cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30777779
title: <b>Data used in research article: </b><b>Coral tissue depth reconstructed using skeletal microstructural offsets is driven by environmental stress</b>

description: Coral tissue depth reflects organismal health and is influenced by environmental stressors. Reconstructing its past variability on inter- and intra-annual timescales, however, is not yet possible. Here we reconstructed seasonal tissue depth by measuring spatial offsets between growth cycles in corallite porosity and theca geochemistry (Lithium/Magnesium and Barium/Calcium ratios) of a single Siderastrea siderea core collected in Barbados. We show spatial offsets and thus tissue depth vary systematically over multiyear timescales, with decreasing values associated with thermal stress that impact extension rate and calcification in subsequent growth cycles. Large environmental disturbances such as the 2021 volcanic eruption of La Soufrière (St. Vincent) also impact tissue depth, in this case due to the release of bioactive metals upon ash deposition. This study investigates the robustness of the offset signal within a single colony and with further validation across multiple colonies could help reconstruct regional to global environmental and ecological stressors.
DOI: 10.6084/m9.figshare.30777779.v1
title: <b>Data used in research article: </b><b>Coral tissue depth reconstructed using skeletal microstructural offsets is driven by environmental stress</b>

description: Coral tissue depth reflects organismal health and is influenced by environmental stressors. Reconstructing its past variability on inter- and intra-annual timescales, however, is not yet possible. Here we reconstructed seasonal tissue depth by measuring spatial offsets between growth cycles in corallite porosity and theca geochemistry (Lithium/Magnesium and Barium/Calcium ratios) of a single Siderastrea siderea core collected in Barbados. We show spatial offsets and thus tissue depth vary systematically over multiyear timescales, with decreasing values associated with thermal stress that impact extension rate and calcification in subsequent growth cycles. Large environmental disturbances such as the 2021 volcanic eruption of La Soufrière (St. Vincent) also impact tissue depth, in this case due to the release of bioactive metals upon ash deposition. This study investigates the robustness of the offset signal within a single colony and with further validation across multiple colonies could help reconstruct regional to global environmental and ecological stressors.
DOI: 10.15468/dl.5pw7my
title: Occurrence Download

description: A dataset containing 1725 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aequabiliella",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1725 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025844-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15014897
title: UCSB-Library-Research-Data-Services/intro2APIs-examples: v1.0.1

description: Code and data collected from the MET Medieval Art Collection API endpoint for testing and superficial analysis
DOI: 10.5281/zenodo.15014898
title: UCSB-Library-Research-Data-Services/intro2APIs-examples: v1.0.1

description: Code and data collected from the MET Medieval Art Collection API endpoint for testing and superficial analysis
DOI: 10.15468/dl.ez6wgv
title: Occurrence Download

description: A dataset containing 203 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Atelerix",
 "Year 2025-2025"
 ]
}

 The dataset includes 203 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025846-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.82172/utfsm/sg1jrz
title: Formación de Capital Humano Avanzado para la Minería Digital y Sostenible del Futuro

description: El conjunto de datos del estudio abarca información cualitativa y documental recolectada entre los años 2014 y 2023, con el objetivo de identificar los contenidos clave que deben incorporarse en la formación de profesionales de la Ingeniería Civil de Minas, en respuesta a las exigencias de una industria minera cada vez más digitalizada y sostenible. Este conjunto de datos se organizó para analizar la alineación entre los planes de estudio universitarios y las demandas actuales del sector minero.
DOI: 10.82172/utfsm/sg1jrz/qxgrwl
title: 3.2. Scopus, Art 4, Parte 1.tab

description: Resultados del Modelo de búsqueda de artículos científicos mediante uso y aplicación de plataforma Scopus.
DOI: 10.82172/utfsm/sg1jrz/uqysar
title: 3.1. WoS, Art. 4, Parte 1.xls

description: Resultados del Modelo de búsqueda de artículos científicos mediante uso y aplicación de plataforma WoS.
DOI: 10.82172/utfsm/sg1jrz/gdxhit
title: 2. Gráficos 1 y 2.tab

description: Gráficos
DOI: 10.82172/utfsm/sg1jrz/j9padm
title: 1. Repositorio, Figura, Gráficos, Tablas de Datos.docx

description: Información clave vinculada al objeto de estudio
DOI: 10.82172/utfsm/sg1jrz/scgs6t
title: 3.0. Modelo búsqueda artículos científicos.tab

description: Resultados del Modelo de búsqueda de artículos científicos mediante uso y aplicación de plataformas WoS y Scopus
DOI: 10.3886/e212781
title: Code and Data for: Report for 2024 by the AEA Data Editor

description: This repository contains the code, data, and manuscript files for the 
2024 report by the AEA Data Editor. If you are reading this on 
openICPSR, then only code and data are present.<br>
DOI: 10.5281/zenodo.17803888
title: Whole Exome Sequencing Data: Tumor Sample SRR13018652 (Breast Cancer Analysis)

description: EnThis dataset contains complete processed files from whole exome sequencing analysis of a tumor sample (SRR13018652). The analysis was performed as part of a breast cancer case study pipeline.Contents:- Raw and trimmed FASTQ files- Aligned and processed BAM files (sorted, deduplicated, recalibrated)- Variant call files (VCF): unfiltered, filtered, annotated, and gene-specific- Quality control reports (FastQC, fastp)- Intermediate analysis tables (BQSR, contamination, orientation model)- Final clinical report and summary tablesSource Data: The raw sequencing data was downloaded from NCBI GEO dataset GSE161181 (accession SRR13018652).Associated Code: Complete analysis pipeline code is available on GitHub: https://github.com/KypAnastasy/Wes_Tumor_Only_PipelineAnalysis Highlights:- TP53 mutation (8.9% VAF)- BRCA1 mutation (15.1% VAF)- PIK3CA mutation (36.9% VAF)- Male patient identification from sequencing dataThis dataset enables full reproducibility of the bioinformatics pipeline described in the associated GitHub repository.Ru

Этот набор данных содержит полностью обработанные файлы, полученные в результате анализа полного экзомного секвенирования образца опухоли (SRR13018652). Анализ проводился в рамках исследования рака молочной железы.

Содержание:- Необработанные и обрезанные файлы FASTQ- Выровненные и обработанные файлы BAM (отсортированные, дедуплицированные, перекалиброванные)- Файлы распознавания вариантов (VCF): нефильтрованные, фильтрованные, аннотированные и ген-специфичные- Отчёты о контроле качества (FastQC, fastp)- Таблицы промежуточного анализа (BQSR, контаминация, ориентационная модель)- Заключительный клинический отчёт и сводные таблицы

Исходные данные: Необработанные данные секвенирования были загружены из набора данных NCBI GEO GSE161181 (регистрационный номер SRR13018652).

Связанный код: Полный код аналитического конвейера доступен на GitHub: https://github.com/KypAnastasy/Wes_Tumor_Only_Pipeline

Основные моменты анализа:- Мутация TP53 (8,9% VAF)- Мутация BRCA1 (15,1% VAF)- Мутация PIK3CA (36,9% VAF)- Идентификация пациента мужского пола по данным секвенирования

Этот набор данных обеспечивает полную воспроизводимость биоинформатического конвейера, описанного в связанном репозитории GitHub.
DOI: 10.5281/zenodo.17803889
title: Whole Exome Sequencing Data: Tumor Sample SRR13018652 (Breast Cancer Analysis)

description: EnThis dataset contains complete processed files from whole exome sequencing analysis of a tumor sample (SRR13018652). The analysis was performed as part of a breast cancer case study pipeline.Contents:- Raw and trimmed FASTQ files- Aligned and processed BAM files (sorted, deduplicated, recalibrated)- Variant call files (VCF): unfiltered, filtered, annotated, and gene-specific- Quality control reports (FastQC, fastp)- Intermediate analysis tables (BQSR, contamination, orientation model)- Final clinical report and summary tablesSource Data: The raw sequencing data was downloaded from NCBI GEO dataset GSE161181 (accession SRR13018652).Associated Code: Complete analysis pipeline code is available on GitHub: https://github.com/KypAnastasy/Wes_Tumor_Only_PipelineAnalysis Highlights:- TP53 mutation (8.9% VAF)- BRCA1 mutation (15.1% VAF)- PIK3CA mutation (36.9% VAF)- Male patient identification from sequencing dataThis dataset enables full reproducibility of the bioinformatics pipeline described in the associated GitHub repository.Ru

Этот набор данных содержит полностью обработанные файлы, полученные в результате анализа полного экзомного секвенирования образца опухоли (SRR13018652). Анализ проводился в рамках исследования рака молочной железы.

Содержание:- Необработанные и обрезанные файлы FASTQ- Выровненные и обработанные файлы BAM (отсортированные, дедуплицированные, перекалиброванные)- Файлы распознавания вариантов (VCF): нефильтрованные, фильтрованные, аннотированные и ген-специфичные- Отчёты о контроле качества (FastQC, fastp)- Таблицы промежуточного анализа (BQSR, контаминация, ориентационная модель)- Заключительный клинический отчёт и сводные таблицы

Исходные данные: Необработанные данные секвенирования были загружены из набора данных NCBI GEO GSE161181 (регистрационный номер SRR13018652).

Связанный код: Полный код аналитического конвейера доступен на GitHub: https://github.com/KypAnastasy/Wes_Tumor_Only_Pipeline

Основные моменты анализа:- Мутация TP53 (8,9% VAF)- Мутация BRCA1 (15,1% VAF)- Мутация PIK3CA (36,9% VAF)- Идентификация пациента мужского пола по данным секвенирования

Этот набор данных обеспечивает полную воспроизводимость биоинформатического конвейера, описанного в связанном репозитории GitHub.
DOI: 10.3886/e212781v1
title: Code and Data for: Report for 2024 by the AEA Data Editor

description: This repository contains the code, data, and manuscript files for the 
2024 report by the AEA Data Editor. If you are reading this on 
openICPSR, then only code and data are present.<br>
DOI: 10.15468/dl.9unk4p
title: Occurrence Download

description: A dataset containing 27449 species occurrences available in GBIF matching the query:
{
 "and" : [
 "SpeciesKey is Vespa velutina",
 "Year is one of (2024, 2025)",
 "HasCoordinate is true",
 "Geometry POLYGON((4.0 44.0, 12.0 44.0, 12.0 50.0, 4.0 50.0, 4.0 44.0))"
 ]
}

 The dataset includes 27449 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025845-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xy3w4m
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Striatoidium",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025847-251120083545085/datasets/export for details.
DOI: 10.15468/dl.2qrrkt
title: Occurrence Download

description: A dataset containing 98 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "TaxonKey is one of (Plantae [GBIF Backbone Taxonomy])",
 "GadmGid is one of (COL.11_2)",
 "StateProvince is one of (Cauca)",
 "Geometry POLYGON((-76.72578 2.80402,-76.73463 2.81777,-76.76217 2.81875,-76.78184 2.82858,-76.81823 2.85019,-76.82905 2.87475,-76.81135 2.88948,-76.80446 2.91601,-76.80053 2.94744,-76.81528 2.9661,-76.85168 2.97691,-76.87233 2.95039,-76.88709 2.90618,-76.88315 2.8885,-76.89495 2.87475,-76.90282 2.85903,-76.89397 2.84724,-76.91463 2.83251,-76.91659 2.81286,-76.88512 2.80991,-76.87725 2.80009,-76.85758 2.75686,-76.82119 2.73033,-76.79364 2.70872,-76.75725 2.72837,-76.73856 2.71854,-76.70414 2.7323,-76.66971 2.71854,-76.63234 2.72346,-76.60775 2.7097,-76.58807 2.70184,-76.56644 2.70675,-76.56939 2.73033,-76.58807 2.75588,-76.59693 2.7991,-76.60381 2.84724,-76.63037 2.8718,-76.64316 2.87966,-76.67266 2.88359,-76.68545 2.87671,-76.6943 2.84822,-76.70512 2.81973,-76.71496 2.81482,-76.72578 2.80402))"
 ]
 }
 ]
}

 The dataset includes 98 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025849-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5285/f5dce1f7bec2447093cf460a4d3ba2c2
title: ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from SLSTR (2020 - 2022), version 1.0

description: This dataset provides daily Snow Cover Fraction Viewable from above (SCFV) derived from Sentinel-3A&B SLSTR observations, produced within the ESA Climate Change Initiative Snow project.

SCFV expresses the proportion of land area within each about 1 km x 1 km pixel that is covered by snow. SCFV represents snow viewable from above, whether on the forest canopy or on the ground in clear-cut or non-forested areas. The SCFV is given in percentage (%) per pixel. The SCFV product is available at about 1 km pixel size for global land areas except the Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. The SCFV time series spans 01 September 2020 to 31 December 2022. The time series is extended within the Copernicus Climate Change Service (C3S) for Cryosphere from 1 January 2023 onwards.

The SCFV product is based on Sea and Land Surface Temperature Radiometer (SLSTR) data on-board the Sentinel-3A and Sentinel-3B satellites. For the SCFV product generation from SLSTR, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm (SCDA) (Metsämäki et al., 2015). For all remaining pixels, the snow_cci SCFV retrieval method is applied, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach that first identifies pixels which are assessed as snow free, followed by SCFV retrieval for remaining pixels. Permanent snow/ice and water bodies are masked using the Land Cover CCI 2000 dataset, supplemented by a manually mapped salt-lake mask. Per-pixel uncertainty is provided in the ancillary variable as an unbiased Root Mean Square Error (RMSE) for all observed land pixels.

The retrieval approach used for the SLSTR based SCFV CRDP (Climate Research Data Package) v1.0 is the same as the one used for the SCFV CRDP v4.0 from Moderate resolution Imaging Spectroradiometer (MODIS) on board of the Terra satellite, covering the period 2000 – 2023 (https://catalogue.ceda.ac.uk/uuid/bc13bb02a958449aac139853c4638f32/).
The SCFV product is aimed to support cryosphere and climate research applications, including variability and trend analyses, climate modelling and studies in hydrology, meteorology, and ecology. 

ENVEO leads the SCFV product development and product generation from SLSTR data, with contributions on the product development from Syke.
DOI: 10.25545/b8dmpi/uq5myg
title: KnowledgeTranslationTheatreHealthResearch_Search_Goudreau_20251203.rtf

description: Search documentation
DOI: 10.15468/dl.sud5h3
title: Occurrence Download

description: A dataset containing 209 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Anolis compressicauda",
 "Year 2000-2025"
 ]
}

 The dataset includes 209 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025850-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/23enfg
title: Astyanax goyanensis (Miranda-Ribeiro, 1944), new combination and Astyanax courensis, new species (Ostariophysi: Characiformes): two Characidae from the upper rio Tocantins basin, Central Brazil

description: This dataset contains the digitized treatments in Plazi based on the original journal article Bertaco, Vinicius A., Carvalho, Fernando R., Jerep, Fernando C. (2010): Astyanax goyanensis (Miranda-Ribeiro, 1944), new combination and Astyanax courensis, new species (Ostariophysi: Characiformes): two Characidae from the upper rio Tocantins basin, Central Brazil. Neotropical Ichthyology 8 (2): 265-275, DOI: 10.1590/S1679-62252010000200004, URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1679-62252010000200004&lng=en&tlng=enAstyanax goyanensis (Miranda-Ribeiro, 1944), new combination, is redescribed based on the type series and topotypes, and A. courensis, new species, is herein described. Both species were collected in the rio dos Couros basin, upper rio Tocantins basin, Chapada dos Veadeiros, Goiás State, Brazil. These species possess the characters and general body morphology of the species of the Astyanax scabripinnis species complex. The new combination of Astyanax goyanensis is supported by the absence of characters that define Astyanacinus. Astyanax goyanensis and A. courensis differ from their congeners by meristic and morphometric characters, and color pattern. Astyanax goyanensis has only seven pelvic-fin rays, an uncommon character in the species of this genus, and A. courensis possesses only three or four scale rows below the lateral-line, fewer than typical of the genus.Astyanax goyanensis (Miranda-Ribeiro, 1944), nova combinação, é redescrita com base na série-tipo e topótipos, e A. courensis, espécie nova, é descrita. Ambas espécies foram coletadas na bacia do rio dos Couros, bacia do alto rio Tocantins, Chapada dos Veadeiros, Goiás, Brasil. Estes táxons possuem os caracteres e morfologia do corpo das espécies do complexo Astyanax scabripinnis. A transferência e nova combinação de Astyanax goyanensis devem-se a ausência de caracteres que definem Astyanacinus. Astyanax goyanensis e A. courensis diferem das congêneres por caracteres merísticos, morfométricos e padrão de colorido. Astyanax goyanensis possui apenas sete raios na nadadeira pélvica, caráter incomum nas espécies do gênero, e A. courensis possui apenas três ou quatro séries de escamas abaixo da linha lateral, menos do que típico do gênero.
DOI: 10.7303/syn65643335
title: EPESE_smRNA_VKrausLab
DOI: 10.15468/dl.mr4kv3
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mucoharknessia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 11 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025851-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kkub2d
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ailanthus altissima",
 "GadmGid is one of (USA.6_1, USA.17_1, USA.27_1, USA.28_1, USA.32_1, USA.35_1, USA.37_1, USA.42_1, USA.44_1, USA.51_1)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2014",
 "Month is greater than or equal to January",
 "Month is less than or equal to December"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025852-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e240891
title: HIFLD OPEN National Flood Hazard Layer (NFHL) Areas Western US

description: The National Flood Hazard Layer (NFHL) is a compilation of GIS data that comprises a nationwide digital Flood Insurance Rate Map. The GIS data and services are designed to provide the user with the ability to determine the flood zone, base flood elevation, and floodway status for a particular location. It also has information about the NFIP communities, map panels, cross sections, hydraulic structures, Coastal Barrier Resource System, and base maps such as road, stream, and public land survey data. Through flood studies, FEMA produces Flood Insurance Study Reports, FIRM Panels, and FIRM Databases. FIRM Databases that become effective are incorporated into the NFHL. Updates to the NFHL are issued through Letters of Map Revision (LOMRs) and Letters of Map Amendment (LOMAs). Continuously updated, the NFHL serves as a Digital Flood Insurance Rate Map representing the current effective flood data for those communities where maps have been digitized. NFHL data can be viewed with widely available GIS software, including freely available programs that work with GIS shapefiles. For more information on the NFHL, see the online resources referenced herein. Using base maps: The minimum horizontal positional accuracy for base map hydrographic and transportation features used with the NFHL is the NSSDA radial accuracy of 38 feet. Letter of Map Amendment (LOMA) point locations are approximate. The location of the LOMA is referenced in the legal description of the letter itself. LOMA points can be viewed in the NFHL Interactive Map on the FEMA GeoPlatform.<br><br>This dataset consists of areas or polygons of flood hazard areas. Given its size, the data is split into two files that cover the eastern and western halves of the United States, with the Mississippi River as the dividing line.
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025.<br>