cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26188/28013753.v1
title: Nemabiome ITS Database

description: 18S, ITS1 and ITS2, 28S Full Nematode Database: Building a NanoCLUST Db for Parasitic Nematodes using 18S rRNA, 28S rRNA, ITS1, 5.8S and ITS2. Nematoda Taxonomy ID: 6231 (hence must use txid6231). <br>Key words:18S ribosomal RNA18S rRNA18S28S ribosomal RNA28S rRNA28S5.8S ribosomal RNA5.8S rRNA5.8SRibosomal RNASSU rRNALSU rRNASSU ribosomal RNALSU ribosomal RNAInternal transcribed spacerInternal transcribed spacer 1Internal transcribed spacer 2ITSITS1ITS2<br>Final NCBI GenBank search term:(((((((((((((((((((((((((18S ribosomal RNA[Title]) OR 18S rRNA[Title]) OR 18S[Title]) OR 28S ribosomal RNA[Title]) OR 28S rRNA[Title]) OR 28S[Title]) OR 5.8S ribosomal RNA[Title]) OR 5.8S rRNA[Title]) OR 5.8S[Title]) OR ribosomal RNA[Title]) OR SSU rRNA[Title]) OR LSU rRNA[Title]) OR SSU ribosomal RNA[Title]) OR LSU ribosomal RNA[Title]) OR Internal transcribed spacer[Title]) OR Internal transcribed spacer 1[Title]) OR Internal transcribed spacer 2[Title]) OR ITS[Title]) OR ITS1[Title]) OR ITS2[Title]) AND txid6231[Organism])) AND 200:10000[Sequence Length])) AND nuccore pubmed[Filter]) NOT unverified[Keyword]<br>Downloaded as a fasta file.<br>Next a list of clade III and V parasitic nematodes i.e. the Ascarids, Ancylostomatids, etc were obtained – these downloaded as a fasta file.Next this fasta file had the titles of the sequences changed to ‘sham’ titles to non-descript accession numbers e.g. Unidentified nematode 18S ribosomal RNA, partial sequence, <br># Simplify the headers of your database fasta file$ awk '{if($0~/^&gt;/){print $1} else {print $0}}' Nemabiome_rRNA_fasta_v5_sequences.fasta &gt; Nematoda_rRNA-ITS-5.8S_v5_30.04.24.fasta# Make a text file of all the accession numbers in the database fasta file$ awk '{if ($1~/^&gt;/) print substr($1,2)}' Nematoda_rRNA-ITS-5.8S_v5_30.04.24.fasta &gt; Nematoda_rRNA_v5_accession_ids.txt# Create a mapping table of each accession to its taxa id - takes about 10 minutes as it has to read each of the 300 million lines nucl_gb.accession2taxid$ awk -F"\t" 'BEGIN{while(getline&lt;"Nematoda_rRNA_v5_accession_ids.txt") hash[$1]=1} {if ($2 in hash) print $2,$3}' nucl_gb.accession2taxid &gt; Nematode_rRNA_v5_tax_map.txt# Make the blast database using the database fasta file for example: $ makeblastdb -in Nematoda_rRNA-ITS-5.8S_v5_30.04.24.fasta -parse_seqids -blastdb_version 5 -taxid_map Nematode_rRNA_v5_tax_map.txt -title "Nemabiome_rRNA database_v5" -out Nemabiome_rRNA_v5_db -dbtype nucl<br>Final database files produced = 10. For example Nemabiome_rRNA_v5_db.ndb, Nemabiome_rRNA_v5_db.nhr, Nemabiome_rRNA_v5_db.nin<br>These can be used by NanoCLUST e.g. in the command <br>nextflow run main.nf -profile docker --reads '/home/Public/Ps1/Lucas_Workspace/MinION_Nemabiome_ECR_Project_SUPdata/100_Sample_Comparison_96-well_Trial-4/pass/barcodes01-96/ITS-reads-amended-filtered/barcode30.tmp.inverse.pblat.fix.fastq-filt.fastq.gz' --db "db/Nemabiome_rRNA_v5_db" --tax "db" --min_read_length 700 --max_read_length 1800 --min_cluster_size 100 --polishing_reads 100 --cluster_sel_epsilon 1 --max_memory ’84.GB’ --max_cpus 12 --outdir ./Nemabiome_trial
DOI: 10.15468/dl.95yxgb
title: Occurrence Download

description: A dataset containing 4158 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cirsium nipponicum (Maxim.) Makino",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is Japan"
 ]
}

 The dataset includes 4158 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030988-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26188/28013747
title: Nemabiome 18S Database

description: 18S Db for clade I and IV nematodes: Trichinellida (NCBI:txid6329) which encompasses: Capillaridae (Capillaria, Pearsonema, etc), Trichuridae (Trichuris) & Trichinellidae (Trichinellas). Strongyloidoidea (NCBI:txid2082224) which encompasses: Rhabdias, Steinernema, Strongyloides and Parastrongyloides<br>Search terms:For Trichinellida (total reads 914)(((((((((18S ribosomal RNA[Title]) OR 18S rRNA[Title]) OR 18S[Title]) OR Ribosomal RNA[Title]) OR SSU rRNA[Title]) OR SSU ribosomal RNA[Title]) AND txid6329[Organism]) AND 200:10000[Sequence Length]) AND nuccore pubmed[Filter]) NOT unverified[Keyword]For Strongyloidoidea (total reads 627)(((((((((18S ribosomal RNA[Title]) OR 18S rRNA[Title]) OR 18S[Title]) OR Ribosomal RNA[Title]) OR SSU rRNA[Title]) OR SSU ribosomal RNA[Title]) AND txid2082224[Organism]) AND 200:10000[Sequence Length]) AND nuccore pubmed[Filter]) NOT unverified[Keyword]<br>Concatenated<br>Next a list of clade III and V parasitic nematodes i.e. the Ascarids, Ancylostomatids, etc were obtained – these downloaded as a fasta file.Next this fasta file had the titles of the sequences changed to ‘sham’ titles to non-descript accession numbers e.g. Unidentified nematode 18S ribosomal RNA, partial sequence, <br># Simplify the headers of your database fasta file$ awk '{if($0~/^&gt;/){print $1} else {print $0}}' Final_Trich_Strongy_with_Sham_reads_v2.fasta &gt; Trich_Strongy_with_unidentified_v2.fasta# Make a text file of all the accession numbers in the database fasta file$ awk '{if ($1~/^&gt;/) print substr($1,2)}' Trich_Strongy_with_unidentified_v2.fasta &gt; Trich_Strongy_with_unidentified_v2_accession_ids.txt# Create a mapping table of each accession to its taxa id - takes about 10 minutes as it has to read each of the 300 million lines nucl_gb.accession2taxid$ awk -F"\t" 'BEGIN{while(getline&lt;"Trich_Strongy_with_unidentified_v2_accession_ids.txt") hash[$1]=1} {if ($2 in hash) print $2,$3}' nucl_gb.accession2taxid &gt; Trich_Strongy_with_unidentified_v2_tax_map.txt# Make the blast database using the database fasta file for example: $ makeblastdb -in Trich_Strongy_with_unidentified_v2.fasta -parse_seqids -blastdb_version 5 -taxid_map Trich_Strongy_with_unidentified_v2_tax_map.txt -title "Trich_Strongy_with_unidentified_v2_db" -out Trich_Strongy_with_unidentified_v2_db -dbtype nucl<br>Final database files produced = 10. For example Trich_Strongy_with_unidentified_v2_db.ndb, Trich_Strongy_with_unidentified_v2_db.nhr, Trich_Strongy_with_unidentified_v2_db.nin<br>These can be used by NanoCLUST e.g. in the command <br>nextflow run main.nf -profile docker --reads '/home/Public/Ps1/Lucas_Workspace/MinION_Nemabiome_ECR_Project_SUPdata/MinION_Nemabiome_Trial-2_Trich-Capi-Strongy/pass/barcodes01-75/barcode14-allfiles.fastq.gz' --db "db/Trich_Strongy_with_unidentified_db" --tax "db" --min_read_length 600 --max_read_length 1200 --min_cluster_size 100 --cluster_sel_epsilon 1 --max_memory ’84.GB’ --max_cpus 12 --outdir ./Nemabiome_Trial
DOI: 10.26188/28013747.v1
title: Nemabiome 18S Database

description: 18S Db for clade I and IV nematodes: Trichinellida (NCBI:txid6329) which encompasses: Capillaridae (Capillaria, Pearsonema, etc), Trichuridae (Trichuris) & Trichinellidae (Trichinellas). Strongyloidoidea (NCBI:txid2082224) which encompasses: Rhabdias, Steinernema, Strongyloides and Parastrongyloides<br>Search terms:For Trichinellida (total reads 914)(((((((((18S ribosomal RNA[Title]) OR 18S rRNA[Title]) OR 18S[Title]) OR Ribosomal RNA[Title]) OR SSU rRNA[Title]) OR SSU ribosomal RNA[Title]) AND txid6329[Organism]) AND 200:10000[Sequence Length]) AND nuccore pubmed[Filter]) NOT unverified[Keyword]For Strongyloidoidea (total reads 627)(((((((((18S ribosomal RNA[Title]) OR 18S rRNA[Title]) OR 18S[Title]) OR Ribosomal RNA[Title]) OR SSU rRNA[Title]) OR SSU ribosomal RNA[Title]) AND txid2082224[Organism]) AND 200:10000[Sequence Length]) AND nuccore pubmed[Filter]) NOT unverified[Keyword]<br>Concatenated<br>Next a list of clade III and V parasitic nematodes i.e. the Ascarids, Ancylostomatids, etc were obtained – these downloaded as a fasta file.Next this fasta file had the titles of the sequences changed to ‘sham’ titles to non-descript accession numbers e.g. Unidentified nematode 18S ribosomal RNA, partial sequence, <br># Simplify the headers of your database fasta file$ awk '{if($0~/^&gt;/){print $1} else {print $0}}' Final_Trich_Strongy_with_Sham_reads_v2.fasta &gt; Trich_Strongy_with_unidentified_v2.fasta# Make a text file of all the accession numbers in the database fasta file$ awk '{if ($1~/^&gt;/) print substr($1,2)}' Trich_Strongy_with_unidentified_v2.fasta &gt; Trich_Strongy_with_unidentified_v2_accession_ids.txt# Create a mapping table of each accession to its taxa id - takes about 10 minutes as it has to read each of the 300 million lines nucl_gb.accession2taxid$ awk -F"\t" 'BEGIN{while(getline&lt;"Trich_Strongy_with_unidentified_v2_accession_ids.txt") hash[$1]=1} {if ($2 in hash) print $2,$3}' nucl_gb.accession2taxid &gt; Trich_Strongy_with_unidentified_v2_tax_map.txt# Make the blast database using the database fasta file for example: $ makeblastdb -in Trich_Strongy_with_unidentified_v2.fasta -parse_seqids -blastdb_version 5 -taxid_map Trich_Strongy_with_unidentified_v2_tax_map.txt -title "Trich_Strongy_with_unidentified_v2_db" -out Trich_Strongy_with_unidentified_v2_db -dbtype nucl<br>Final database files produced = 10. For example Trich_Strongy_with_unidentified_v2_db.ndb, Trich_Strongy_with_unidentified_v2_db.nhr, Trich_Strongy_with_unidentified_v2_db.nin<br>These can be used by NanoCLUST e.g. in the command <br>nextflow run main.nf -profile docker --reads '/home/Public/Ps1/Lucas_Workspace/MinION_Nemabiome_ECR_Project_SUPdata/MinION_Nemabiome_Trial-2_Trich-Capi-Strongy/pass/barcodes01-75/barcode14-allfiles.fastq.gz' --db "db/Trich_Strongy_with_unidentified_db" --tax "db" --min_read_length 600 --max_read_length 1200 --min_cluster_size 100 --cluster_sel_epsilon 1 --max_memory ’84.GB’ --max_cpus 12 --outdir ./Nemabiome_Trial
DOI: 10.26188/28013723
title: 18S and ITS read separation file and database

description: PBlat Read Separation<br>Sequencing data was demultiplexed using MinKNOW and fastq files for each barcode concatenated prior to downstream analysis. Because both the 18S rDNA and ITS1-to-ITS2 sequences were pooled and given the same barcode for each sample analysed these firstly had to be separated using pblat (M. Wang & Kong, 2019) and seqtk (https://github.com/lh3/seqtk). To conduct separation and binning of 18S rDNA and ITS sequences into different files a database of nematode 18S rDNA sequences was built just using the region of the 18S rDNA targeted by our primers. Using a pblat minimum score value of 50, the 18S rDNA sequences from a barcode sequencing file were compared to our pblat 18S rDNA database and extracted to form an 18S rDNA sequence file, whilst the remaining sequences were used to form an ITS1-to-ITS2 sequence file. <br>Code is<br>conda activate pblatfor i in {01..75}; doDIR=/home/Public/Ps1/Lucas_Workspace/MinION_Nemabiome_ECR_Project_SUPdata/MinION_Nemabiome_Trial-2_Trich-Capi-Strongy/pass/looping_test_on_nemabiome_trial-2REF=18S_ref_seqs_for_Pblat_v2.fastaconda run -n seqtk seqtk seq -A /home/Public/Ps1/Lucas_Workspace/MinION_Nemabiome_ECR_Project_SUPdata/MinION_Nemabiome_Trial-2_Trich-Capi-Strongy/pass/looping_test_on_nemabiome_trial-2/barcode${i}-allfiles.fastq.gz &gt; tmp.faconda run -n pblat pblat -noHead minScore=50 -threads=48 18S_ref_seqs_for_Pblat_v2.fasta tmp.fa barcode${i}.tmp.pblat.pslawk -F"\t" '{print $10 }' barcode${i}.tmp.pblat.psl | sort | uniq | wc -l &gt; barcode${i}.count.txtawk '{if ($1&gt;=50) print $10}' barcode${i}.tmp.pblat.psl | sort | uniq &gt; barcode${i}.tmp.pblat.header.txtconda run -n seqtk seqtk subseq /home/Public/Ps1/Lucas_Workspace/MinION_Nemabiome_ECR_Project_SUPdata/MinION_Nemabiome_Trial-2_Trich-Capi-Strongy/pass/looping_test_on_nemabiome_trial-2/barcode${i}-allfiles.fastq.gz barcode${i}.tmp.pblat.header.txt &gt; barcode${i}.tmp.pblat.fastqseqkit grep -v -f &lt;(seqkit seq -ni /home/Public/Ps1/Lucas_Workspace/MinION_Nemabiome_ECR_Project_SUPdata/MinION_Nemabiome_Trial-2_Trich-Capi-Strongy/pass/looping_test_on_nemabiome_trial-2/barcode${i}.tmp.pblat.fastq) /home/Public/Ps1/Lucas_Workspace/MinION_Nemabiome_ECR_Project_SUPdata/MinION_Nemabiome_Trial-2_Trich-Capi-Strongy/pass/looping_test_on_nemabiome_trial-2/barcode${i}-allfiles.fastq.gz &gt; barcode${i}.tmp.inverse.pblat.fastqdone<br>NOTE:Barcode[#].tmp.pblat.fastq = fastq file of 18S readsBarcode[#].tmp.inverse.pblat.fastq = fastq file of ITS reads and other non-18S reads<br><br><br>
DOI: 10.26188/28013723.v1
title: 18S and ITS read separation file and database

description: PBlat Read Separation<br>Sequencing data was demultiplexed using MinKNOW and fastq files for each barcode concatenated prior to downstream analysis. Because both the 18S rDNA and ITS1-to-ITS2 sequences were pooled and given the same barcode for each sample analysed these firstly had to be separated using pblat (M. Wang & Kong, 2019) and seqtk (https://github.com/lh3/seqtk). To conduct separation and binning of 18S rDNA and ITS sequences into different files a database of nematode 18S rDNA sequences was built just using the region of the 18S rDNA targeted by our primers. Using a pblat minimum score value of 50, the 18S rDNA sequences from a barcode sequencing file were compared to our pblat 18S rDNA database and extracted to form an 18S rDNA sequence file, whilst the remaining sequences were used to form an ITS1-to-ITS2 sequence file. <br>Code is<br>conda activate pblatfor i in {01..75}; doDIR=/home/Public/Ps1/Lucas_Workspace/MinION_Nemabiome_ECR_Project_SUPdata/MinION_Nemabiome_Trial-2_Trich-Capi-Strongy/pass/looping_test_on_nemabiome_trial-2REF=18S_ref_seqs_for_Pblat_v2.fastaconda run -n seqtk seqtk seq -A /home/Public/Ps1/Lucas_Workspace/MinION_Nemabiome_ECR_Project_SUPdata/MinION_Nemabiome_Trial-2_Trich-Capi-Strongy/pass/looping_test_on_nemabiome_trial-2/barcode${i}-allfiles.fastq.gz &gt; tmp.faconda run -n pblat pblat -noHead minScore=50 -threads=48 18S_ref_seqs_for_Pblat_v2.fasta tmp.fa barcode${i}.tmp.pblat.pslawk -F"\t" '{print $10 }' barcode${i}.tmp.pblat.psl | sort | uniq | wc -l &gt; barcode${i}.count.txtawk '{if ($1&gt;=50) print $10}' barcode${i}.tmp.pblat.psl | sort | uniq &gt; barcode${i}.tmp.pblat.header.txtconda run -n seqtk seqtk subseq /home/Public/Ps1/Lucas_Workspace/MinION_Nemabiome_ECR_Project_SUPdata/MinION_Nemabiome_Trial-2_Trich-Capi-Strongy/pass/looping_test_on_nemabiome_trial-2/barcode${i}-allfiles.fastq.gz barcode${i}.tmp.pblat.header.txt &gt; barcode${i}.tmp.pblat.fastqseqkit grep -v -f &lt;(seqkit seq -ni /home/Public/Ps1/Lucas_Workspace/MinION_Nemabiome_ECR_Project_SUPdata/MinION_Nemabiome_Trial-2_Trich-Capi-Strongy/pass/looping_test_on_nemabiome_trial-2/barcode${i}.tmp.pblat.fastq) /home/Public/Ps1/Lucas_Workspace/MinION_Nemabiome_ECR_Project_SUPdata/MinION_Nemabiome_Trial-2_Trich-Capi-Strongy/pass/looping_test_on_nemabiome_trial-2/barcode${i}-allfiles.fastq.gz &gt; barcode${i}.tmp.inverse.pblat.fastqdone<br>NOTE:Barcode[#].tmp.pblat.fastq = fastq file of 18S readsBarcode[#].tmp.inverse.pblat.fastq = fastq file of ITS reads and other non-18S reads<br><br><br>
DOI: 10.15468/dl.fmttkk
title: Occurrence Download

description: A dataset containing 253273 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nemipteridae"
 ]
}

 The dataset includes 253273 records from 212 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030986-241126133413365/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.28013798
title: Supplementary Material for: Regional deprivation and diabetic ketoacidosis at type 1 diabetes diagnosis in children and adolescents: International comparison among 6 countries

description: Introduction: The incidence of diabetic ketoacidosis (DKA) at the time of diagnosis of type 1 diabetes in youth varies between countries and is influenced by socioeconomic factors. We investigated the relationship between regional deprivation indices and DKA at diagnosis of type 1 diabetes in six countries.

Methods: We analyzed children 0.5 to 17.0 years old diagnosed with type 1 diabetes between 2019-2022 across six diabetes registries. Regional deprivation index within each country was standardized to compare each individual’s socioeconomic status internationally. Log-binomial regression models assessed the association between the standardized deprivation index and DKA, with sex, age group, and year as covariates.

Results: Among 21,020 children (mean age 9.08 [SD 4.19] years) the DKA rate of 36.5%. Cohorts were: Germany (n=13,561, DKA 32.5%), Italy (4,659, 42.5%), Colorado USA (1,318, 54.9%), Wales (769, 35.2%), New Zealand (407, 43.7%), and Slovenia (306, 37.6%). Deprivation was associated with increased risk of DKA in children 0.5-&lt; 6 (OR 1.16 [95% CI 1.10 - 1.23], p &lt; 0.0001) and 6-&lt;12 years of age (1.05 [1.05 – 1.11], p = 0.02). Female sex increased risk of DKA (1.06, [1.00 - 1.13], p = 0.04). The proportion of DKA was lower in 2019 than in 2020, 2021, and 2022 (each p &lt; 0.0001).

Conclusion: Deprivation was significantly associated with the risk of DKA at the diagnosis of type 1 diabetes, and DKA was more common during the pandemic years 2020-2022 than in 2019. Younger children appear to be more vulnerable to deprivation than older patients. Understanding and reducing local and demographic-specific disparities is essential for effective intervention.
DOI: 10.5281/zenodo.14405557
title: DMSP HPI dataset

description: Data of DMSP HPI (in GW)

Every DMSP satellites available during that period are used. HPI is measured from the northern hemisphere. 



HPOW: Hemispheric Power Input, units: GW
DHPOW: Error in Hemispheric Power Input, units: GW
DOI: 10.5281/zenodo.14405558
title: DMSP HPI dataset

description: Data of DMSP HPI (in GW)

Every DMSP satellites available during that period are used. HPI is measured from the northern hemisphere. 



HPOW: Hemispheric Power Input, units: GW
DHPOW: Error in Hemispheric Power Input, units: GW
DOI: 10.15468/dl.qy9jtq
title: Occurrence Download

description: A dataset containing 1652 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Occurrence evidence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Trigonella foenum-graecum L."
 ]
}

 The dataset includes 1652 records from 61 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030989-241126133413365/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.28013666
title: <b>Trends, key contributors, and emerging issues in honey and breast cancer: A bibliometric analysis from 2014 to </b><b>2024</b>

description: This study examined the relationship between honey and breast cancer. The researchers analyzed scientific publications from 2014 to 2024 to understand research trends, researcher collaborations, and key topics studied. The results showed that honey has potential as a natural agent for preventing and treating breast cancer. The study also investigated several promising areas of research, such as the use of cerium oxide nanoparticles and selenium nanoconstructs. However, further research is needed to fully understand the potential and safety of honey in breast cancer treatment.
DOI: 10.15468/dl.kp3zm8
title: Occurrence Download

description: A dataset containing 113639 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((-61.63002 12.24941,-61.6693 12.25814,-61.71607 12.22322,-61.76159 12.17084,-61.7884 12.05049,-61.8096 12.01246,-61.79339 11.99188,-61.74351 11.9819,-61.67491 11.98876,-61.61942 12.04114,-61.60071 12.1116,-61.59136 12.22322,-61.63002 12.24941))"
 ]
}

 The dataset includes 113639 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030978-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g7xjj2
title: Occurrence Download

description: A dataset containing 10940 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((138 37,149 37,149 46,138 46,138 37))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Gastropoda"
 ]
}

 The dataset includes 10940 records from 83 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030991-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r92x3n
title: Occurrence Download

description: A dataset containing 1906 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "TaxonKey is Butomus umbellatus L."
 ]
}

 The dataset includes 1906 records from 55 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030993-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wvj47a
title: Occurrence Download

description: A dataset containing 1430 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Nysius huttoni White, 1878)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1430 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030994-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2kxex5
title: Occurrence Download

description: A dataset containing 124696 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((-59.61789 13.34853,-59.66171 13.35127,-59.66581 13.29444,-59.65554 13.24309,-59.66102 13.16298,-59.64664 13.09177,-59.59734 13.06643,-59.55147 13.0411,-59.47615 13.03494,-59.40289 13.10615,-59.40563 13.16435,-59.46177 13.202,-59.54531 13.31019,-59.61789 13.34853))"
 ]
}

 The dataset includes 124696 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030983-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ctyw2u
title: Occurrence Download

description: A dataset containing 766160 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((-61.12365 10.86624,-61.37986 10.83964,-61.61367 10.79204,-61.77607 10.72063,-61.78027 10.64363,-61.65007 10.64923,-61.53946 10.60723,-61.57026 10.40423,-61.58146 10.27262,-61.64447 10.26142,-61.95387 10.07382,-61.92867 10.03462,-61.66687 10.02622,-61.06345 10.04442,-60.97245 10.14102,-60.93185 10.31042,-60.98785 10.44483,-60.96405 10.64363,-60.88985 10.77664,-60.90665 10.85784,-61.12365 10.86624))"
 ]
}

 The dataset includes 766160 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030985-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mkqh8v
title: Occurrence Download

description: A dataset containing 1051 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Occurrence evidence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Actaea cimicifuga L."
 ]
}

 The dataset includes 1051 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030996-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.