Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
453
datasets available to search
ShareScore release 0.9.0
Dataset results
453 results for “respectability”
Dataset Clinical Epilepsy iEEG to BIDS -RESPect_intraoperative_iEEG
Open the record for dataset details and reuse information.
Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG
Open the record for dataset details and reuse information.
Respectable Standards of Living: The Alternative Lens of Maintenance Costs, Britain 1270-1860
<p>Data set and code book. Replication materials for paper accepted in Economic History Review, April 2024.</p> <p>Abstract </p> <p><span>This paper argues that in all societies there is considerable agreement about what goods and services are needed to provide a decent living, and that this standard can be measured by the expense involved in maintaining people of good standing.<span> </span><span> </span>Maintenance costs include two components of living costs that are neglected in conventional approaches.<span> </span>First, in contrast to the usual focus on a fixed basket of commodities, maintenance costs capture changes in the composition and quality of the goods required for a respectable lifestyle.<span> </span>Second, unlike the conventional accounting they include the costs of the household services required to turn the basket commodities into livings. Ignored in the conventional methodology, the inclusion of these costs represents a core innovation. More than 4600 observations, drawn mainly from primary sources, trace levels and trends in maintenance costs for Britain, 1270-1860. <span> </span>These can be compared with established cost of living indicators to offer a complementary perspective on real consumption that accommodates aspirational goods and the input of household labour.<span> </span>The struggle to support families at respectable standards emerges as driving industriousness and motivating prudence among a class that played a major role in economic development.<span> </span><span> </span></span></p> <p> </p>
Contribution of CO2 and CH4 emissions at ice-melt to annual emissions from 450 and 270 lakes, respectively, 1986 to 2014
The ice-covered period on lakes in the northern hemisphere can be extensive, lasting up to 7 months of the year. During this time, C cycling in lakes is altered affecting CO2 and CH4 dynamics below ice. Lake ice impedes atmospheric exchange, trapping CO2 and CH4 in the lake over winter. As lake ice-melts, CO2 and CH4 that has accumulated over winter is emitted from the into the atmosphere. To investigate the importance of CO2 and CH4 emissions during the ice-melt period, we conducted a literature search for studies that had CO2 and CH4 emission estimates for both the ice-melt and open water period. From these literature values, we could calculate the percent contribution of the ice-melt period to annual CO2 and CH4 emissions. We obtained data for 271 (n= 258) and 447 (n= 689) individual lakes, for CH4 and CO2, respectively.
AFLP and MS-AFLP data for Spartina alterniflora and Borrichia frutescens collected from three habitats (i.e. low, medium, and high salt) within five sites, respectively, on Sapelo Island, GA in May 2011
Using amplified fragment length polymorphism (AFLP) and methylation sensitive (MS)-AFLP we assessed genetic and epigenetic variation in two salt marsh perennials, Spartina alterniflora and Borrichia frutescens, in Sapelo Island, Georgia. We sampled Apex (A), Cabretta (C), Hunt Camp (H), Lighthouse (L), and Marsh Landing (M) for S. alterniflora, and C, H, L, M, and Shell Hammock for B. frutescens due to site specific differences in species among the sites. We tested the hypothesis that populatation structure at the habitat level would be due to epigenetic loci and not genetic. The presence and absence AFLP bands and MS-AFLP methylation respresents a genome-wide snapsnot of variation within individuals. We used hierarchical AMOVAs, permutational MANOVA, Bayesian clustering (genetic only), Mantel and partial Mantel tests, and generalized linear models to assess the spatial structure of genetic and epigenetic variation among our two study organisms across five sites for each organism. (Note: genetic and habitat distance tables were normalized for database compatibility. These data must be formatted as a square dissimilarity matrix for input to the code files.)
Accepted Artifact for Privacy-Respecting Type Error Telemetry at Scale
<p>This artifact packages the data for the paper: <em>Privacy-Respecting Type Error Telemetry at Scale</em></p> <p>There are two files on Zenodo:</p> <ul> <li>data.tar.gz has the original Luau telemetry data</li> <li>artifact.tar.gz has a result PDF, intermediate data, and scripts for processing the data</li> </ul> <p>The artifact code and the source for the paper are also on GitHub:</p> <ul> <li><a href="https://github.com/bennn/luau-telemetry">https://github.com/bennn/luau-telemetry</a></li> </ul> <p>This artifact is primarily a **dataset**. It shows how we reached the conclusions in the paper.</p> <p>The scripts in this artifact are provided as-is for completeness. They may have bugs. They may not work as advertised.</p>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - United Kingdom in respect of Northern Ireland
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_XI - Department of Agriculture, Environment and Rural Affairs</li> <li>TSE_2022_XI - Department of Agriculture, Environment and Rural Affairs</li> <li>TSE_2021_XI - Department of Agriculture, Environment and Rural Affairs</li> </ul> <p> </p>
Genome assemblies and respective wg/cgMLST profiles of a diverse dataset comprising 3,076 Campylobacter jejuni isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 2,794-loci whole-genome (wg) Multiple Locus Sequence Type (MLST) profiles [INNUENDO schema (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>) available in <a href="https://chewbbaca.online/species/4/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 3,076 <em>Campylobacter jejuni </em>samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of Sequence Type [ST]). In total, 476 different STs are represented in this dataset, with ST21, ST50, ST48, ST45 and ST257 being the most represented ones and, together, corresponding to 29.1% of the dataset.</p> <p>File “Cj_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Cj_profiles_wgMLST.tsv” corresponds to a tab separated file with the 2,794-loci wgMLST profiles of each solate presented in the metadata file. The files “profiles/Cj_profiles_cgMLST_95.tsv”, “profiles/Cj_profiles_cgMLST_98.tsv” and “profiles/Cj_profiles_cgMLST_100.tsv” correspond to a 1,012-loci, 987-loci and 29-loci cgMLST profiles of each isolate presented in the metadata file, respectively. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>C. jejuni</em> genome assemblies, we collected information about the genetic diversity (serotype) of the isolates available at <a href="https://pubmlst.org/organisms/campylobacter-jejunicoli">PubMLST</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 3,539 samples. The majority of them are associated with the INNUENDO project (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>). The remaining ones are associated with five BioProjects (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB31119">PRJEB31119</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB38253">PRJEB38253</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB40238">PRJEB40238</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB4165">PRJEB4165</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA350537">PRJNA350537</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 3,076 isolates passed this curation step and were included in the final dataset. wgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 2,794-loci INNUENDO schema available in <a href="https://chewbbaca.online/species/4">chewie-NS</a> (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>; <a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on May 31st, 2022. Three cgMLST schemas were obtained with <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> v1.0.0 (<a href="https://www.researchsquare.com/article/rs-1404655/v1">Mixão et al. 2022</a>) using the 2,794-loci wgMLST profiles of the 3,076 isolates as input and setting distinct “--site-inclusion” thresholds: 0.95, 0.98 and 1.0 (i.e., keep schema loci called in at least 95%, 98% and 100% of the samples, resulting in a 1,012-loci, 987-loci and 29-loci allelic matrices, respectively).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Genome assemblies and respective wg/cgMLST profiles of a diverse dataset comprising 1,999 Escherichia coli isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 7,601-loci whole-genome (wg) Multiple Locus Sequence Type (MLST) profiles [INNUENDO schema (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>) available in <a href="https://chewbbaca.online/species/5/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 1,999 <em>Escherichia coli </em>samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of serotype). In total, 411 different serotypes are represented in this dataset, with O157:H7 being the most represented one, corresponding to 37.1% of the dataset.</p> <p>File “Ec_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST and serotype.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Ec_profiles_wgMLST.tsv” corresponds to a tab separated file with the 7,601-loci wgMLST profiles of each isolate presented in the metadata file. The files “profiles/Ec_profiles_cgMLST_95.tsv”, “profiles/Ec_profiles_cgMLST_98.tsv” and “profiles/Ec_profiles_cgMLST_100.tsv” correspond to a 2,826-loci, 2,704-loci and 465-loci cgMLST profiles of each isolate presented in the metadata file, respectively. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>E. coli </em>genome assemblies, we collected information about the genetic diversity (serotype) of the isolates available at <a href="https://enterobase.warwick.ac.uk/species/index/ecoli">Enterobase</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 2,688 samples associated with three BioProjects (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA230969">PRJNA230969</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB27020">PRJEB27020</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA248042">PRJNA248042</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 1,999 isolates passed this curation step and were included in the final dataset. In-silico serotyping was performed with <a href="https://github.com/B-UMMI/seq_typing">seq_typing</a> v2.2. wgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 7,601-loci INNUENDO schema available in <a href="https://chewbbaca.online/species/5">chewie-NS</a> (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>; <a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on May 31st, 2022. Three cgMLST schemas were obtained with <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> v1.0.0 (<a href="https://www.researchsquare.com/article/rs-1404655/v1">Mixão et al. 2022</a>) using the 7,601-loci wgMLST profiles of the 1,999 isolates as input and setting distinct “--site-inclusion” thresholds: 0.95, 0.98 and 1.0 (i.e., keep schema loci called in at least 95%, 98% and 100% of the samples, resulting in a 2,826-loci, 2,704-loci and 465-loci allelic matrices, respectively).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Genome assemblies and respective wg/cgMLST profiles of a diverse dataset comprising 1,434 Salmonella enterica isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 8,558-loci whole-genome (wg) Multiple Locus Sequence Type (MLST) profiles [INNUENDO schema (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>) available in <a href="https://chewbbaca.online/species/8/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 1,434 <em>Salmonella enterica </em>samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of serotype). In total, 125 different serotypes are represented in this dataset, with Typhimurium (including monophasic), Enteritidis and Infantis being the most represented ones and, together, corresponding to 56.2% of the dataset.</p> <p>File “Se_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST and serotype.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Se_profiles_wgMLST.tsv” corresponds to a tab separated file with the 8,558-loci wgMLST profiles of each isolate presented in the metadata file. The files “profiles/Se_profiles_cgMLST_95.tsv”, “profiles/Se_profiles_cgMLST_98.tsv” and “profiles/Se_profiles_cgMLST_100.tsv” correspond to a 3,261-loci, 3,179-loci and 874-loci cgMLST profiles of each isolate presented in the metadata file, respectively. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>S. enterica</em> genome assemblies, we collected information about the genetic diversity (serotype) of the isolates available at <a href="https://enterobase.warwick.ac.uk/species/index/senterica">Enterobase</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 1,779 samples associated with four BioProjects (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB16326">PRJEB16326</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB20997">PRJEB20997</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB30335">PRJEB30335</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB39988">PRJEB39988</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 1,434 isolates passed this curation step and were included in the final dataset. In-silico serotyping was performed with SeqSero2 v1.2.1 (<a href="https://pubmed.ncbi.nlm.nih.gov/31540993/">Zhang et al. 2019</a>). wgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 8,558-loci INNUENDO schema available in <a href="https://chewbbaca.online/species/8">chewie-NS</a> (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>; <a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on May 31<sup>st</sup>, 2022. Three cgMLST schemas were obtained with <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> v1.0.0 (<a href="https://www.researchsquare.com/article/rs-1404655/v1">Mixão et al. 2022</a>) using the 8,558-loci wgMLST profiles of the 1,434 isolates as input and setting distinct “--site-inclusion” thresholds: 0.95, 0.98 and 1.0 (i.e., keep schema loci called in at least 95%, 98% and 100% of the samples, resulting in a 3,261-loci, 3,179-loci and 874-loci allelic matrices, respectively).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Figs. 1-6. Pygophore dorsal and ventral views respectively. 1-2 N in New Species of Neotibilis Grazia & Barcellos (Hemiptera: Pentatomidae: Pentatomini)
Figs. 1-6. Pygophore dorsal and ventral views respectively. 1-2 N. (N.) claviformis sp. nov., 3-4, N. (N.) ecuadorensis sp. nov. 5-6, N. (N.) manauara sp. nov. (dr = dorsal rim; par = paramere; vr = ventral rim; X = proctiger). Scale 1mm.
Text-fig. 3. Progonomys morganae molars from the upper part of the Nagri Formation. a) YGSP 33180, holotype, from Y 450 and b) 54091 from Y 450; both left M1 with buccal side upward. c) 34159 from Y259; and d) 36168 from Y 311, left and right m1, respectively, with lingual side upward. Scale for all is 1 mm. in Early Late Miocene Murine Rodents From The Upper Part Of The Nagri Formation, Siwalik Group, Pakistan, With A New Fossil Calibration Point For The Tribe Apodemurini (Apodemus/Tokudaia)
Text-fig. 3. Progonomys morganae molars from the upper part of the Nagri Formation. a) YGSP 33180, holotype, from Y 450 and b) 54091 from Y 450; both left M1 with buccal side upward. c) 34159 from Y259; and d) 36168 from Y 311, left and right m1, respectively, with lingual side upward. Scale for all is 1 mm.
Fig.ç2.Ec hinoderes ohtsukai sp. nov., camera lucida drawings. A, B, Holotype, male (ZIHU 3976), entire animal, dorsal and ventral view, respectively; C, D, allotype, female (ZIHU 3977), segments 9–11, dorsal and ventral view, respectively. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; lts, lateral terminal spine; lvt, lateroventral tubule; mds, middorsal spine; ne, neck; ps, penile spine; rss, rounded sensory spot; si, sieve plate. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç2.Ec hinoderes ohtsukai sp. nov., camera lucida drawings. A, B, Holotype, male (ZIHU 3976), entire animal, dorsal and ventral view, respectively; C, D, allotype, female (ZIHU 3977), segments 9–11, dorsal and ventral view, respectively. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; lts, lateral terminal spine; lvt, lateroventral tubule; mds, middorsal spine; ne, neck; ps, penile spine; rss, rounded sensory spot; si, sieve plate.
Results from the monitoring of veterinary medicinal product residues and other substances in live animals and animal products - United Kingdom in respect of Northern Ireland
<p>This dataset contains the monitoring results of veterinary medicinal product residues and other substances measured in live animals and animal products analysed by the national competent authority of United Kingdom in respect of Northern Ireland. The presence of unauthorised substances, residues of veterinary medicinal products in food may pose a risk factor for public health.</p> <p>For this reason and in order to ensure a high level of consumer protection, a comprehensive legislative framework has been established in the European Union (EU) which defines maximum limits permitted in food and monitoring programmes for the control of the presence of these substances in the food chain. Regulation (EU) No 37/2010 establishes maximum limits for residues of veterinary medicinal products in food-producing animals and animal products. Maximum residue levels for pesticides in or on food and feed of plant and animal origin are laid down in Regulation (EC) No 396/2005. Commission Implementing Regulation (EU) 2022/1646 lays down practical arrangements for and specific content of official controls of the use of veterinary medicinal products in live animals and products of animal origin through three different official national control plans: a national risk-based control plan for production in the Member States, a national randomised surveillance plan for production in the Member States and a national risk-based control plan for third-country imports. Additionally, Commission Delegated Regulation (EU) 2022/1644 lays down the range of samples and stage of production, processing and distribution at which the samples are to be taken.</p> <p>Since 2018 until 2022, the data on the national residue monitoring plan were reported to EFSA in accordance with Council Directive 96/23/EC.</p> <p>The dataset contains the results of laboratory tests from samples taken from bovines, pigs, sheep, goats, horses, poultry, rabbits, farmed game, wild game aquaculture, milk, eggs and honey, and from 2023 also samples from casings, insects and reptiles.</p> <p>Targeted samples are taken with the aim of detecting illegal treatment or controlling compliance with the maximum levels laid down in the relevant legislation. This means that, in their national plans Member States target the groups of animals (species, gender, age) where the probability of finding residues is the highest. Conversely, the objective of random sampling is to collect significant data to evaluate, for example, consumer exposure to a specific substance.</p> <p>Suspect samples are taken as a consequence of i) non-compliant results on samples taken in accordance with the control plans, ii) possession or presence of prohibited substances at any point during manufacture, storage, distribution or sale through the food and feed production chain, or iii) suspicion or evidence of illegal treatment or non-compliance with the withdrawal period for an authorised medicinal veterinary product.</p> <p>Residues of pharmacologically active substances mean active substances, excipients or degradation products and their metabolites, which remain in food.</p> <p>Unauthorised substances mean substances that are not authorised as veterinary medicinal products or as a feed additive under European Union legislation.</p> <p>Prohibited substances mean substances which are prohibited for use in food producing animals according to the European Union legislation.</p> <p>Non-compliant sample is a sample that has been analysed for the presence of one or more substances and failed to comply with the legal provisions for at least one substance. Thus, a sample can be non-compliant for one or more substances.</p> <p>For 2021 and 2022 the only United Kingdom data that were reported to EFSA were from Northern Ireland. In accordance with the Agreement on the withdrawal of the United Kingdom of Great Britain and Northern Irelandfrom the European Union and the European Atomic Energy Community, and in particular Article 5(4) of the WindsorFramework (see Joint Declaration No 1/2023 of the Union and the United Kingdom in the Joint Committee establishedby the Agreement on the withdrawal of the United Kingdom of Great Britain and Northern Ireland from the EuropeanUnion and the European Atomic Energy Community of 24 March 2023, OJ L 102, 17.4.2023, p.87) in conjunction withsection 24 of Annex 2 to that Framework, for the purposes of this Regulation, references to Member States include theUnited Kingdom in respect of Northern Ireland.</p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>VMPR_2023 – Department of Agriculture, Environment and Rural Affairs</p> <p>VMPR_2022 – Department of Agriculture, Environment and Rural Affairs</p> <p>VMPR_2021 – Department of Agriculture, Environment and Rural Affairs</p>
FI GU R E 3 Maximum likelihood phylogenetic tree of the Hyalospheniformes with a focus on Apodera, Alocodera, and Padaungiella based on COI gene sequences. Bootstrap values (bs) and Bayesian posterior probabilities (p.p.) are indicated respectively between branches. COI sequences from genera other than Apodera were retrieved from GenBank in Superficially described and ignored for 92 years, rediscovered and emended: Apodera angatakere (Amoebozoa: Arcellinida: Hyalospheniformes) is a new flagship testate amoeba taxon from Aotearoa (New Zealand)
FI GU R E 3 Maximum likelihood phylogenetic tree of the Hyalospheniformes with a focus on Apodera, Alocodera, and Padaungiella based on COI gene sequences. Bootstrap values (bs) and Bayesian posterior probabilities (p.p.) are indicated respectively between branches. COI sequences from genera other than Apodera were retrieved from GenBank
Scanning electron microscope images of Dunaliela tertiolecta and Phaeodactylum tricornutum cultures and scanning electron microscope images and cryogenic electron microscope images of isolated small cellular particles from respective conditioned media
<p>Scanning electron microscope images of cultures of microalgae<em> Dunaliela</em><em> </em><em>tertiolecta</em><em> </em>and <em>Phaeodactylum</em><em> </em><em>tricornutum</em><em> </em>and scanning electron microscope images and cryogenic electron microscope images of isolated small cellular particles from respective conditioned media are presented. Each image is supplemented by description of the preparation of the sample and the data on the imaging technique and equipment. The data are curated by Veronika Kralj-Iglic and Anna Romolo, University of Ljubljana, Faculty of Health Sciences, Laboratory of Clinical Biophysics.</p> <p> </p> <p> </p>
Evaluating homophily of human PPI with respect to chromosomes
<p>Homophily/heterophily evaluation, expressed in terms of z-score values, is related to the human Protein-Protein Interaction Network (PPI), obtained from the STRING v11.5 database (<a href="https://string-db.org/">https://string-db.org</a>) setting standard threshold on edge score (T=700). Each protein occurring in the PPI was assigned to a class corresponding to the chromosome the related gene belongs to.</p> <p>A total of 23 classes (<em>chr1</em>, <em>chr2</em>, ..., <em>chr22</em>, <em>chrX</em>) were considered (excluding the class corresponding to chromosome Y because of the small number of genes occurring in the network).</p> <p>The homophily/heterophily nature of the network, with respect to chromosome classes, was evaluated through HONTO tool (<a href="https://github.com/cumbof/honto">https://github.com/cumbof/honto</a>).</p> <p>In other words, the tendency of proteins to preferentially interact with proteins whose genes are physically located on the same chromosome (homophily) or on different chromosomes (heterophily) was investigated and evaluated in terms of z-scores.</p> <p>Values related to intra (along the diagonal) and inter chromosomal interactions (other than the diagonal) are also reported as a heatmap.</p> <p>As one can observe, values occurring in the diagonal are clearly higher than values out of the diagonal, leading to assess a homophilic nature of the network, confirming the link between shared chromosome and interaction in the PPI.</p>
Text-fig. 8. Enamel ultrastructure of M1, Equus przewalskii (Chornobyl Exclusion Zone). a, b: enamel row, scale bar = 100 and 20 Μm respectively; c: type I and III, scale bar = 20 Μm; d–f: first type enamel arrangement, scale bar d = 10, e = 3 Μm and f = 2 Μm; g, h: prisms of TZ, scale bar = 50 and 30 Μm respectively; i: type II near OES, scale bar = 20 Μm. in The Ultrastructure Of The Tooth Enamel Of Small Equus Of The "Tarpan" Group And Their Possible Phylogenetic Connections
Text-fig. 8. Enamel ultrastructure of M1, Equus przewalskii (Chornobyl Exclusion Zone). a, b: enamel row, scale bar = 100 and 20 Μm respectively; c: type I and III, scale bar = 20 Μm; d–f: first type enamel arrangement, scale bar d = 10, e = 3 Μm and f = 2 Μm; g, h: prisms of TZ, scale bar = 50 and 30 Μm respectively; i: type II near OES, scale bar = 20 Μm.
Text-fig. 10. Enamel ultrastructure of I1 (a) and I2 (b, c), Equus gmelini, tarpan (Myrne). a: vertical section; b, c: horizontal cross-section, scale bar = 250 and 100 Μm respectively. in The Ultrastructure Of The Tooth Enamel Of Small Equus Of The "Tarpan" Group And Their Possible Phylogenetic Connections
Text-fig. 10. Enamel ultrastructure of I1 (a) and I2 (b, c), Equus gmelini, tarpan (Myrne). a: vertical section; b, c: horizontal cross-section, scale bar = 250 and 100 Μm respectively.
Text-fig. 6. Enamel ultrastructure of M2, Equus gmelini (Hirzhevo). a: type I and III, scale bar = 20 Μm; b: IPM and PE first type prisms, scale bar = 3 Μm; c, d: wavy/decussated enamel of TZ, scale bar = 20 and 10 Μm respectively; e: type II near OES border, scale bar = 2 Μm; f: type III, scale bar = 2 Μm. in The Ultrastructure Of The Tooth Enamel Of Small Equus Of The "Tarpan" Group And Their Possible Phylogenetic Connections
Text-fig. 6. Enamel ultrastructure of M2, Equus gmelini (Hirzhevo). a: type I and III, scale bar = 20 Μm; b: IPM and PE first type prisms, scale bar = 3 Μm; c, d: wavy/decussated enamel of TZ, scale bar = 20 and 10 Μm respectively; e: type II near OES border, scale bar = 2 Μm; f: type III, scale bar = 2 Μm.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.