cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20427786.v2
title: Dataset "Do microplastics and climate change negatively affect shredder invertebrates from an Amazon stream? An ecosystem functioning perspective"

description: Database refers to leaf decomposition and survival of Amazon stream shredders. Able to be updated later.
DOI: 10.6084/m9.figshare.20427786.v1
title: Do microplastics and climate change negatively affect shredder invertebrates from an Amazon stream? An ecosystem functioning perspective

description: Database refers to leaf decomposition and survival of Amazon stream shredders. Able to be updated later.
DOI: 10.6084/m9.figshare.20440509
title: ARRIVE Checklist MSCs therapy

description: ARRIVE Checklist MSCs therapy
DOI: 10.5281/zenodo.6967309
title: Potential and realized distribution at 30m for pedunculate oak (Quercus robur) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the pedunculate oak (<em>Quercus robur, L.</em>) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: 2000 - 2002, 2002 - 2006, 2006 - 2010, 2010 - 2014, 2014 - 2018, 2018 - 2020. Files are named according to the following naming convention, e.g: veg_quercus.robur_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3 with the following fields: theme: e.g. <strong>veg</strong>, species code: e.g. <strong>quercus.robur</strong>, species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation), species estimation method: e.g. <strong>eml</strong>, species estimation type: e.g. <strong>md</strong> ( = model deviation), resolution in meters e.g. <strong>30m</strong>, reference depths (vertical dimension): e.g. <strong>0..0cm</strong>, reference period begin end: e.g. <strong>2000..2002</strong>, reference area: e.g. <strong>eumap</strong>, coordinate system: e.g. <strong>epsg3035</strong>, data set version: e.g. <strong>v0.3</strong>. For each species is then easy to identify probability and uncertainty distribution maps: veg_quercus.robur_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_quercus.robur_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_quercus.robur_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_quercus.robur_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution Files are provided as Cloud Optimized GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format. If you would like to know more about the creation of the maps and the modeling: <strong>watch</strong> the talk at Open Data Science Workshop 2021 (TIB AV-PORTAL) <strong>access </strong>the repository with our R/Python scripts and follow the instructions (GitLab) <strong>access </strong>the repository with the training dataset (Zenodo) <strong>read </strong>the tutorial with executable code on our GitBook A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on PeerJ. To suggest any improvement/fix use https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues
description: This work is co-financed under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 by the European Union (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5281/zenodo.5887415
title: Potential and realized distribution at 30m for pedunculate oak (Quercus robur) in Europe for 2000 - 2020

description: Probability and uncertainty maps showing the potential and realized distribution for the pedunculate oak (<em>Quercus robur, L.</em>) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: 2000 - 2002, 2002 - 2006, 2006 - 2010, 2010 - 2014, 2014 - 2018, 2018 - 2020. Files are named according to the following naming convention, e.g: veg_quercus.robur_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3 with the following fields: theme: e.g. <strong>veg</strong>, species code: e.g. <strong>quercus.robur</strong>, species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation), species estimation method: e.g. <strong>eml</strong>, species estimation type: e.g. <strong>md</strong> ( = model deviation), resolution in meters e.g. <strong>30m</strong>, reference depths (vertical dimension): e.g. <strong>0..0cm</strong>, reference period begin end: e.g. <strong>2000..2002</strong>, reference area: e.g. <strong>eumap</strong>, coordinate system: e.g. <strong>epsg3035</strong>, data set version: e.g. <strong>v0.3</strong>. For each species is then easy to identify probability and uncertainty distribution maps: veg_quercus.robur_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_quercus.robur_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_quercus.robur_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_quercus.robur_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution Files are provided as Cloud Optimized GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format. If you would like to know more about the creation of the maps and the modeling: <strong>watch</strong> the talk at Open Data Science Workshop 2021 (TIB AV-PORTAL) <strong>access </strong>the repository with our R/Python scripts and follow the instructions (GitLab) <strong>access </strong>the repository with the training dataset (Zenodo) <strong>read </strong>the tutorial with executable code on our GitBook A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on PeerJ. To suggest any improvement/fix use https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues
description: This work is co-financed under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 by the European Union (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.35077/ace-cat
title: 190806_JH_HK0200_PlexinD1LSLflp_SSp_S1hindlimb_female

description: An essential step toward a comprehensive understanding of brain function is to establish a cellular-resolution structural framework upon which multiscale and multi-modal information spanning molecules, cells, circuits, systems and behavior can be registered, integrated, interpreted and mined. Dr. Huang and Arlotta will lead the Center to produce two complementary cell atlases integrated within a digital Common Coordinate Framework of the mouse brain: 1) a Brainwide Cell Transcriptome Atlas of unprecedented scale and resolution, integrated with 2) an in-depth Forebrain Projection Neuron Atlas that combines comprehensive anatomic and molecular information.
description: projection
description: DATASET METADATA: title:'190806_JH_HK0200_PlexinD1LSLflp_SSp_S1hindlimb_female', GeneralModality:'anatomy/morphology', Technique:'STPT' SPECIMEN METADATA: LocalID:'190806_JH_HK0200_PlexinD1LSLflp_SSp_S1hindlimb_female', Species:'mouse', NCBITaxonomy:'NCBI:txid10090', Age:'9', Ageunit:'Weeks', Sex:'Female', OrganName:'Brain', Locations:'whole brain' INSTRUMENT METADATA: MicroscopeType:'Two Photon', MicroscopeManufacturerAndModel:'TissueVision' IMAGE METADATA: yAxis:'right-to-left', zAxis:'anterior-to-posterior', Number:'1,2,red,green', displayColor:'(1.0, 0.0, 0.0),(0.0, 1.0, 0.0)', stepSizeX:'1 micron/pixel', stepSizeY:'1 micron/pixel', stepSizeZ:'50 micron/pixel'
DOI: 10.5281/zenodo.6943921
title: Wikidata Dump human

description: RDF dump of wikidata produced with wdumper.<br>View on wdumper<b>entity count<b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0</b></b>
DOI: 10.5281/zenodo.6943920
title: Wikidata Dump human

description: RDF dump of wikidata produced with wdumper.<br>View on wdumper<b>entity count<b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0</b></b>
DOI: 10.5281/zenodo.6946246
title: Wikidata Dump I don really know what im doing

description: RDF dump of wikidata produced with wdumper.Confusion intensifies.<br>View on wdumper<b>entity count<b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 38</b></b>
DOI: 10.5281/zenodo.6946245
title: Wikidata Dump I don really know what im doing

description: RDF dump of wikidata produced with wdumper.Confusion intensifies.<br>View on wdumper<b>entity count<b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 38</b></b>
DOI: 10.6069/wzhr-v063
title: Global Burden of Disease Study 2019 (GBD 2019) Contraceptive Prevalence Estimates 1970-2019

description: Annual estimates of contraceptive use and need for family planning were produced for 204 countries and territories from 1970–2019. Data used came from cross-sectional surveys that sampled women ages 15-49 in which respondents self-reported contraceptive use. This dataset includes annual estimates by location, age group, and marital status for any and modern contraceptive prevalence, unmet need for any contraception, and demand satisfied with modern methods. Additionally, prevalence was estimated for 15 contraceptive methods. If a women reported using more than one method, only the most effective method was counted; the methods above are listed in order of prioritization. Due to small sample sizes of partnered and unpartnered women in some locations, only all age and age-standardized estimates (ages 15-49) are provided with marital breakdowns.
DOI: 10.35077/ace-cop
title: 190422_JH_HK0131_PlexinD1LSLflp_SSp_S1forelimb_male

description: An essential step toward a comprehensive understanding of brain function is to establish a cellular-resolution structural framework upon which multiscale and multi-modal information spanning molecules, cells, circuits, systems and behavior can be registered, integrated, interpreted and mined. Dr. Huang and Arlotta will lead the Center to produce two complementary cell atlases integrated within a digital Common Coordinate Framework of the mouse brain: 1) a Brainwide Cell Transcriptome Atlas of unprecedented scale and resolution, integrated with 2) an in-depth Forebrain Projection Neuron Atlas that combines comprehensive anatomic and molecular information.
description: projection
description: DATASET METADATA: title:'190422_JH_HK0131_PlexinD1LSLflp_SSp_S1forelimb_male', GeneralModality:'anatomy/morphology', Technique:'STPT' SPECIMEN METADATA: LocalID:'190422_JH_HK0131_PlexinD1LSLflp_SSp_S1forelimb_male', Species:'mouse', NCBITaxonomy:'NCBI:txid10090', Age:'14', Ageunit:'Weeks', Sex:'Male', OrganName:'Brain', Locations:'SSp' INSTRUMENT METADATA: MicroscopeType:'Two Photon', MicroscopeManufacturerAndModel:'TissueVision' IMAGE METADATA: yAxis:'right-to-left', zAxis:'anterior-to-posterior', Number:'1,2,red,green', displayColor:'(1.0, 0.0, 0.0),(0.0, 1.0, 0.0)', stepSizeX:'1 micron/pixel', stepSizeY:'1 micron/pixel', stepSizeZ:'50 micron/pixel'
DOI: 10.18119/n9h60h
title: Metabolic network extended with drug-target interactions
DOI: 10.5281/zenodo.6954396
title: NoF_7_vz

description: Raw output from GHOST Nx = 2048, Ny = 2048, Nz = 2048 bvfreq = 58.49 omega_z = 4.178 kdn = 10.00 <br> kup = 11.00
DOI: 10.5281/zenodo.6954390
title: NoF_7_vy

description: Raw output from GHOST Nx = 2048, Ny = 2048, Nz = 2048 bvfreq = 58.49 omega_z = 4.178 kdn = 10.00 <br> kup = 11.00
DOI: 10.5281/zenodo.6954395
title: NoF_7_vz

description: Raw output from GHOST Nx = 2048, Ny = 2048, Nz = 2048 bvfreq = 58.49 omega_z = 4.178 kdn = 10.00 <br> kup = 11.00
DOI: 10.5281/zenodo.6954321
title: NoF_7_vx

description: Raw output from GHOST Nx = 2048, Ny = 2048, Nz = 2048 bvfreq = 58.49 omega_z = 4.178 kdn = 10.00 <br> kup = 11.00
DOI: 10.5281/zenodo.6954389
title: NoF_7_vy

description: Raw output from GHOST Nx = 2048, Ny = 2048, Nz = 2048 bvfreq = 58.49 omega_z = 4.178 kdn = 10.00 <br> kup = 11.00
DOI: 10.5281/zenodo.6954320
title: NoF_7_vx

description: Raw output from GHOST Nx = 2048, Ny = 2048, Nz = 2048 bvfreq = 58.49 omega_z = 4.178 kdn = 10.00 <br> kup = 11.00
DOI: 10.6084/m9.figshare.20440500
title: ff_frag.xlsx

description: A sample of 227 rebel groups active between 1989 and 2011.
DOI: 10.5285/9ce1fd9f-cfaf-44fa-aa5d-45c24c0a76cc
title: Annual microparticle and ion fluxes in the Jurassic, Sherman Island and Sky-Blu ice cores (1992-2019 CE)

description: This dataset presents the microparticle and ion fluxes from a set of ice cores from the Antarctic Peninsula and Ellsworth Land, as presented in Tetzner et al. (2022). Microparticle (MPC_flux) and ionic (nssCa+2_flux, nssK+_flux, ssNa+_flux, MSA_flux) data are provided as annual fluxes for the 1992-2019 CE interval. Annual fluxes were calculated as winter-to-winter averages. Data points represent the annual austral winter-to-winter average and are presented over the correspondent austral summer. The dataset comprises timeseries CSV files. The first column represents years between 1992 and 2019 CE, and the remaining columns represent annual flux data as the number of microparticles (particles) or ion concentration (ppb), multiplied by annual snow accumulation (kg m-2), listed for each ice core site alphabetically (Jurassic (JUR), Sherman Island (SHIC) and Sky-Blu (SKBL)). This dataset was created with the support of the Comision Nacional de Investigacion Cientifica y Tecnologica (grant number 72180432).
description: Microparticle records were obtained using a flow-through Klotz Abakus laser particle counter connected to a continuous ice core melter system at the British Antarctic Survey. Discrete Ionic records (5 cm) were obtained using ion chromatographic techniques in a reagent free Dionex ICS-2500 anion and IC 2000 cation systems in a class-100 clean room at the British Antarctic Survey. nssCa+2, nssK+ and ssNa+ parameters, presented in this dataset, were calculated following Equations (1), (2) and (3) presented in Section 2.2 of Tetzner et al. (2022).
description: MPC_flux data was obtained using a flow-through Klotz Abakus laser particle counter connected to a continuous ice core melter system at the British Antarctic Survey. Ca+2, K+, Na+ and MSA datasets were obtained using ion chromatographic techniques in a reagent free Dionex ICS-2500 anion and IC 2000 cation systems in a class-100 clean room at the British Antarctic Survey.
description: Microparticle measurements were calibrated using using polystyrene particle standards. Microparticle data from the topmost part of the JUR ice core is missing due to an instrument failure (equivalent to the interval 2011.5-2013 CE). Samples for ion analyses were run several times and calibrated against three working standards. Missing data are presented as -999.99.
DOI: 10.15156/bio/sh1720025.08fu
title: SH1720025.08FU

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/sh355277.07fu
title: SH355277.07FU

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/sh355278.07fu
title: SH355278.07FU

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/sh1720026.08fu
title: SH1720026.08FU

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/sh1720027.08fu
title: SH1720027.08FU

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/sh1720028.08fu
title: SH1720028.08FU

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/sh355279.07fu
title: SH355279.07FU

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.