cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4121/ec2fffe3-0bd7-4a7b-b477-74b25ad17983
title: Data and code related to the publication: "Past peak prominence: The changing role of integrated assessment modeling in the IPCC"

description: We created this dataset and scripts with the research aim to analyze the influence of Integrated Assessment Modelling (IAM) research on Intergovernmental Panel of Climate Change (IPCC) reports. We analyzed the following research questions (Table 1):1.    Which IAM researchers have been cited in IPCC WG3 reports?2.    To what extent are IPCC WG3 reports based on IAM research?2.1. What share of IPCC WG3 references are authored by any IAM researcher individually?2.2. How much of the evidence base of IPCC WG3 reports is IAM research?2.2.1.    What share of IPCC WG3 references are authored by all IAM researchers together?2.2.2.    How sensitive are the results to different sample sizes of IAM researchers?2.3. How many IAM researchers are responsible for the majority of IAM research in IPCC reports?2.4. What share of IPCC WG3 references in each chapter are authored by IAM researchers?3.    To what extent are IPCC WG3 SPM statements based on IAM research?4.    How diverse is the sample of IAM research covered in IPCC WG3 reports?<br>RQs that can be answered using the same dataset:·     Who are the most cited researchers by IPCC WG3 reports? (Section 1)·     Which chapters act as knowledge bridges in IPCC reports? Which chapters are most commonly co-cited in the SPM? Which chapters are not well covered in the SPM? (Section 3)·     What share of evidence in IPCC WG3 reports is attributable to the top 30 most cited researchers? (Section 1 & 2.3)·     What is the distribution of affiliated institutes for IAM researchers who have been cited in IPCC WG3 reports? (Section 4) RQs that can be answered by modifying the script/files:·     Who are the most cited researchers in other reports (e.g. UNEP reports)? (Section 1)·     To what extent has research from another research field (e.g. nuclear energy research) been cited in IPCC WG3 reports? (Section 2.1, 2.2 & 2.4)·     To what extent has IAM research been covered in other reports (e.g. UNEP reports)? (Section 2.1, 2.2 & 2.4)·     How diverse is the IAM research covered in IPCC WG3 reports regarding ethnicity and academic experience level? (Section 4)<br>Check the documentation file for more details.
DOI: 10.4121/ec2fffe3-0bd7-4a7b-b477-74b25ad17983.v2
title: Data and code related to the publication: "Past peak prominence: The changing role of integrated assessment modeling in the IPCC"

description: We created this dataset and scripts with the research aim to analyze the influence of Integrated Assessment Modelling (IAM) research on Intergovernmental Panel of Climate Change (IPCC) reports. We analyzed the following research questions (Table 1):1.    Which IAM researchers have been cited in IPCC WG3 reports?2.    To what extent are IPCC WG3 reports based on IAM research?2.1. What share of IPCC WG3 references are authored by any IAM researcher individually?2.2. How much of the evidence base of IPCC WG3 reports is IAM research?2.2.1.    What share of IPCC WG3 references are authored by all IAM researchers together?2.2.2.    How sensitive are the results to different sample sizes of IAM researchers?2.3. How many IAM researchers are responsible for the majority of IAM research in IPCC reports?2.4. What share of IPCC WG3 references in each chapter are authored by IAM researchers?3.    To what extent are IPCC WG3 SPM statements based on IAM research?4.    How diverse is the sample of IAM research covered in IPCC WG3 reports?<br>RQs that can be answered using the same dataset:·     Who are the most cited researchers by IPCC WG3 reports? (Section 1)·     Which chapters act as knowledge bridges in IPCC reports? Which chapters are most commonly co-cited in the SPM? Which chapters are not well covered in the SPM? (Section 3)·     What share of evidence in IPCC WG3 reports is attributable to the top 30 most cited researchers? (Section 1 & 2.3)·     What is the distribution of affiliated institutes for IAM researchers who have been cited in IPCC WG3 reports? (Section 4) RQs that can be answered by modifying the script/files:·     Who are the most cited researchers in other reports (e.g. UNEP reports)? (Section 1)·     To what extent has research from another research field (e.g. nuclear energy research) been cited in IPCC WG3 reports? (Section 2.1, 2.2 & 2.4)·     To what extent has IAM research been covered in other reports (e.g. UNEP reports)? (Section 2.1, 2.2 & 2.4)·     How diverse is the IAM research covered in IPCC WG3 reports regarding ethnicity and academic experience level? (Section 4)<br>Check the documentation file for more details.
DOI: 10.15468/dl.g9kuwe
title: Occurrence Download

description: A dataset containing 1071 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sacciolepis africana C.E.Hubb. & Snowden",
 "Continent is Africa"
 ]
}

 The dataset includes 1071 records from 70 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002269-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.um9dct
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Abies bornmuelleriana Mattf.",
 "HasCoordinate is true",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 5 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002270-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.59fmtw
title: Occurrence Download

description: A dataset containing 389 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sacciolepis chevalieri Stapf",
 "Continent is Africa"
 ]
}

 The dataset includes 389 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002271-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sf5tqx
title: Occurrence Download

description: A dataset containing 1721 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is Chinese Taipei",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Geopelia striata (Linnaeus, 1766)"
 ]
}

 The dataset includes 1721 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002273-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jhkxtv
title: Occurrence Download

description: A dataset containing 3574 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rhodiola rosea L.",
 "HasCoordinate is true",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 3574 records from 183 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002274-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4d2xga
title: Occurrence Download

description: A dataset containing 644 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sacciolepis indica (L.) Chase",
 "Continent is Africa"
 ]
}

 The dataset includes 644 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002275-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13741631
title: MASPOT panel GBS genotype data - v6.1 reference

description: Genotype-by-sequencing data of the MASPOT panel clones (762 clones in total, 755 with phenotypes) generated by Illumina sequencing of leaf tissue cf. (Sverrisdottir et al., 2017). Biallelic variants have been called relative to the v6.1 Phureja double monoploid reference genome. The SNPs have been filtered to root mean square mapping quality of > 30, MAF > 1 %, missing data < 50 %, and minimum reading depth of 5x. This leaves 175700 variants. 

This does not include the GBS data of the 18 MASPOT parent lines. These are in preparation. 

 

Please see the readme.txt file for explanation of the content of each file. 

For our analysis, we are using the FILT3 filtration settings (outlined above) and discrete scale genotypes.
DOI: 10.5281/zenodo.13766792
title: MASPOT panel GBS genotype data - v6.1 reference

description: Genotype-by-sequencing data of the MASPOT panel clones (762 clones in total, 755 with phenotypes) generated by Illumina sequencing of leaf tissue cf. (Sverrisdottir et al., 2017). Biallelic variants have been called relative to the v6.1 Phureja double monoploid reference genome. The SNPs have been filtered to root mean square mapping quality of > 30, MAF > 1 %, missing data < 50 %, and minimum reading depth of 5x. This leaves 175435 variants. 

This does not include the GBS data of the 18 MASPOT parent lines. These are in preparation. 

F1 sample names are in HEADER.SAMPLES, SNP identifyer and coordinates are in FILT3.KEY (both outlined in the new readme.txt. The genotypes are in the SNP_V1.0_DMv6.vcf.FILT3_FINAL.DISC.gz file. The snp_counts.txt is a file of the SNP counts in the full sets of filtration we have computed. For our purpose, we use only the discrete genotypes and the FILT3 filtration in version 1. This has the 175435 variants. 

Please see the readme.txt file for explanation of the content of each file. 

For our analysis, we are using the FILT3 filtration settings (outlined above) and discrete scale genotypes.
DOI: 10.5281/zenodo.13889176
title: MASPOT panel GBS genotype data - v6.1 reference

description: Genotype-by-sequencing data of the MASPOT panel clones (762 clones in total, 755 with phenotypes) generated by Illumina sequencing of leaf tissue cf. (Sverrisdottir et al., 2017). Biallelic variants have been called relative to the v6.1 Phureja double monoploid reference genome. The SNPs have been filtered to root mean square mapping quality of > 30, MAF > 1 %, missing data < 50 %, and minimum reading depth of 5x. This leaves 175435 variants. 

F1 sample names are in HEADER.SAMPLES, SNP identifyer and coordinates are in FILT3.KEY (both outlined in the new readme.txt. The genotypes are in the SNP_V1.0_DMv6.vcf.FILT3_FINAL.DISC.gz file. The snp_counts.txt is a file of the SNP counts in the full sets of filtration we have computed. For our purpose, we use only the discrete genotypes and the FILT3 filtration in version 1. This has the 175435 variants. 

The phased MASPOT parent genotypes are in the MASPOT_phased_genome_FILT3_modified_filtered.vcf.gz file. These are the genotype calls for the 18 MASPOT parents, phased to the long read sequencing files using WhatsHap. They are filtered to 1) SNPs passing the quality the FILT3 quality filters, 2) SNPs that are also called for the F1 panel, and 3) SNPs where all parents are phased. The latter, to allow imputation of all offspring. This leaves a subset of 112720 SNPs. 

Please see the readme.txt file for explanation of the content of each file. 

For our analysis, we are using the FILT3 filtration settings (outlined above) and discrete scale genotypes.
DOI: 10.5281/zenodo.13741630
title: MASPOT panel GBS genotype data - v6.1 reference

description: Genotype-by-sequencing data of the MASPOT panel clones (762 clones in total, 755 with phenotypes) generated by Illumina sequencing of leaf tissue cf. (Sverrisdottir et al., 2017). Biallelic variants have been called relative to the v6.1 Phureja double monoploid reference genome. The SNPs have been filtered to root mean square mapping quality of > 30, MAF > 1 %, missing data < 50 %, and minimum reading depth of 5x. This leaves 175435 variants. 

F1 sample names are in HEADER.SAMPLES, SNP identifyer and coordinates are in FILT3.KEY (both outlined in the new readme.txt. The genotypes are in the SNP_V1.0_DMv6.vcf.FILT3_FINAL.DISC.gz file. The snp_counts.txt is a file of the SNP counts in the full sets of filtration we have computed. For our purpose, we use only the discrete genotypes and the FILT3 filtration in version 1. This has the 175435 variants. 

The phased MASPOT parent genotypes are in the MASPOT_phased_genome_FILT3_modified_filtered.vcf.gz file. These are the genotype calls for the 18 MASPOT parents, phased to the long read sequencing files using WhatsHap. They are filtered to 1) SNPs passing the quality the FILT3 quality filters, 2) SNPs that are also called for the F1 panel, and 3) SNPs where all parents are phased. The latter, to allow imputation of all offspring. This leaves a subset of 112720 SNPs. 

Please see the readme.txt file for explanation of the content of each file. 

For our analysis, we are using the FILT3 filtration settings (outlined above) and discrete scale genotypes.
DOI: 10.5281/zenodo.14054003
title: MASPOT panel GBS genotype data - v6.1 reference

description: Genotype-by-sequencing data of the MASPOT panel clones (762 clones in total, 755 with phenotypes) generated by Illumina sequencing of leaf tissue cf. (Sverrisdottir et al., 2017). Biallelic variants have been called relative to the v6.1 Phureja double monoploid reference genome. The SNPs have been filtered to root mean square mapping quality of > 30, MAF > 1 %, missing data < 50 %, and minimum reading depth of 5x. This leaves 175435 variants. 

F1 sample names are in HEADER.SAMPLES, SNP identifyer and coordinates are in FILT3.KEY (both outlined in the new readme.txt. The genotypes are in the SNP_V1.0_DMv6.vcf.FILT3_FINAL.DISC.gz file. The snp_counts.txt is a file of the SNP counts in the full sets of filtration we have computed. For our purpose, we use only the discrete genotypes and the FILT3 filtration in version 1. This has the 175435 variants. 

The phased MASPOT parent genotypes are in the MASPOT_phased_genome_FILT3_modified_filtered.vcf.gz file. These are the genotype calls for the 18 MASPOT parents, phased to the long read sequencing files using WhatsHap. They are filtered to 1) SNPs passing the quality the FILT3 quality filters, 2) SNPs that are also called for the F1 panel, and 3) SNPs where all parents are phased. The latter, to allow imputation of all offspring. This leaves a subset of 112720 SNPs. 

Please see the readme.txt file for explanation of the content of each file. 

For our analysis, we are using the FILT3 filtration settings (outlined above) and discrete scale genotypes.
DOI: 10.15468/dl.ydmvfx
title: Occurrence Download

description: A dataset containing 394 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Setaria magna, Setaria magna, Setaria magna, Setaria magna Grisebach, Setaria magna Griseb., Setaria magna Griseb., Setaria magna Griseb., Setaria magna Griseb., Setaria magna Griseb., Setaria magna Griseb., Setaria magna Griseb., Setaria magna Griseb., Setaria magna Griseb., 161112906, Setaria magna Griseb., 1864, Setaria magna Griseb., 1864, Setaria magna Griseb., 1864, 161112908, Chamaeraphis magna (Griseb.) Beal, Chaetochloa magna (Griseb.) Scribn., Chamaeraphis magna (Griseb.) Beal, Chaetochloa magna (Griseb.) Scribn., Chamaeraphis magna (Griseb.) Beal, Chamaeraphis magna (Griseb.) Beal, Chamaeraphis magna (Griseb.) Beal, 1896, Chaetochloa magna (Griseb.) Scribn., 1897, 161112907, Chamaeraphis magna (Griseb.) Beal, Chaetochloa magna (Griseb.) Scribn., Chaetochloa magna (Griseb.) Scribn., Chaetochloa magna (Griseb.) Scribn., Chaetochloa magna (Griseb.) Scribn., Chaetochloa magna (Griseb.) Scribn., Chamaeraphis magna (Griseb.) Beal)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 394 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002276-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0182013.10fu
title: SH0182013.10FU

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/sh0182014.10fu
title: SH0182014.10FU

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/sh0182015.10fu
title: SH0182015.10FU

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/sh0182016.10fu
title: SH0182016.10FU

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/sh0182017.10fu
title: SH0182017.10FU

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/sh0182018.10fu
title: SH0182018.10FU

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.