cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.60893/figshare.apl.28578317
title: Dataset 1

description: The original dataset for the upper panel of Figure 2(a) (temperature monitoring) in the main text. Each row in this file denotes the simulated value of imbalance for a given p-bit readout number N and different temperature changes δT. From the first row to the last, N=1Mb, 1Gb, 1Tb respectively. We presume the reference temperature T=300K in this simulation, and the temperature variation δT in the i-th column is -10^(1.5-0.05(i-1))K, and we plotted only the i=1,4,7,... points in Figure 2(a) for clarity. We use negative δT to ensure positive δn_T (except the undetectable cases where thermal fluctuation can change the sign of δn_T).
DOI: 10.60893/figshare.apl.28578317.v1
title: Dataset 1

description: The original dataset for the upper panel of Figure 2(a) (temperature monitoring) in the main text. Each row in this file denotes the simulated value of imbalance for a given p-bit readout number N and different temperature changes δT. From the first row to the last, N=1Mb, 1Gb, 1Tb respectively. We presume the reference temperature T=300K in this simulation, and the temperature variation δT in the i-th column is -10^(1.5-0.05(i-1))K, and we plotted only the i=1,4,7,... points in Figure 2(a) for clarity. We use negative δT to ensure positive δn_T (except the undetectable cases where thermal fluctuation can change the sign of δn_T).
DOI: 10.60893/figshare.apl.28578320
title: Dataset 2

description: The original dataset for the upper panel of Figure 2(b) (timekeeping). From the first row to the last, N=1Mb, 1Gb, 1Tb. The measured time elapse in the i-th column is t/τ=10^(-4+0.08(i-1)).
DOI: 10.60893/figshare.apl.28578320.v1
title: Dataset 2

description: The original dataset for the upper panel of Figure 2(b) (timekeeping). From the first row to the last, N=1Mb, 1Gb, 1Tb. The measured time elapse in the i-th column is t/τ=10^(-4+0.08(i-1)).
DOI: 10.60893/figshare.apl.28578323
title: Dataset 3

description: The original dataset for the upper panel of Figure 2(c) (field sensing). The corresponding number of readouts N in each cell is the same as that in Dataset 1, and the field strength B in the i-th column is 10^(-1-0.08(i-1))T.
DOI: 10.60893/figshare.apl.28578323.v1
title: Dataset 3

description: The original dataset for the upper panel of Figure 2(c) (field sensing). The corresponding number of readouts N in each cell is the same as that in Dataset 1, and the field strength B in the i-th column is 10^(-1-0.08(i-1))T.
DOI: 10.60893/figshare.apl.28578326
title: Dataset 4

description: The original dataset for the 0%, 10%, 20% and 30% device variation cases in Figure 3(a) (temperature monitoring). In the simulation we assume T=300K, and the detectivity is estimated by the smallest detectable |δT| value in a geometric sequence ranging from 10K to 1mK, with a common ratio 10^(-0.1)K. The detectivity values are only used for qualitative observation and the significant digits do not count.
DOI: 10.60893/figshare.apl.28578326.v1
title: Dataset 4

description: The original dataset for the 0%, 10%, 20% and 30% device variation cases in Figure 3(a) (temperature monitoring). In the simulation we assume T=300K, and the detectivity is estimated by the smallest detectable |δT| value in a geometric sequence ranging from 10K to 1mK, with a common ratio 10^(-0.1)K. The detectivity values are only used for qualitative observation and the significant digits do not count.
DOI: 10.60893/figshare.apl.28578329
title: Dataset 5

description: The original dataset for the 0%, 10%, 20% and 30% device variation cases in Figure 3(b) (field sensing). In the simulation, the field strength used to evaluate detectivity is geometrically decreased from 0.1T to 1nT with a common ratio 10^(-0.08)T.
DOI: 10.60893/figshare.apl.28578329.v1
title: Dataset 5

description: The original dataset for the 0%, 10%, 20% and 30% device variation cases in Figure 3(b) (field sensing). In the simulation, the field strength used to evaluate detectivity is geometrically decreased from 0.1T to 1nT with a common ratio 10^(-0.08)T.
DOI: 10.34808/dcdd-ga94
title: Raw data for publication DOI:10.1002/celc.202400459

description: - raw data of the results presented in the published manuscript 
- raw data of electrochemical measurements
DOI: 10.15468/dl.e6mj6a
title: Occurrence Download

description: A dataset containing 1206 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Erwinia amylovora (Burrill, 1882) Winslow et al., 1920"
 ]
}

 The dataset includes 1206 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000372-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.udwxde
title: Occurrence Download

description: A dataset containing 2975 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Tetrápodos de Uruguay"
 ]
}

 The dataset includes 2975 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000373-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2gwwqa
title: Occurrence Download

description: A dataset containing 478 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Pelodiscus maackii (Brandt, 1858)"
 ]
}

 The dataset includes 478 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000376-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mytvdm
title: Occurrence Download

description: A dataset containing 221446 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-71.69738 4.07203,-70.06491 4.26964,-69.86831 5.66937,-70.59004 5.64287,-71.87768 5.70973,-71.69738 4.07203))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Chordata"
 ]
}

 The dataset includes 221446 records from 113 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000357-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r4u96b
title: Occurrence Download

description: A dataset containing 3723 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Venturia inaequalis (Cooke) G.Winter"
 ]
}

 The dataset includes 3723 records from 149 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000377-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.stzyv2
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Material sample)",
 "Country is China",
 "OccurrenceStatus is Present",
 "StateProvince is 浙江",
 "TaxonKey is Shibataea Makino ex Nakai"
 ]
}

 The dataset includes 10 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000378-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/vsspfzh9m5.1
title: Supplemental Figures and Tables for JAAD manuscript titled "Treatment of Livedoid vasculopathy with Sulodexide: a prospective study of 33 cases."

description: Supplemental Figures and Tables for JAAD manuscript titled "Treatment of Livedoid vasculopathy with Sulodexide: a prospective study of 33 cases."
DOI: 10.15468/dl.naxpdg
title: Occurrence Download

description: A dataset containing 1279 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Podosphaera leucotricha (Ellis & Everh.) E.S.Salmon"
 ]
}

 The dataset includes 1279 records from 102 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000379-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r8wxym
title: Occurrence Download

description: A dataset containing 529 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Serrasalmus brandtii"
 ]
}

 The dataset includes 529 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061507-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18742/30231460
title: Gender Equality Index UK (GEIUK) - Domain and index scores

description: The Gender Equality Index UK (GEIUK) offers a pioneering data tool to measure and map women’s and men’s socioeconomic outcomes across local authorities in England, Wales, Scotland and Northern Ireland. Drawing on existing data from 2021 to 2023, it captures 19 outcomes in six key domains: <i>Paid Work</i>, <i>Unpaid Work</i>, <i>Money</i>, <i>Power & Participation</i>, <i>Education</i> and <i>Health</i>.This file contains the calculated GEIUK scores and deciles for the 372 local authorities in the UK. The GEIUK is made up of three measures to investigate gender and geographical differences in women’s and men’s socioeconomic outcomes:- The Gender Equality Measure: Gaps between women and men locally, irrespective of the direction of (dis)advantage.- The Women’s Outcomes Measure: Gaps between women locally compared to women nationally.- The Men’s Outcomes Measure: Gaps between men locally compared to men nationally.
DOI: 10.18742/30231460.v2
title: Gender Equality Index UK (GEIUK) - Domain and index scores

description: The Gender Equality Index UK (GEIUK) offers a pioneering data tool to measure and map women’s and men’s socioeconomic outcomes across local authorities in England, Wales, Scotland and Northern Ireland. Drawing on existing data from 2021 to 2023, it captures 19 outcomes in six key domains: <i>Paid Work</i>, <i>Unpaid Work</i>, <i>Money</i>, <i>Power & Participation</i>, <i>Education</i> and <i>Health</i>.This file contains the calculated GEIUK scores and deciles for the 372 local authorities in the UK. The GEIUK is made up of three measures to investigate gender and geographical differences in women’s and men’s socioeconomic outcomes:- The Gender Equality Measure: Gaps between women and men locally, irrespective of the direction of (dis)advantage.- The Women’s Outcomes Measure: Gaps between women locally compared to women nationally.- The Men’s Outcomes Measure: Gaps between men locally compared to men nationally.
DOI: 10.18742/30231460.v1
title: Gender Equality Index UK (GEIUK) - Domain and index scores

description: The Gender Equality Index UK (GEIUK) offers a pioneering data tool to measure and map women’s and men’s socioeconomic outcomes across local authorities in England, Wales, Scotland and Northern Ireland. Drawing on existing data from 2021 to 2023, it captures 19 outcomes in six key domains: <i>Paid Work</i>, <i>Unpaid Work</i>, <i>Money</i>, <i>Power & Participation</i>, <i>Education</i> and <i>Health</i>.This file contains the calculated GEIUK scores and deciles for the 372 local authorities in the UK. The GEIUK is made up of three measures to investigate gender and geographical differences in women’s and men’s socioeconomic outcomes:- The Gender Equality Measure: Gaps between women and men locally, irrespective of the direction of (dis)advantage.- The Women’s Outcomes Measure: Gaps between women locally compared to women nationally.- The Men’s Outcomes Measure: Gaps between men locally compared to men nationally.
DOI: 10.15156/bio/sh1159348.10fu
title: SH1159348.10FU

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/sh1159355.10fu
title: SH1159355.10FU

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/sh1159358.10fu
title: SH1159358.10FU

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/sh1159375.10fu
title: SH1159375.10FU

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/sh1159378.10fu
title: SH1159378.10FU

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/sh1159379.10fu
title: SH1159379.10FU

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/sh1159381.10fu
title: SH1159381.10FU

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/sh1159384.10fu
title: SH1159384.10FU

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/sh1159385.10fu
title: SH1159385.10FU

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/sh1159388.10fu
title: SH1159388.10FU

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.