cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26893624
title: Tiller counts - 2020

description: Sampling was conducted in Kematen in Tirol, Austria, where spring barley (Hordeum vulgare L.) was grown in six fields that have had organic management for the past ten years, three in 2020 and 2021, respectively. Each field was split in half, one side being fertilized with manure and the other remaining unfertilized, as a negative control. The manure treatment was independently applied by each field’s respective owner, at a standard rate of 1.5 metric tons (1 500kg) per hectare, using manure spreaders, after tilling and pressing the soil. Four 5x5m plots per treatment were delimited, to carry out all sampling. These plots had no barriers or fences surrounding them that would impede or limit movement between the inside of the plots and the rest of the field area. Additionally, the plots were at least 5m from the field edge, to avoid edge effects. They were at least 10m way from each other along the field’s width, and between 15 to 20m along the length of the field. To avoid any spillover of treatment effects, plots were set at least 25m away from the border between the two treatments within the same field.Sampling was conducted every two weeks, with one week of sampling and one of rest. The sampling period started on the 21st of April and ended on the 14th of July in 2020, while it started on the 3rd of May and ended on the 16th of July in 2021, to encompass almost the entire crop season. This resulted in seven sampling time points for the first year and six for the second.<br>Transects were carried out along the outside border of the plots (5m), to avoid trampling on the inside, collecting 30 individual barley plant tillers to count the number of aphids per tiller.
DOI: 10.6084/m9.figshare.26893405
title: Wet Pitfall trap data - 2021

description: Sampling was conducted in Kematen in Tirol, Austria, where spring barley (Hordeum vulgare L.) was grown in six fields that have had organic management for the past ten years, three in 2020 and 2021, respectively. Each field was split in half, one side being fertilized with manure and the other remaining unfertilized, as a negative control. The manure treatment was independently applied by each field’s respective owner, at a standard rate of 1.5 metric tons (1 500kg) per hectare, using manure spreaders, after tilling and pressing the soil. Four 5x5m plots per treatment were delimited, to carry out all sampling. These plots had no barriers or fences surrounding them that would impede or limit movement between the inside of the plots and the rest of the field area. Additionally, the plots were at least 5m from the field edge, to avoid edge effects. They were at least 10m way from each other along the field’s width, and between 15 to 20m along the length of the field. To avoid any spillover of treatment effects, plots were set at least 25m away from the border between the two treatments within the same field.Sampling was conducted every two weeks, with one week of sampling and one of rest. The sampling period started on the 21st of April and ended on the 14th of July in 2020, while it started on the 3rd of May and ended on the 16th of July in 2021, to encompass almost the entire crop season. This resulted in seven sampling time points for the first year and six for the second.Wet pitfall traps, with a teaspoon of salt (5-6g) and a drop of detergent (approx. 1ml) per litre of water, remained active for four days to sample the soil surface dwelling community. A total of 168 wet pitfall trap samples were obtained in 2020 (4 sampling plots x 2 treatments x 3 fields x 7 sampling sessions) and 144 for 2021 (4 x 2 x 3 x 6 sampling sessions), from which Shannon-Weaver’s diversity was calculated for the predator and prey communities.
DOI: 10.6084/m9.figshare.26893354
title: Wet Pitfall trap data - 2020

description: Sampling was conducted in Kematen in Tirol, Austria, where spring barley (Hordeum vulgare L.) was grown in six fields that have had organic management for the past ten years, three in 2020 and 2021, respectively. Each field was split in half, one side being fertilized with manure and the other remaining unfertilized, as a negative control. The manure treatment was independently applied by each field’s respective owner, at a standard rate of 1.5 metric tons (1 500kg) per hectare, using manure spreaders, after tilling and pressing the soil. Four 5x5m plots per treatment were delimited, to carry out all sampling. These plots had no barriers or fences surrounding them that would impede or limit movement between the inside of the plots and the rest of the field area. Additionally, the plots were at least 5m from the field edge, to avoid edge effects. They were at least 10m way from each other along the field’s width, and between 15 to 20m along the length of the field. To avoid any spillover of treatment effects, plots were set at least 25m away from the border between the two treatments within the same field.Sampling was conducted every two weeks, with one week of sampling and one of rest. The sampling period started on the 21st of April and ended on the 14th of July in 2020, while it started on the 3rd of May and ended on the 16th of July in 2021, to encompass almost the entire crop season. This resulted in seven sampling time points for the first year and six for the second.Wet pitfall traps, with a teaspoon of salt (5-6g) and a drop of detergent (approx. 1ml) per litre of water, remained active for four days to sample the soil surface dwelling community. A total of 168 wet pitfall trap samples were obtained in 2020 (4 sampling plots x 2 treatments x 3 fields x 7 sampling sessions) and 144 for 2021 (4 x 2 x 3 x 6 sampling sessions), from which Shannon-Weaver’s diversity was calculated for the predator and prey communities.
DOI: 10.15468/dl.aeva6u
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Astragalus aquilanus Anzal.",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113606-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48329/am3k-vr38
title: CPRD GOLD September 2024

description: The CPRD Gold database contains longitudinal routinely-collected electronic health records (EHR data) from UK primary care practices using Vision® general practice patient management software. The database captures information on demographic characteristics; diagnoses and symptoms; drug exposures; vaccination history; laboratory tests; and referrals to hospital and specialist care. Access to anonymised data from CPRD is subject to a full licence agreement containing detailed terms and conditions of use. Anonymised patient datasets can be extracted for researchers against specific study specifications, following protocol approval. Further information is available at https://www.cprd.com/data-access.
DOI: 10.48329/zrvz-6a47
title: CPRD Aurum September 2024

description: The CPRD Aurum database contains longitudinal routinely-collected electronic health records (EHR data) from UK primary care practices using EMIS Web® general practice patient management software. The database captures information on demographic characteristics; diagnoses and symptoms; drug exposures; vaccination history; laboratory tests; and referrals to hospital and specialist care. Access to anonymised data from CPRD is subject to a full licence agreement containing detailed terms and conditions of use. Anonymised patient datasets can be extracted for researchers against specific study specifications, following protocol approval. Further information is available at https://www.cprd.com/data-access.
DOI: 10.25921/t1m3-w179
title: EK80 Water Column Sonar Data Collected During RL2107
DOI: 10.15468/dl.gtz43c
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Linum mulleri Moris, 1828",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 10 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113608-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ndru87
title: Occurrence Download

description: A dataset containing 2391 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Buceros hydrocorax Linnaeus, 1766"
 ]
}

 The dataset includes 2391 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113607-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.67f2kw
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Buceros mindanensis Hay, 1877"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113609-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rt9gp7
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Daphne petraea Leyb.",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 17 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113610-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.psc48z
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Athamanta cortiana Ferrarini",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 14 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113612-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.djh5pt
title: Occurrence Download

description: A dataset containing 9458 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Issue is one of (Geodetic datum assumed WGS84, Coordinate reprojected, Coordinate rounded)",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Helianthemum hirtum (L.) Mill., Helianthemum hirtum subsp. bethuricum Rivas Goday ex Rivas Mart., T.E.Díaz & F.Fernández-González, Helianthemum hirtum var. villosissimum Emb. & Maire, Helianthemum hirtum var. procumbens (Willk.) Rouy & Foucaud, Helianthemum hirtum var. albiflorum Gren., 1848, Helianthemum hirtum var. bethuricum Rivas Goday, Helianthemum hirtum var. calvescens Maire, Helianthemum hirtum var. aragonense Pau ex Guinea, Helianthemum hirtum var. lactiflorum Pau, Helianthemum hirtum subsp. bergevinii (Maire) Maire)"
 ]
}

 The dataset includes 9458 records from 64 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113613-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vq5kr4
title: Occurrence Download

description: A dataset containing 281 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Primula apennina Widmer",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 281 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113614-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h8c2pq
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Primula palinuri Petagna",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 32 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113615-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mwja5z
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Saudi Arabia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is one of (CC0 1.0, CC-BY-NC 4.0)",
 "TaxonKey is Capra nubiana F.Cuvier, 1825"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113616-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13619388
title: Datasets for testing the robustness of LiDAR vegetation metrics to varying point densities

description: The calculation of vegetation metrics from LiDAR point clouds might be affected by the available point density of a dataset. Testing how the same LiDAR vegetation metrics differ with different point densities can therefore inform about their robustness for upscaling metrics to other areas or other LiDAR point clouds. The datasets made available here were generated to test the robustness of LiDAR vegetation metrics to varying point densities. A total of 25 LiDAR vegetation metrics representing different aspects of vegetation height, vegetation cover and structural complexity were tested (see metric definition in Kissling et al. 2023, https://doi.org/10.1016/j.dib.2022.108798). The metric calculation was similar to the metric calculation in the Laserchicken software (Meijer et al. 2020, https://doi.org/10.1016/j.softx.2020.100626) and the Laserfarm workflow (Kissling et al. 2022, https://doi.org/10.1016/j.ecoinf.2022.101836). The Dutch AHN4 dataset from the years 2020–2022 with a point density of 20–30 points/m2 was used. A number of plots (i.e., squared polygons around centre points) were randomly placed across the Netherlands within Dutch Natura 2000 sites (using shapefiles from the European Environmental Agency). Different Dutch Natura 2000 sites were distinguished based on their dominant habitat type (dunes, grassland, marsh, shrubland, and woodland). About 100 plots were randomly placed in each habitat type. The AHN4 point cloud of each plot was clipped and then randomly downsampled to 1, 2, 5, 10, 15, 20 points per square meter, respectively. This was done for six different spatial resolutions (1, 2, 5, 10, 20 and 30 meter). The clipped points were then used to calculate the 25 LiDAR vegetation metrics for the original point density and for the six down-sampled point densities.
DOI: 10.5281/zenodo.13628041
title: Datasets for testing the robustness of LiDAR vegetation metrics to varying point densities

description: The calculation of vegetation metrics from LiDAR point clouds might be affected by the available point density of a dataset. Testing how the same LiDAR vegetation metrics differ with different point densities can therefore inform about their robustness for upscaling metrics to other areas or other LiDAR point clouds. The datasets made available here were generated to test the robustness of LiDAR vegetation metrics to varying point densities and spatial resolutions (i.e., plots of 1 × 1 m, 2 × 2 m, 5 × 5 m and 10 × 10 m size). A total of 25 LiDAR vegetation metrics representing different aspects of vegetation height, vegetation cover and structural complexity were tested (see metric definition in Kissling et al. 2023, https://doi.org/10.1016/j.dib.2022.108798). The metric calculation was similar to the metric calculation in the Laserchicken software (Meijer et al. 2020, https://doi.org/10.1016/j.softx.2020.100626) and the Laserfarm workflow (Kissling et al. 2022, https://doi.org/10.1016/j.ecoinf.2022.101836). The Dutch AHN4 dataset from the years 2020–2022 with a point density of 20–30 points/m2 was used. Initially, 100 plots (i.e., squared polygons around centre points) were randomly placed across the Netherlands in Dutch Natura 2000 sites that predominantly contain woodland habitats (using shapefiles from the European Environmental Agency). For each centre point, square polygons of the desired resolutions (i.e., 1 × 1 m, 2 × 2 m, 5 × 5 m or 10 × 10 m plot size) were generated. The square polygons were subsequently used to clip the LiDAR point clouds from the Dutch AHN4 point cloud dataset. Since not all locations of the 100 randomly placed plots contained points, the actual sample sizes were slightly smaller than 100, i.e., 94 plots for the 1 × 1 m, 2 × 2 m and 5 × 5 m resolution and 95 plots for the 10 × 10 m resolution. Metrics were calculated with the original point density of the Dutch AHN4 dataset (20–30 points/m2) and with six systematically down-sampled point clouds for the same plots (i.e., keeping 5%, 10%, 20%, 40%, 60% and 80% of the points in the original point clouds). For each clipped point cloud of a plot at a given resolution, the points were first sorted according to their GPS acquisition time (from earliest to latest). Points were then systematically discarded and only 5%, 10%, 20%, 40%, 60% and 80% of the points in the original point clouds were kept. The kept points were used for calculating the 25 LiDAR vegetation metrics.
DOI: 10.34740/kaggle/dsv/9301167
title: World Weather Repository ( Daily Updating )
DOI: 10.5281/zenodo.13628194
title: terrapene_carolina_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.13628195
title: terrapene_carolina_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.12572003
title: Datasets - Unveiling Host-Parasite Relationships through Conserved MITEs in Prokaryote and Viral Genomes

description: Title:

Unveiling Host-Parasite Relationships through Conserved MITEs in Prokaryote and Viral Genomes 

 

Authors:

Francisco Nadal-Molero(1), Riccardo Roselli(1), Silvia Garcia-Juan(1), Alicia Campos-Lopez(1), Ana-Belen Martin-Cuadrado(1*)

 

SUPPLEMENTARY FILES

 

Supplementary File S1. Sequences of cMITEs detected in Bacteria genomes (fasta format). The hosting microbial species and inferred NCBI-taxonomy are indicated in the name of each sequence. The structure of the MITE name is: “Accession|Genome|start|end|TSD|TIRlength|MITETracker_group|Lineage”.

Supplementary File S2. Sequences of cMITEs detected in the Archaea genomes (fasta format). The hosting microbial species and inferred NCBI-taxonomy are indicated in the name of each sequence. The structure of the MITE name is: “Accession|Genome|start|end|TSD|TIRlength|MITETracker_group|Lineage”.

Supplementary File S3. Sequences of vMITEs detected in the virus sequences from the NCBI and IMG/VR v.4.1 database (fasta format). Virus, microbial host (if known) and inferred NCBI-taxonomy is stated in the name of each sequence. The structure of the MITE name is: 

“Accession|Genome|start|end|TSD|TIRlength|MITETracker_group|Virus|Name|Host”.

Supplementary File S4. Sequences of si-vMITEs detected in the virus sequences from the NCBI and IMG/VR v.4.1 database (fasta format). Virus, microbial host (if known) and inferred NCBI-taxonomy are stated in the name of each sequence. The structure of the MITE name is: “Accession|Genome|start|end|Ident.Method.by.DB|Host”.

Supplementary Files S5. Cytoscape networks. (A) Figure 1A, (B) Figure 1B.

Supplementary File S6. Sequences of cMITEs obtained from 5837 genomes of Neisseriales. The structure of the MITE name is: 

“Accession|NucleotideID|start|end|TSD|TIRlength|MITETracker_group|Genome|Lineage”.

Supplementary File S7. Sequences of si-vMITEs obtained from 5837 genomes of Neisseriales. The structure of the MITE name is: “Accession|Genome|start|end|Host”.

Supplementary File S8. Sequences of cMITEs obtained from 46051 genomes of Bacteroidota. The structure of the MITE name is:

“Accession|NucleotideID|start|end|TSD|TIRlength|MITETracker_group|Genome|Lineage”.

Supplementary File S9. Sequences of si-vMITEs obtained from 46051 genomes of Bacteroidota. The structure of the MITE name is: “Accession|Genome|start|end|Host”.
DOI: 10.5281/zenodo.12572002
title: Datasets - Unveiling Host-Parasite Relationships through Conserved MITEs in Prokaryote and Viral Genomes

description: Title:

Unveiling Host-Parasite Relationships through Conserved MITEs in Prokaryote and Viral Genomes 

 

Authors:

Francisco Nadal-Molero(1), Riccardo Roselli(1), Silvia Garcia-Juan(1), Alicia Campos-Lopez(1), Ana-Belen Martin-Cuadrado(1*)

 

SUPPLEMENTARY FILES

 

Supplementary File S1. Sequences of cMITEs detected in Bacteria genomes (fasta format). The hosting microbial species and inferred NCBI-taxonomy are indicated in the name of each sequence. The structure of the MITE name is: “Accession|Genome|start|end|TSD|TIRlength|MITETracker_group|Lineage”.

Supplementary File S2. Sequences of cMITEs detected in the Archaea genomes (fasta format). The hosting microbial species and inferred NCBI-taxonomy are indicated in the name of each sequence. The structure of the MITE name is: “Accession|Genome|start|end|TSD|TIRlength|MITETracker_group|Lineage”.

Supplementary File S3. Sequences of vMITEs detected in the virus sequences from the NCBI and IMG/VR v.4.1 database (fasta format). Virus, microbial host (if known) and inferred NCBI-taxonomy is stated in the name of each sequence. The structure of the MITE name is: 

“Accession|Genome|start|end|TSD|TIRlength|MITETracker_group|Virus|Name|Host”.

Supplementary File S4. Sequences of si-vMITEs detected in the virus sequences from the NCBI and IMG/VR v.4.1 database (fasta format). Virus, microbial host (if known) and inferred NCBI-taxonomy are stated in the name of each sequence. The structure of the MITE name is: “Accession|Genome|start|end|Ident.Method.by.DB|Host”.

Supplementary Files S5. Cytoscape networks. (A) Figure 1A, (B) Figure 1B.

Supplementary File S6. Sequences of cMITEs obtained from 5837 genomes of Neisseriales. The structure of the MITE name is: 

“Accession|NucleotideID|start|end|TSD|TIRlength|MITETracker_group|Genome|Lineage”.

Supplementary File S7. Sequences of si-vMITEs obtained from 5837 genomes of Neisseriales. The structure of the MITE name is: “Accession|Genome|start|end|Host”.

Supplementary File S8. Sequences of cMITEs obtained from 46051 genomes of Bacteroidota. The structure of the MITE name is:

“Accession|NucleotideID|start|end|TSD|TIRlength|MITETracker_group|Genome|Lineage”.

Supplementary File S9. Sequences of si-vMITEs obtained from 46051 genomes of Bacteroidota. The structure of the MITE name is: “Accession|Genome|start|end|Host”.