). MasterMap was generalised to remove unnecessary detail, then the framework was segmented according to the underlying satellite data to split areas of non-uniform landscape. The data was classified according to a parcel-based supervised maximum likelihood classification procedure. The raster products are derived from the vector products.
DOI: 10.5285/ef1cbd8a-9c8e-4ddc-90e6-360b1c7cae50
title: Land Cover Map 2007 (1km raster dominant aggregate class, N. Ireland)

description: Land Cover Map 2007 (LCM2007) is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB) 1990 and LCM2000. Like the earlier 1990 and 2000 products, LCM2007 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors. It also covers Northern Ireland and incorporates information derived from other ancillary datasets. LCM2007 was classified using a nomenclature corresponding to Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible, incorporating land cover classes sought by other users. LCM2007 is produced in both vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. This dataset consists of 1km raster, dominant aggregate class (aggregation of Target Classes). Northern Ireland only.
description: LCM2007 uses a spatial framework based on OSNI Vector Map. Vector Map was generalised to remove unnecessary detail, then the framework was segmented according to the underlying satellite data to split areas of non-uniform landscape. The data was classified according to a parcel-based supervised maximum likelihood classification procedure. The raster products are derived from the vector products.
DOI: 10.5285/a382af78-129e-4326-a561-d3034b72c4a3
title: Land Cover Map 2007 (25m raster, GB)

description: Land Cover Map 2007 (LCM2007) is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB) 1990 and LCM2000. Like the earlier 1990 and 2000 products, LCM2007 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors. It also covers Northern Ireland and incorporates information derived from other ancillary datasets. LCM2007 was classified using a nomenclature corresponding to the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible, incorporating land cover classes sought by other users. LCM2007 is produced in both vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. This dataset consists of the 25m raster version for Great Britain only.
description: LCM2007 uses a spatial framework based on OS MasterMap (R). MasterMap was generalised to remove unnecessary detail, then the framework was segmented according to the underlying satellite data to split areas of non-uniform landscape. The data was classified according to a parcel-based supervised maximum likelihood classification procedure. The raster products are derived from the vector products.
DOI: 10.5285/337f9dea-726e-40c7-9f9b-e269911c9db6
title: Land Cover Map 2007 (1km raster dominant target class, GB)

description: Land Cover Map 2007 (LCM2007) is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB) 1990 and LCM2000. Like the earlier 1990 and 2000 products, LCM2007 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors. It also covers Northern Ireland and incorporates information derived from other ancillary datasets. LCM2007 was classified using a nomenclature corresponding to Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible , incorporating land cover classes sought by other users. LCM2007 is produced in both vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. Dataset consists of the dominant Target Class per 1km square, raster dataset (Great Britain only).
description: LCM2007 uses a spatial framework based on OS MasterMap (R). MasterMap was generalised to remove unnecessary detail, then the framework was segmented according to the underlying satellite data to split areas of non-uniform landscape. The data was classified according to a parcel-based supervised maximum likelihood classification procedure. The raster products are derived from the vector products.
DOI: 10.5285/82a0f4a1-01ff-4ed1-853e-224d8404b3fd
title: Land Cover Map 2007 (1km dominant aggregate class, GB)

description: Land Cover Map 2007 (LCM2007) is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB) 1990 and LCM2000. Like the earlier 1990 and 2000 products, LCM2007 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors. It also covers Northern Ireland and incorporates information derived from other ancillary datasets. LCM2007 was classified using a nomenclature corresponding to Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible , incorporating land cover classes sought by other users. LCM2007 is produced in both vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. Product consists of 1km raster, dominant aggregate class (aggregation of Target Classes). Great Britain only.
description: LCM2007 uses a spatial framework based on OS MasterMap (R). MasterMap was generalised to remove unnecessary detail, then the framework was segmented according to the underlying satellite data to split areas of non-uniform landscape. The data was classified according to a parcel-based supervised maximum likelihood classification procedure. The raster products are derived from the vector products.
DOI: 10.5281/zenodo.18506966
title: Adaptive Spatiotemporal Anomaly Detection for Flexible Thermal Sensor Arrays in Wearable Breast Cancer Monitoring

description: Dataset
DOI: 10.5281/zenodo.18506967
title: Adaptive Spatiotemporal Anomaly Detection for Flexible Thermal Sensor Arrays in Wearable Breast Cancer Monitoring

description: Dataset
DOI: 10.17026/ar/4xtyi0
title: Bureauonderzoek Archeologie AZC Velsen
title: AER 512

description: In opdracht van het Centraal Orgaan opvang Asielzoekers (COA) heeft Arcadis Nederland B.V. een archeologisch bureauonderzoek uitgevoerd voor plangebied AZC Velsen in Santpoort-Zuid (gemeente Velsen). Het COA is voornemens hier een nieuw AZC te ontwikkelen. Bij de uitvoering van de voorgenomen ontwikkelingen kunnen mogelijk archeologische waarden worden verstoord. Het bureauonderzoek heeft als doel inzicht te verschaffen in de archeologische waarden die zich in het plangebied kunnen bevinden.
DOI: 10.15468/dl.478fdp
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Australia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ornithorhynchus anatinus",
 "Year 2026-2026"
 ]
}

 The dataset includes 14 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016568-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.