cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48597/q9kp-ha7a
title: Measurement of Aerosol_absorption_coefficient at Barrow

description: Measurement of Aerosol_absorption_coefficient at Barrow
DOI: 10.48597/qa7b-nvpa
title: Measurement of Aerosol_absorption_coefficient at Barrow

description: Measurement of Aerosol_absorption_coefficient at Barrow
DOI: 10.48597/qvmx-uj6z
title: Measurement of Aerosol_absorption_coefficient at Cape San Juan

description: Measurement of Aerosol_absorption_coefficient at Cape San Juan
DOI: 10.48597/rrj9-uheq
title: Measurement of Aerosol light scattering at K-puszta

description: Measurement of Aerosol light scattering at K-puszta
DOI: 10.48597/rxfw-nxsj
title: Measurement of Aerosol_absorption_coefficient at Cape San Juan

description: Measurement of Aerosol_absorption_coefficient at Cape San Juan
DOI: 10.48597/swzt-pphr
title: Measurement of Pm10_mass at Kollumerwaard

description: Measurement of Pm10_mass at Kollumerwaard
DOI: 10.48597/t3ep-tmqe
title: Measurement of Aerosol light scattering at Cape San Juan

description: Measurement of Aerosol light scattering at Cape San Juan
DOI: 10.48597/t6gk-48he
title: Measurement of Aerosol_absorption_coefficient at Cape San Juan

description: Measurement of Aerosol_absorption_coefficient at Cape San Juan
DOI: 10.48597/tdcw-ctt8
title: Measurement of Aerosol light scattering at K-puszta

description: Measurement of Aerosol light scattering at K-puszta
DOI: 10.48597/tv9r-suvp
title: Measurement of Aerosol_absorption_coefficient at Cape San Juan

description: Measurement of Aerosol_absorption_coefficient at Cape San Juan
DOI: 10.48597/unbn-d9qh
title: Measurement of Aerosol_absorption_coefficient at Barrow

description: Measurement of Aerosol_absorption_coefficient at Barrow
DOI: 10.48597/v6rp-pzwb
title: Measurement of Particle_number_concentration at Cape San Juan

description: Measurement of Particle_number_concentration at Cape San Juan
DOI: 10.48597/vf5c-cy2b
title: Measurement of Aerosol_absorption_coefficient at Cape San Juan

description: Measurement of Aerosol_absorption_coefficient at Cape San Juan
DOI: 10.48597/vn6r-ep4c
title: Measurement of Aerosol_absorption_coefficient at Barrow

description: Measurement of Aerosol_absorption_coefficient at Barrow
DOI: 10.48597/vtua-e8mb
title: Measurement of Aerosol light scattering at Cape San Juan

description: Measurement of Aerosol light scattering at Cape San Juan
DOI: 10.48597/vvmz-w7pv
title: Measurement of Aerosol_absorption_coefficient at Barrow

description: Measurement of Aerosol_absorption_coefficient at Barrow
DOI: 10.48597/vy6j-4ckx
title: Measurement of Particle_number_concentration at Cape San Juan

description: Measurement of Particle_number_concentration at Cape San Juan
DOI: 10.48597/vyjt-8xxb
title: Measurement of Aerosol_absorption_coefficient at Cape San Juan

description: Measurement of Aerosol_absorption_coefficient at Cape San Juan
DOI: 10.48597/w5cq-sfcv
title: Measurement of Aerosol_absorption_coefficient at Cape San Juan

description: Measurement of Aerosol_absorption_coefficient at Cape San Juan
DOI: 10.15468/dl.2sry5q
title: Occurrence Download

description: A dataset containing 1371 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Eulophia graminea Lindl.)"
 ]
}

 The dataset includes 1371 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010177-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gtp6wj
title: Occurrence Download

description: A dataset containing 40399 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Euonymus alatus (Thunb.) Siebold)"
 ]
}

 The dataset includes 40399 records from 333 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010178-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.brak22
title: Occurrence Download

description: A dataset containing 1434 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Indonesia, Malaysia, Singapore, Brunei Darussalam)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Troides helena (Linnaeus, 1758)"
 ]
}

 The dataset includes 1434 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010179-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11143931
title: Maldivian seagrass aerial extent raster layers 2021 - 2000

description:  Contemporary Seagrass Map (2021)The contemporary product was derived from Sentinel-2 satellite imagery, operated by the European Space Agency (ESA). The imagery, with a spatial resolution of 10 meters was pre-processed in Google Earth Engine (GEE) following established methods for retrieval of benthic signals. A support vector machine (SVM) classifier was used for classification. Training data encompassed three classes: seagrass, non-seagrass (including coral reefs, mangroves, sand/rubble, and macroalgal beds), and optical-deep water (ODW), totaling 25,463 training pixels. Important: the classification output is a binary (seagrass/non-seagrass) class. Validation of the map was conducted independently using 1,019 in-situ field survey points collected from 2017-2023. Mapping accuracy was assessed through an error matrix. Overall accuracy = 82%

Historical Seagrass Maps (2000-2021)The historical mapping product is derived from Landsat data spanning 2000 to 2021. The Landsat missions, operated by the United States Geological Survey (USGS) in collaboration with NASA, provide satellite data with a spatial resolution of 30 meters. There are no suitable data for 2010-2011. Each composite, representing a two-year period, underwent radiometric normalisation relative to a reference image from 2020-2021. Training and validation data were designated using an identical methodology as the contemporary maps, with 823 validation points utilised for accuracy assessment from 2017-2023. A fixed pixel approach was adopted to assess accuracy across the entire time series, involving the manual delineation of seagrass and non-seagrass areas. Overall accuracy was >89% in all cases.

 

These data represent GeoTIFF files of seagrass habitat extent (binary classification). Contemporary data come from habitat classification of Sentinel-2 imagery (10 m pixel size). Historical maps come from habitat classification of Landsat data (30 m pixel size). For further details of workflow and data specifications please see the original publication DOI: 10.1038/s41598-024-61088-1
DOI: 10.5281/zenodo.11143930
title: Maldivian seagrass aerial extent raster layers 2021 - 2000

description: Contemporary Seagrass Map (2021)The contemporary product was derived from Sentinel-2 satellite imagery, operated by the European Space Agency (ESA). The imagery, with a spatial resolution of 10 meters was pre-processed in Google Earth Engine (GEE) following established methods for retrieval of benthic signals. A support vector machine (SVM) classifier was used for classification. Training data encompassed three classes: seagrass, non-seagrass (including coral reefs, mangroves, sand/rubble, and macroalgal beds), and optical-deep water (ODW), totaling 25,463 training pixels. Important: the classification output is a binary (seagrass/non-seagrass) class. Validation of the map was conducted independently using 1,019 in-situ field survey points collected from 2017-2023. Mapping accuracy was assessed through an error matrix. Overall accuracy = 82%

Historical Seagrass Maps (2000-2021)The historical mapping product is derived from Landsat data spanning 2000 to 2021. The Landsat missions, operated by the United States Geological Survey (USGS) in collaboration with NASA, provide satellite data with a spatial resolution of 30 meters. There are no suitable data for 2010-2011. Each composite, representing a two-year period, underwent radiometric normalisation relative to a reference image from 2020-2021. Training and validation data were designated using an identical methodology as the contemporary maps, with 823 validation points utilised for accuracy assessment from 2017-2023. A fixed pixel approach was adopted to assess accuracy across the entire time series, involving the manual delineation of seagrass and non-seagrass areas. Overall accuracy was >89% in all cases.

 

These data represent GeoTIFF files of seagrass habitat extent (binary classification). Contemporary data come from habitat classification of Sentinel-2 imagery (10 m pixel size). Historical maps come from habitat classification of Landsat data (30 m pixel size). For further details of workflow and data specifications please see the original publication DOI: 10.1038/s41598-024-61088-1
DOI: 10.5281/zenodo.11144240
title: Maldivian seagrass aerial extent raster layers 2021 - 2000

description: Contemporary Seagrass Map (2021)The contemporary product was derived from Sentinel-2 satellite imagery, operated by the European Space Agency (ESA). The imagery, with a spatial resolution of 10 meters was pre-processed in Google Earth Engine (GEE) following established methods for retrieval of benthic signals. A support vector machine (SVM) classifier was used for classification. Training data encompassed three classes: seagrass, non-seagrass (including coral reefs, mangroves, sand/rubble, and macroalgal beds), and optical-deep water (ODW), totaling 25,463 training pixels. Important: the classification output is a binary (seagrass/non-seagrass) class. Validation of the map was conducted independently using 1,019 in-situ field survey points collected from 2017-2023. Mapping accuracy was assessed through an error matrix. Overall accuracy = 82%

Historical Seagrass Maps (2000-2021)The historical mapping product is derived from Landsat data spanning 2000 to 2021. The Landsat missions, operated by the United States Geological Survey (USGS) in collaboration with NASA, provide satellite data with a spatial resolution of 30 meters. There are no suitable data for 2010-2011. Each composite, representing a two-year period, underwent radiometric normalisation relative to a reference image from 2020-2021. Training and validation data were designated using an identical methodology as the contemporary maps, with 823 validation points utilised for accuracy assessment from 2017-2023. A fixed pixel approach was adopted to assess accuracy across the entire time series, involving the manual delineation of seagrass and non-seagrass areas. Overall accuracy was >89% in all cases.

 

These data represent GeoTIFF files of seagrass habitat extent (binary classification). Contemporary data come from habitat classification of Sentinel-2 imagery (10 m pixel size). Historical maps come from habitat classification of Landsat data (30 m pixel size). For further details of workflow and data specifications please see the original publication DOI: 10.1038/s41598-024-61088-1
DOI: 10.15468/dl.9nudjc
title: Occurrence Download

description: A dataset containing 724153 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Xeno-canto - Bird sounds from around the world"
 ]
}

 The dataset includes 724153 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010169-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10688270
title: sORFdb

description: This repository contains the data of the sORFdb database.

 

sORFdb is a dedicated taxon-independent database for short open reading frames (sORFs) and small proteins as well as their functions in bacteria.

It combines high quality sORF and protein sequences and enrichines them with additional information such as physicochemical properties and the presence ribosomal binding sites. Small protein families were identified and can be used for sequence search and further studies.

 

The complete database with all small proteins is available as TSV file and the sequence data (sORF and small proteins) of all entries in the database are avilable as compressed FASTA files:



sorfdb.tsv.gz: sORFdb database

sorfdb.faa.gz: Small proteins

sorfdb.fna.gz: sORFs


For the small protein families a TSV with small protein sequences, family IDs as well as family statistics and a compressed ASCII HMM file (HMMER3/f 3.4) is provided:



sorfdb.families.tsv.gz: Small protein families

sorfdb.hmm.gz: ASCII HMM file
DOI: 10.5281/zenodo.10688271
title: sORFdb

description: This repository contains the data of the sORFdb database.

 

sORFdb is a dedicated taxon-independent database for short open reading frames (sORFs) and small proteins as well as their functions in bacteria.

It combines high quality sORF and protein sequences and enrichines them with additional information such as physicochemical properties and the presence ribosomal binding sites. Small protein families were identified and can be used for sequence search and further studies.

 

The complete database with all small proteins is available as TSV file and the sequence data (sORF and small proteins) of all entries in the database are avilable as compressed FASTA files:



sorfdb.tsv.gz: sORFdb database

sorfdb.faa.gz: Small proteins

sorfdb.fna.gz: sORFs


For the small protein families a TSV with small protein sequences, family IDs as well as family statistics and a compressed ASCII HMM file (HMMER3/f 3.4) is provided:



sorfdb.families.tsv.gz: Small protein families

sorfdb.hmm.gz: ASCII HMM file
DOI: 10.15468/dl.8hyyjp
title: Occurrence Download

description: A dataset containing 440158 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Euonymus europaeus L.)"
 ]
}

 The dataset includes 440158 records from 2205 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010180-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aqkn5k
title: Occurrence Download

description: A dataset containing 55326114 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year 1000-2023"
 ]
}

 The dataset includes 55326114 records from 3682 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047977-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pzdjf7
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "GbifId" : [
 "is one of (2284337142, 2284219248, 2284343290, 2284346348, 2571407256, 2592239210)"
 ]
}

 The dataset includes 6 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022590-250402121839773/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.26944302.v1
title: Additional file 5 of Single-cell RNA sequencing identifies inherent abnormalities of adipose-derived stem cells from nonlesional sites of patients with localized scleroderma

description: Additional file 5: Table S5. Gene-coexpression modules identified in ASCs through hdWGCNA.
DOI: 10.6084/m9.figshare.26944584
title: Additional file 2 of Deciphering glial contributions to CSF1R-related disorder via single-nuclear transcriptomic profiling: a case study

description: Additional file 2 (XLSX 6148 KB). Supplementary Table 2. Lists of differentially expressed genes (MAST test) for the following comparisons: (1) A1-CSF1R-RD versus A0-Control, (2) A2-CSF1R-RD versus A0-Control, (3) M0-AD versus M1-Control, (4) M2-CSF1R-RD versus M1-Control, (4) M3-CSF1R-RD versus M1-Control, (5) M3-CSF1R-RD versus M2-CSF1R-RD, (6) O3-CSF1R-RD versus O1-Control, (7) OP0-CSF1R-RD versus OP0-Control, (8) OP1-CSF1R-RD versus OP0-Control, (9) OP1-CSF1R-RD versus OP2 + OP3-Control.
DOI: 10.6084/m9.figshare.26944425.v1
title: Additional file 1 of Network pharmacology-based mechanism analysis of dauricine on the alleviating Aβ-induced neurotoxicity in Caenorhabditis elegans

description: Supplementary Material 1: The original data of Network pharmacology analysis
DOI: 10.6084/m9.figshare.26944359.v1
title: Additional file 1 of Adaptation in landlocked Atlantic salmon links genetics in wild and farmed salmon to smoltification

description: Supplementary Material 1.
DOI: 10.6084/m9.figshare.26944605.v1
title: Additional file 4 of The role of social motivation in sharing and fairness: insights from Williams syndrome

description: Supplementary Material 4
DOI: 10.6084/m9.figshare.26944236
title: Additional file 1 of Serum and urinary monocyte chemoattractant protein-1 as markers of inflammation and renal damage in dogs with naturally occurring leishmaniosis

description: Additional file 1
DOI: 10.5281/zenodo.13691584
title: Code for "An increasing Arctic-boreal CO2 sink offset by wildfires and source regions"

description: This repository includes the codes used to produce the results in Virkkala et al. (2024) in review. "An increasing Arctic-boreal CO2 sink offset by wildfires and source regions" (preprint: https://doi.org/10.1101/2024.02.09.579581). In short, there are R codes used to train random forest models, estimate importance scores and partial dependences for the variables, assess model predictive performance and uncertainties, predict (i.e. upscale) with the models and summarize model outputs using R version 4.2. Note that some of the datasets and results (e.g. monthly geospatial predictors or upscaling outputs) were not included in the repository due to their large file size (hundreds of GBs). Therefore, some of the paths to the files included in the codes are not working. These additional files are available from the corresponding author upon request. 

Folder structure:

flux_upscaling_data: includes the model training data, i.e. in-situ data as well as environmental data extracted from geospatial datasets

abcflux_modeling: includes the R analysis scripts in the codes-folder

abcflux_modeling_bigfiles: includes the key results and figures produced by the scripts

More details from avirkkala@woodwellclimate.org and from manuscript:

Anna-Maria Virkkala, Brendan M. Rogers, Jennifer D. Watts, Kyle A. Arndt, Stefano Potter, Isabel Wargowsky, Edward A. G. Schuur, Craig See, Marguerite Mauritz, Julia Boike, Syndonia M. Bret-Harte, Eleanor J. Burke, Arden Burrell, Namyi Chae, Abhishek Chatterjee, Frederic Chevallier, Torben R. Christensen, Roisin Commane, Han Dolman, Bo Elberling, Craig A. Emmerton, Eugenie S. Euskirchen, Liang Feng, Mathias Goeckede, Achim Grelle, Manuel Helbig, David Holl, Järvi Järveoja, Hideki Kobayashi, Lars Kutzbach, Junjie Liu, Ingrid Liujkx, Efrén López-Blanco, Kyle Lunneberg, Ivan Mammarella, Maija E. Marushchak, Mikhail Mastepanov, Yojiro Matsuura, Trofim Maximov, Lutz Merbold, Gesa Meyer, Mats B. Nilsson, Yosuke Niwa, Walter Oechel, Sang-Jong Park, Frans-Jan W. Parmentier, Matthias Peichl, Wouter Peters, Roman Petrov, William Quinton, Christian Rödenbeck, Torsten Sachs, Christopher Schulze, Oliver Sonnentag, Vincent St.Louis, Eeva-Stiina Tuittila, Masahito Ueyama, Andrej Varlagin, Donatella Zona, and Susan M. Natali. An increasing Arctic-boreal CO2 sink offset by wildfires and source regions.

bioRxiv 2024.02.09.579581; doi: https://doi.org/10.1101/2024.02.09.579581
DOI: 10.5281/zenodo.13691585
title: Code for "An increasing Arctic-boreal CO2 sink offset by wildfires and source regions"

description: This repository includes the codes used to produce the results in Virkkala et al. (2024) in review. "An increasing Arctic-boreal CO2 sink offset by wildfires and source regions" (preprint: https://doi.org/10.1101/2024.02.09.579581). In short, there are R codes used to train random forest models, estimate importance scores and partial dependences for the variables, assess model predictive performance and uncertainties, predict (i.e. upscale) with the models and summarize model outputs using R version 4.2. Note that some of the datasets and results (e.g. monthly geospatial predictors or upscaling outputs) were not included in the repository due to their large file size (hundreds of GBs). Therefore, some of the paths to the files included in the codes are not working. These additional files are available from the corresponding author upon request. 

Folder structure:

flux_upscaling_data: includes the model training data, i.e. in-situ data as well as environmental data extracted from geospatial datasets

abcflux_modeling: includes the R analysis scripts in the codes-folder

abcflux_modeling_bigfiles: includes the key results and figures produced by the scripts

More details from avirkkala@woodwellclimate.org and from manuscript:

Anna-Maria Virkkala, Brendan M. Rogers, Jennifer D. Watts, Kyle A. Arndt, Stefano Potter, Isabel Wargowsky, Edward A. G. Schuur, Craig See, Marguerite Mauritz, Julia Boike, Syndonia M. Bret-Harte, Eleanor J. Burke, Arden Burrell, Namyi Chae, Abhishek Chatterjee, Frederic Chevallier, Torben R. Christensen, Roisin Commane, Han Dolman, Bo Elberling, Craig A. Emmerton, Eugenie S. Euskirchen, Liang Feng, Mathias Goeckede, Achim Grelle, Manuel Helbig, David Holl, Järvi Järveoja, Hideki Kobayashi, Lars Kutzbach, Junjie Liu, Ingrid Liujkx, Efrén López-Blanco, Kyle Lunneberg, Ivan Mammarella, Maija E. Marushchak, Mikhail Mastepanov, Yojiro Matsuura, Trofim Maximov, Lutz Merbold, Gesa Meyer, Mats B. Nilsson, Yosuke Niwa, Walter Oechel, Sang-Jong Park, Frans-Jan W. Parmentier, Matthias Peichl, Wouter Peters, Roman Petrov, William Quinton, Christian Rödenbeck, Torsten Sachs, Christopher Schulze, Oliver Sonnentag, Vincent St.Louis, Eeva-Stiina Tuittila, Masahito Ueyama, Andrej Varlagin, Donatella Zona, and Susan M. Natali. An increasing Arctic-boreal CO2 sink offset by wildfires and source regions.

bioRxiv 2024.02.09.579581; doi: https://doi.org/10.1101/2024.02.09.579581
DOI: 10.6084/m9.figshare.26944587
title: Additional file 3 of Deciphering glial contributions to CSF1R-related disorder via single-nuclear transcriptomic profiling: a case study

description: Additional file 3 (XLSX 10033 KB). Supplementary Table 3. Gene Set Enrichment Analysis results employing the Biological Process branch of Gene Ontology for the following comparisons: (1) A1-CSF1R-RD versus A0-Control, (2) A2-CSF1R-RD versus A0-Control, (3) M0-AD versus M1-Control, (4) M2-CSF1R-RD versus M1-Control, (4) M3-CSF1R-RD versus M1-Control, (5) M3-CSF1R-RD versus M2-CSF1R-RD, (6) O3-CSF1R-RD versus O1-Control, (7) OP0-CSF1R-RD versus OP0-Control, (8) OP1-CSF1R-RD versus OP0-Control, (9) OP1-CSF1R-RD versus OP2 + OP3-Control.
DOI: 10.6084/m9.figshare.26944155
title: Additional file 6 of Genome-wide characterization and expression analysis of the CINNAMYL ALCOHOL DEHYDROGENASE gene family in Triticum aestivum

description: Supplementary Material 6
DOI: 10.6084/m9.figshare.26945025.v1
title: Additional file 1 of Integration of new zircon U–Pb ages with biostratigraphy to establish a high-precision age model of the Miocene Nakayama Formation on Sado Island in Central Japan

description: Supplementary Table 1. Input age constraint data for Undatable age model constructions.
DOI: 10.6084/m9.figshare.26944659
title: Additional file 1 of Comparative analysis of phosphorylated proteomes between plerocercoid and adult Spirometra mansoni reveals phosphoproteomic profiles of the medical tapeworm

description: Supplementary Material 1. Table S1. Modification of peptide information.