cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29489120
title: Kidney Transplant Biopsy-derived signature matrix of 18 cell phenotypes (KTB18) for deconvolution using the CIBERSORTx algorithm

description: CIBERSORTx is an algorithm, accessible through a web portal, designed to infer the cellular composition of bulk RNA-seq or microarray data, referred to as "mixture files". This process, known as deconvolution, involves using a source called a "signature matrix", which contains known gene expression profiles of various cell types, built by CIBERSORTx from a single-cell RNA-seq dataset referred to as a "reference matrix". By comparing the mixture files to the signature matrix, CIBERSORTx can estimate the proportions of different cell types within the sample.The files included here are used for and obtained from the construction of a kidney allograft biopsy signature matrix by CIBERSORTx, and can be downloaded to infer cell fractions on kidney allograft biopsy bulk RNA-seq or microarray data.A gene expression matrix, derived from a single-cell RNA-seq dataset (Lamarthée et al. Nat comm 2023) comprised of 35,132 cells divided into 18 different cell phenotypes and originating from 16 kidney allograft biopsies, was normalized to counts-per-million (CPM) and subset to 10,000 random cells, maintaining the relative proportion of each cell type.The resulting text file, named KTB18_reference_matrix.txt, was used as input to the "Create Signature Matrix" module of CIBERSORTx. Given that a droplet-based technique (10X Chromium) was used for generating the single-cell RNA-seq dataset, the <i>Min. Expression</i> value was reduced to 0.1. This adjustment aims to increase the reliability of the signature matrix, as recommended by CIBERSORTx.Upon completion, the resulting text file, named KTB18_signature_matrix.txt, can be used as input to the "Inpute Cell Fractions" module of CIBERSORTx to estimate the cell proportions of mixture files. Given again that a droplet-based technique was used for generating the single-cell RNA-seq dataset, the <i>S-mode</i> batch correction is necessary to remove technical differences between the signature matrix and the mixture files, since they originate from different sequencing technologies. The reference matrix is included for this purpose, as this method requires it.
DOI: 10.6084/m9.figshare.29489120.v3
title: Kidney Transplant Biopsy-derived signature matrix of 18 cell phenotypes (KTB18) for deconvolution using the CIBERSORTx algorithm

description: CIBERSORTx is an algorithm, accessible through a web portal, designed to infer the cellular composition of bulk RNA-seq or microarray data, referred to as "mixture files". This process, known as deconvolution, involves using a source called a "signature matrix", which contains known gene expression profiles of various cell types, built by CIBERSORTx from a single-cell RNA-seq dataset referred to as a "reference matrix". By comparing the mixture files to the signature matrix, CIBERSORTx can estimate the proportions of different cell types within the sample.The files included here are used for and obtained from the construction of a kidney allograft biopsy signature matrix by CIBERSORTx, and can be downloaded to infer cell fractions on kidney allograft biopsy bulk RNA-seq or microarray data.A gene expression matrix, derived from a single-cell RNA-seq dataset (Lamarthée et al. Nat comm 2023) comprised of 35,132 cells divided into 18 different cell phenotypes and originating from 16 kidney allograft biopsies, was normalized to counts-per-million (CPM) and subset to 10,000 random cells, maintaining the relative proportion of each cell type.The resulting text file, named KTB18_reference_matrix.txt, was used as input to the "Create Signature Matrix" module of CIBERSORTx. Given that a droplet-based technique (10X Chromium) was used for generating the single-cell RNA-seq dataset, the <i>Min. Expression</i> value was reduced to 0.1. This adjustment aims to increase the reliability of the signature matrix, as recommended by CIBERSORTx.Upon completion, the resulting text file, named KTB18_signature_matrix.txt, can be used as input to the "Inpute Cell Fractions" module of CIBERSORTx to estimate the cell proportions of mixture files. Given again that a droplet-based technique was used for generating the single-cell RNA-seq dataset, the <i>S-mode</i> batch correction is necessary to remove technical differences between the signature matrix and the mixture files, since they originate from different sequencing technologies. The reference matrix is included for this purpose, as this method requires it.
DOI: 10.25625/jgdi8y
title: Data for "Viscoelasticity Analysis of Coarse-grained Cytoskeletal Simulations with Cytosim and Cytocalc"

description: This dataset contains the data files, Cytosim config files and Cytocalc scripts for reproducing the results in "Viscoelasticity Analysis of Coarse-grained Cytoskeletal Simulations with Cytosim and Cytocalc". All figures included in the main text can be generated from provided data by running "generate_figures.sh". Note Requires installing Cytocalc from https://gitlab.gwdg.de/vadakkeputha/csmparser and vskache (pip install vskache) For convenience and speed, this uses the report files generated from cytosim. However, Cytosim config files for all report files are included in the same directory as the report files. These can be run with cytosim to generate the cmo files. Note The report files provided are of the type "fiber:rpt" which is a custom report type selecting a random point per filament. This can be added to simul_report.cpp as shown in https://github.com/vskrishna1082/report-random-cytosim/blob/main/report-random.cpp. Using "fiber:points" is another (albeit data heavy) alternative Report (and config) files can be found in the "out" or "out_extended" directories corresponding to each figure. Analysis scripts using cytocalc are stored in 'analyze' or 'analysis' directories parallel to the 'out' directories.
DOI: 10.15468/dl.67kxjf
title: Occurrence Download

description: A dataset containing 562685 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Invertebrate Paleontology Division, Yale Peabody Museum",
 "OccurrenceStatus is Present",
 "PublishingOrg is 2e167bb0-4441-11db-9ba2-b8a03c50a862"
 ]
}

 The dataset includes 562685 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025013-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30581261
title: Dataset on Anatomical Pathology Laboratory Capabilities in Eastern Democratic Republic of Congo

description: This dataset documents the infrastructure and diagnostic capacity of pathology laboratories in the eastern Democratic Republic of Congo (DRC). Data were generated through a multi-site mixed-methods study conducted between February and April 2023. The dataset includes information on laboratory equipment, human resources, workflow organization, and diagnostic performance, accompanied by qualitative insights from interviews and field observations
DOI: 10.6084/m9.figshare.30581261.v1
title: Dataset on Anatomical Pathology Laboratory Capabilities in Eastern Democratic Republic of Congo

description: This dataset documents the infrastructure and diagnostic capacity of pathology laboratories in the eastern Democratic Republic of Congo (DRC). Data were generated through a multi-site mixed-methods study conducted between February and April 2023. The dataset includes information on laboratory equipment, human resources, workflow organization, and diagnostic performance, accompanied by qualitative insights from interviews and field observations
DOI: 10.15468/dl.fe8vfs
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is Asia",
 "Country is India",
 "OccurrenceStatus is Present",
 "TaxonKey is Nardostachys jatamansi"
 ]
}

 The dataset includes 7 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025028-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4cxhgk
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "TaxonKey is Echinopsis spiniflora"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025030-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.es7vzg
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "TaxonKey is Chamaecereus stilowianus"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025029-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ha4d62
title: Occurrence Download

description: A dataset containing 110288 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Epipactis atrorubens",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation, Occurrence evidence)",
 "OccurrenceStatus is Present",
 "HasCoordinate is true"
 ]
}

 The dataset includes 110288 records from 423 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024997-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30581555
title: Data used for ecological network construction and simulation

description: <b>1.Land use data</b> was derived from the 2023 version of <b>SinoLC-1</b>. Based on mainstream large-scale land cover classification systems and Chinese landscape characteristics, the SinoLC-1 classification system defines 11 land cover types: <i>Arbor cover</i>, <i>Shrubland</i>, <i>Grassland</i>, <i>Cropland</i>, <i>Buildings</i>, <i>Transportation routes</i>, <i>Sparse or barren vegetation</i>, <i>Ice and snow</i>, <i>Water</i>, <i>Wetlands</i>, and <i>Mosses and lichens</i>. In this study, “Buildings” and “Transportation routes” were aggregated into the category of <b>Built-up areas</b>, “Cropland” was categorized as <b>Cropland</b>, “Arbor cover” and “Shrubland” were combined into <b>Forest</b>, “Grassland” remained as <b>Grassland</b>, and “Water” and “Wetlands” were merged into <b>Water bodies</b>. “Sparse or barren vegetation” was categorized as <b>Unused land</b>, while “Ice and snow” and “Mosses and lichens” were not present in the study area and thus were excluded from analysis.<b>2.</b><b>Road data</b>, including railways, expressways, national highways, and major urban roads, were incorporated for both habitat quality evaluation and resistance surface construction.3.The <b>Normalized Difference Vegetation Index (NDVI)</b> was utilized in the construction of the resistance surface.<b>4.</b><b>Digital Elevation Model (DEM)</b> data were also employed to construct the resistance surface.<b>5.</b><b>The coded data</b> were used to simulate cascading failures.<br>
DOI: 10.6084/m9.figshare.30581555.v1
title: Data used for ecological network construction and simulation

description: <b>1.Land use data</b> was derived from the 2023 version of <b>SinoLC-1</b>. Based on mainstream large-scale land cover classification systems and Chinese landscape characteristics, the SinoLC-1 classification system defines 11 land cover types: <i>Arbor cover</i>, <i>Shrubland</i>, <i>Grassland</i>, <i>Cropland</i>, <i>Buildings</i>, <i>Transportation routes</i>, <i>Sparse or barren vegetation</i>, <i>Ice and snow</i>, <i>Water</i>, <i>Wetlands</i>, and <i>Mosses and lichens</i>. In this study, “Buildings” and “Transportation routes” were aggregated into the category of <b>Built-up areas</b>, “Cropland” was categorized as <b>Cropland</b>, “Arbor cover” and “Shrubland” were combined into <b>Forest</b>, “Grassland” remained as <b>Grassland</b>, and “Water” and “Wetlands” were merged into <b>Water bodies</b>. “Sparse or barren vegetation” was categorized as <b>Unused land</b>, while “Ice and snow” and “Mosses and lichens” were not present in the study area and thus were excluded from analysis.<b>2.</b><b>Road data</b>, including railways, expressways, national highways, and major urban roads, were incorporated for both habitat quality evaluation and resistance surface construction.3.The <b>Normalized Difference Vegetation Index (NDVI)</b> was utilized in the construction of the resistance surface.<b>4.</b><b>Digital Elevation Model (DEM)</b> data were also employed to construct the resistance surface.<b>5.</b><b>The coded data</b> were used to simulate cascading failures.<br>
DOI: 10.15468/dl.9june2
title: Occurrence Download

description: A dataset containing 3044 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rangifer tarandus",
 "Year 2000-2001"
 ]
}

 The dataset includes 3044 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025032-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26143/ganil-2021-e810_20
title: Identification of exotic reaction channels in 238U+238U
title: e810_20

description: The aim of the experiment was to produce and identify neutron-rich nuclei in the uranium region by multi-Nucleon Transfer (MNT) aiming at a particle identification in atomic mass A by VAMOS and an atomic chargeidentification by X-ray detection in a set of three Low Energy Photon Spectrometers (LEPS) in the reaction 238U+238U at above-Coulomb barrier energies. The VAMOS setup was equipped with a new target chamber accommodating the three LEPS X-ray detectors anda second arm including the HARPEE particle detector. VAMOS was equipped with new MWPC detectors. The germanium detector AGATA was used to investigate the nuclear excitation structure of the detected nuclei.
description: Detector: VAMOS + AGATA + ID-fix
description: projectileIsotope: 238U; projectileChargeState: 33+; projectileBeamEnergy: 6.91; projectileBeamEnergyUnit: MeV/u; projectileParticle: Ion; projectileAverageBeamIntensity: 6.2e+9; projectileBeamIntensityUnit: pps; projectilePercentage: 100; projectileBeamType: Stable; projectileAcceleratorType: Cyclotrons
description: projectileIsotope: 238U; projectileChargeState: 32+; projectileBeamEnergy: 6.5; projectileBeamEnergyUnit: MeV/u; projectileParticle: Ion; projectileAverageBeamIntensity: 6.2e+9; projectileBeamIntensityUnit: pps; projectilePercentage: 100; projectileBeamType: Stable; projectileAcceleratorType: Cyclotrons
description: projectileIsotope: 238U; projectileChargeState: 31+; projectileBeamEnergy: 6.1; projectileBeamEnergyUnit: MeV/u; projectileParticle: Ion; projectileAverageBeamIntensity: 6.2e+9; projectileBeamIntensityUnit: pps; projectilePercentage: 100; projectileBeamType: Stable; projectileAcceleratorType: Cyclotrons
description: targetIsotopes: Au; targetIsotopePercentage: 100; targetSamplePhase: Solid; targetType: Isotope; targetTargetThicknessValue: 0.090; targetTargetThicknessUnit: mg/cm2; targetDensity: ; targetDensityUnit: ;
description: targetIsotopes: Al degrader; targetIsotopePercentage: 100; targetSamplePhase: Solid; targetType: Isotope; targetTargetThicknessValue: 20; targetTargetThicknessUnit: μm; targetDensity: ; targetDensityUnit: ;
description: targetIsotopes: C; targetIsotopePercentage: 100; targetSamplePhase: Solid; targetType: Isotope; targetTargetThicknessValue: 0.050; targetTargetThicknessUnit: mg/cm2; targetDensity: ; targetDensityUnit: ;
description: targetIsotopes: C; targetIsotopePercentage: 100; targetSamplePhase: Solid; targetType: Isotope; targetTargetThicknessValue: 0.005; targetTargetThicknessUnit: mg/cm2; targetDensity: ; targetDensityUnit: ;
description: targetIsotopes: 238U; targetIsotopePercentage: 100; targetSamplePhase: Solid; targetType: Isotope; targetTargetThicknessValue: 0.615; targetTargetThicknessUnit: mg/cm2; targetDensity: ; targetDensityUnit: ;
description: targetIsotopes: 238U; targetIsotopePercentage: 100; targetSamplePhase: Solid; targetType: Isotope; targetTargetThicknessValue: 0.620; targetTargetThicknessUnit: mg/cm2; targetDensity: ; targetDensityUnit: ;
DOI: 10.15468/dl.3mjeut
title: Occurrence Download

description: A dataset containing 137 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Epipactis exilis",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation, Occurrence evidence)",
 "OccurrenceStatus is Present",
 "HasCoordinate is true"
 ]
}

 The dataset includes 137 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025033-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5439/3000997
title: Mobile measurement of CH4 around the SGP ARM site during June 23-26, 2024

description: A mobile measurement campaign from June 23-26, 2024 was conducted to detect and quantify CH₄ emissions within about 8 km of the SGP site. Using a LI-COR LI-7810 tracer gas analyzer mounted on a vehicle, we sampled CH₄ along predefined routes surrounding a suspected source area around the SGP ARM site. Measurements were collected between 04:30 and 09:00 local time when the PBL was shallow, increasing sensitivity to surface emissions. Driving speeds were kept below 5 mph to minimize pressure-related artifacts.
DOI: 10.15468/dl.k95jct
title: Occurrence Download

description: A dataset containing 91 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "TaxonKey is Soehrensia schickendantzii"
 ]
}

 The dataset includes 91 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025034-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48366/r1563131
title: Summary of Reported Anisotropic and Isotropic Atomic Layer Etching (ALE) Processes for Metals

description: This comparison, derived from Table II of “Anisotropic/Isotropic Atomic Layer Etching of Metals” (Appl. Sci. Converg. Technol. 29(3), 2020) https://doi.org/10.5757/ASCT.2020.29.3.041 , provides an overview of reported ALE processes applied to metallic materials. It summarizes adsorption and desorption chemistries, process temperatures, and etch-per-cycle values for various metals under both anisotropic (plasma- or ion-assisted) and isotropic (thermal or chemical) conditions. The table highlights how diverse chemistries—such as oxidation, fluorination, ligand exchange, and acid vapor reactions—enable angstrom-scale control in metal etching while maintaining selectivity and surface smoothness.
DOI: 10.17026/ar/azmlm5
title: Archeologisch vooronderzoek plangebied Lijndenweg 9 te Beverwijk, gemeente Beverwijk
title: Ruimtelijk advies op basis van bureauonderzoek

description: BO
DOI: 10.5281/zenodo.17572453
title: SI from Impact of granular activated carbon, voltage application and sewage sludge pretreatment on mesophilic anaerobic digestion performance and per- and polyfluoroalkyl substances removal

description: SI from the paper Impact of granular activated carbon, voltage application and sewage sludge pretreatment on mesophilic anaerobic digestion performance and per- and polyfluoroalkyl substances removal
DOI: 10.5281/zenodo.17572452
title: SI from Impact of granular activated carbon, voltage application and sewage sludge pretreatment on mesophilic anaerobic digestion performance and per- and polyfluoroalkyl substances removal

description: SI from the paper Impact of granular activated carbon, voltage application and sewage sludge pretreatment on mesophilic anaerobic digestion performance and per- and polyfluoroalkyl substances removal
DOI: 10.17894/ucph.cec069f6-5654-464d-bc79-c5ece42eaeb7
title: 3RADseq data for Platanthera chlorantha and P. bifolia populations in Denmark
DOI: 10.15468/dl.52zgda
title: Occurrence Download

description: A dataset containing 81733 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Haliaeetus leucoryphus, Aythya baeri, Platalea minor, Aquila heliaca, Neophron percnopterus, Falco cherrug, Aquila clanga, Anser erythropus, Aquila nipalensis, Otis tarda)",
 "Geometry POLYGON((73.5208333 16.6625,135.0791667 16.6625,135.0791667 53.5541667,73.5208333 53.5541667,73.5208333 16.6625))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Country is one of (China, Hong Kong, Macao, Chinese Taipei)"
 ]
}

 The dataset includes 81733 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024998-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p9vp9g
title: Occurrence Download

description: A dataset containing 1721542 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Vulpes vulpes"
 ]
}

 The dataset includes 1721542 records from 3997 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024952-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1413769.10fu
title: SH1413769.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1413772.10fu
title: SH1413772.10FU

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