cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/nzgy-6as
title: Surface projections of Julich-Brain cytoarchitectonic maps (v2.9)

description: This dataset contains Julich-Brain cytoarchitectonic maps ([Amunts et al. 2020](https://www.science.org/doi/10.1126/science.abb4588)) projected to the „fsaverage" standard surface template. This surface-based parcellation is derived from the volumetric Maximum Probability Map (MPM) in the MNI Colin 27 reference space of the [Julich-Brain Cytoarchitectonic Atlas (v2.9)](https://search.kg.ebrains.eu/instances/a8932c7e-063c-4131-ab96-996d843998e9).
DOI: 10.25493/vsmk-h94
title: Whole-brain parcellation of the Julich-Brain Cytoarchitectonic Atlas (v2.9)

description: This dataset contains the whole-brain parcellation of the Julich-Brain Cytoarchitectonic Atlas ([Amunts et al., 2020](https://www.science.org/doi/10.1126/science.abb4588)) in the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space ([Evans et al., 2012](https://www.sciencedirect.com/science/article/pii/S1053811912000419?via%3Dihub)). The parcellation is derived from the individual probability maps (PMs) of the cytoarchitectonic regions released in the Julich-Brain Cytoarchitectonic Atlas, that are further combined into a Maximum Probability Map (MPM). The MPM is calculated by considering for each voxel the probability of all cytoarchitectonic areas released in the atlas, and determining the most probable assignment ([Eickhoff, 2005](https://www.sciencedirect.com/science/article/pii/S105381190400792X?via%3Dihub)). Gap maps complement cortical maps to achieve full cortical coverage.
DOI: 10.5066/f7g15zq6
title: Results of a U.S. General Public Survey to Inform the 2018 North American Waterfowl Management Plan Update (2017)

description: This contains data from 1,030 surveys returned in 2017 from across the United States. Data were collected via a mail-out survey stratified by the population of each state. Data collected include nature-related activity participation, attitudes and barriers to hunting and birdwatching, knowledge of others who hunt and birdwatch, preferred birds, involvement in conservation activities, preferences for information channels on nature-related topics, trust in sources on nature-related topics, wetlands knowledge/visitation, evaluation of wetlands' ecosystem services, and demographics. The purpose of this survey was to inform the 2018 update of the North American Waterfowl Management Plan.
DOI: 10.5281/zenodo.10523162
title: Data and scripts for figures in Walton & Huntingford, "Little Evidence of Hysteresis in Regional Precipitation, When Indexed by Global Temperature Rise and Fall in an Overshoot Climate Simulation"

description: The datasets included here are of the plotted data from the figures of the paper entitled "Little Evidence of Hysteresis in Regional Precipitation, When Indexed by Global Temperature Rise and Fall in an Overshoot Climate Simulation", submitted for publication to Environmental Research Letters.  Scripts used for plotting and analysis are also included.
DOI: 10.5281/zenodo.10547612
title: VAE extracted latent variables

description: This dataset includes VAE-extracted latent variables from Zhang et al.
DOI: 10.15156/bio/sh0744731.10fu
title: SH0744731.10FU

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: 10.17190/amf/1871136
title: AmeriFlux FLUXNET-1F US-GLE GLEES

description: This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-GLE GLEES. This is the FLUXNET version of the carbon flux data for the site US-GLE GLEES produced by applying the standard ONEFlux (1F) software. Site Description - The Glacier Lakes Ecosystem Experiments Site (GLEES) site is located on land owned by the U.S. government and managed by US Forest Service as part of the Medicine Bow National Forest. Many of the trees in the immediate vicinity of the site are older than 400 years, inter-dispersed among trees much younger in age. This widespread age distribution is most likely a derivation of one of two scenarios: 1) A widespread stand replacement about 400 years ago followed by a slow replacement; 2) Intermittent random disturbances over the past 400 years (Bradford et al. 2008). A decade long spruce beetle outbreak that peaked in 2008 resulted in the mortality of 85% of the forested basal area. There are a few private land holdings in the area, with scattered uncorked mining claims. Following the establishment of the National Forest, mining was banned and grazing was closed in the early 1990's in the upper portion of the GLEES site. Recreation in the winter, when snow can remain in patches into the summer months, snow mobiling and cross country skiing are popular. During the limited summer, hiking, camping and fishing are common activities. The site is accessible by vehicle only during the summer on Forest Road FDR 317, and in the winter, the tower is only reachable via snowmobile.
DOI: 10.17632/k7nvtgn2x6.3
title: Data for glomeruli characterization in histopathological images

description: The data presented hereis part of the whole slide imaging (WSI) datasets generated in European project AIDPATH. This data is also related to the research paper entitle “Glomerulosclerosis Identification in Whole Slide Images using Semantic Segmentation”, published in Computer Methods and Programs in Biomedicine Journal (DOI: 10.1016/j.cmpb.2019.105273) . In that article, different methods based on deep learning for glomeruli segmentation and their classification into normal and sclerotic glomerulous are presented and discussed. These data will encourage research on artificial intelligence (AI) methods, create and compare fresh algorithms, and measure their usability in quantitative nephropathology. Parameters for data collection: Tissue samples were collected with a biopsy needle having an outer diameter between 100μm and 300μm. Afterwards, paraffin blocks were prepared using tissue sections of 4μm and stained using Periodic acid–Schiff (PAS). Then, images at 20x magnification were selected. Description of data collection: The tissue samples were scanned at 20x with a Leica Aperio ScanScope CS scanner. Data format: DATASET_A_DIB: Raw data, original images in SVS format. DATASET_B_DIB: Classified: Detected glomeruli to be used for classification in PNG format. The data is composed of two datasets: 1.) DATASET_A: Raw data with 31 whole slide images (WSI) in SVS format. The size of the WSI range between 21651x10498 pixels and 49799 x 32359 pixles acquired at 20x. The images contain different types of glomeruli that were detected using the algorithms explained at the following article [https://doi.org/10.1016/j.cmpb.2019.105273]. The detected glomeruli are provided in DATASET_B. 2.) DATASET_B: 2,340 images with a single glomerulous, 1,170 normal glomeruli and 1,170 sclerosed glomeruli. All of them are in PNG format. Value of the Data · These data can be used for benchmarking to encourage further research on AI methods applied to digital pathology in nephrology. · The additional value of this data is that it has been acquired and evaluated by expert pathologists from different European countries. · All researches in digital pathology can benefit from these data, to test classification algorithms. And particularly for glomeruli identification in nephrology studies. · This data can be used for further development and new experiments in glomeruli classification with more classes, like focal glomeruli besides normal and sclerotic glomeruli.
DOI: 10.18140/flx/1440118
title: FLUXNET2015 US-GBT GLEES Brooklyn Tower

description: This is the FLUXNET2015 version of the carbon flux data for the site US-GBT GLEES Brooklyn Tower.
DOI: 10.18140/flx/1440195
title: FLUXNET2015 AU-Cpr Calperum

description: This is the FLUXNET2015 version of the carbon flux data for the site AU-Cpr Calperum.
DOI: 10.5281/zenodo.11617187
title: An Empirical Characterization of Anycast Convergence Time
DOI: 10.5281/zenodo.11623043
title: Data and code from: Climate-based prediction of carbon fluxes from deadwood in Australia

description: This repository contains the code for the publication 'Climate-based prediction of carbon fluxes from deadwood in Australia'.
DOI: 10.5281/zenodo.12544401
title: HTTP/3's Extensible Prioritization Scheme in the Wild - Experiment Results
DOI: 10.5281/zenodo.6514916
title: PAN 22 Author Profiling: Profiling Irony and Stereotype Spreaders on Twitter (IROSTEREO)

description: <strong>TASK</strong> With irony, language is employed in a figurative and subtle way to mean the opposite to what is literally stated. In case of sarcasm, a more aggressive type of irony, the intent is to mock or scorn a victim without excluding the possibility to hurt. Stereotypes are often used, especially in discussions about controversial issues such as immigration or sexism and misogyny. At PAN’22, we will focus on profiling ironic authors in Twitter. Special emphasis will be given to those authors that employ irony to spread stereotypes, for instance, towards women or the LGTB community. The goal will be to classify authors as ironic or not depending on their number of tweets with ironic content. Among those authors we will consider a subset that employs irony to convey stereotypes in order to investigate if state-of-the-art models are able to distinguish also these cases. Therefore, given authors of Twitter together with their tweets, the goal will be to profile those authors that can be considered as ironic. <strong>DATA</strong> <strong>Input</strong> The uncompressed dataset consists in a folder which contains: A XML file per author (Twitter user) with 200 tweets. The name of the XML file correspond to the unique author id. A truth.txt file with the list of authors and the ground truth. The format of the XML files is: <pre><code class="language-xml"> &lt;author lang="en"&gt; &lt;documents&gt; &lt;document&gt;Tweet 1 textual contents&lt;/document&gt; &lt;document&gt;Tweet 2 textual contents&lt;/document&gt; ... &lt;/documents&gt; &lt;/author&gt;</code></pre> The format of the truth.txt file is as follows. The first column corresponds to the author id. The second column contains the truth label. <pre><code> 2d0d4d7064787300c111033e1d2270cc:::I b9eccce7b46cc0b951f6983cc06ebb8:::NI f41251b3d64d13ae244dc49d8886cf07:::I 47c980972060055d7f5495a5ba3428dc:::NI d8ed8de45b73bbcf426cdc9209e4bfbc:::I 2746a9bf36400367b63c925886bc0683:::NI ...</code></pre> <strong>Evaluation</strong> The performance of your system will be ranked by accuracy. More info on the task: https://pan.webis.de/clef22/pan22-web/author-profiling.html
DOI: 10.57912/25274497
title: Open Government and US Development Cooperation in Colombia: Lessons for Locally-Led Development

description: Set of open source data used for an analysis of USAID locally-led development practices
DOI: 10.15156/bio/sh0744732.10fu
title: SH0744732.10FU

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/sh0744733.10fu
title: SH0744733.10FU

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/sh0744734.10fu
title: SH0744734.10FU

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/sh0744735.10fu
title: SH0744735.10FU

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/sh0744736.10fu
title: SH0744736.10FU

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/sh0744737.10fu
title: SH0744737.10FU

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: 10.15468/dl.bs27m7
title: Occurrence Download

description: A dataset containing 369 species occurrences available in GBIF matching the query:
{
 "or" : [
 "Geometry POLYGON((-74.04884 4.67748,-74.04913 4.67681,-74.04774 4.67603,-74.04744 4.67672,-74.04884 4.67748))",
 "Geometry POLYGON((-74.09395 4.65021,-74.09025 4.64436,-74.08884 4.64543,-74.08797 4.64658,-74.09353 4.65048,-74.09395 4.65021))"
 ]
}

 The dataset includes 369 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008793-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2se4xp
title: Occurrence Download

description: A dataset containing 3492 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhincodon typus Smith, 1828"
 ]
}

 The dataset includes 3492 records from 125 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008790-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.47vq7y
title: Occurrence Download

description: A dataset containing 578 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (CAN.7_1, CAN.4_1, CAN.10_1)",
 "OccurrenceStatus is Present",
 "TaxonKey is Psocoptera"
 ]
}

 The dataset includes 578 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008784-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7q8uct
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Apus apus (Linnaeus, 1758)",
 "Year is 77",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008785-241107131044228/datasets/export for details.
DOI: 10.15468/dl.sb9gvn
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rusa timorensis (Blainville, 1822)",
 "Country is New Caledonia",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 26 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008786-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b7g76e
title: Occurrence Download

description: A dataset containing 3109 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Hydrochoerus hydrochaeris (Linnaeus, 1766)"
 ]
}

 The dataset includes 3109 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008797-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5p5wr2
title: Occurrence Download

description: A dataset containing 3109 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Hydrochoerus hydrochaeris (Linnaeus, 1766)"
 ]
}

 The dataset includes 3109 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008796-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4yqn8a
title: Occurrence Download

description: A dataset containing 5068 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Mamíferos de Nuevo León: distribución y taxonomía"
 ]
}

 The dataset includes 5068 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008795-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uhuhre
title: Occurrence Download

description: A dataset containing 80 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((124.22702 8.33850,127.88518 8.33850,127.88518 9.80113,124.22702 9.80113,124.22702 8.33850))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Penelopides affinis Tweeddale, 1877"
 ]
}

 The dataset includes 80 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008801-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xzrgr2
title: Occurrence Download

description: A dataset containing 3492 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhincodon typus Smith, 1828"
 ]
}

 The dataset includes 3492 records from 125 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008787-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.