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DH2.1 working docs: DH 2.1 working version March 2022
The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
Fig. 1 in Working towards a conservation plan for fish parasites: Cyprinid parasites from the south African cape fold freshwater ecoregion as a case study
Fig. 1. Map illustrating localities where the five cyprinid hosts were collected in the Cape Fold ecoregion in the Western Cape, South Africa.
Fig. 7 in Working towards a conservation plan for fish parasites: Cyprinid parasites from the south African cape fold freshwater ecoregion as a case study
Fig. 7. Rarefaction/extrapolation curve estimating the diversity of parasites as a function of sampling effort for three of the five hosts collected in the OlifantsDoorn River System, Western Cape Province, South Africa. Shaded area represents the 95% confidence interval obtained using the bootstrap method based on 100 repetitions. Created using iNEXT Online (Chao et al., 2016).
Fig. 4 in Working towards a conservation plan for fish parasites: Cyprinid parasites from the south African cape fold freshwater ecoregion as a case study
Fig. 4. Pseudobarbus calidus (Barnard, 1938) (max. length: 125 mm) (A). Sclerites of Paradiplozoon sp. from the gills (B). Acanthocephala from the body cavity, whole specimen (C) and hooks on proboscis (top left insert). Larval Contracaecum sp. from the body cavity, anterior (D) and posterior (E) ends, lateral view. Scale bars: 100 μm (B, C, D, E).
Fig. 6 in Working towards a conservation plan for fish parasites: Cyprinid parasites from the south African cape fold freshwater ecoregion as a case study
Fig. 6. Sedercypris erubescens (Skelton, 1974) (max. length: 120 mm) (A). Larval Contracaecum sp. from the body cavity, anterior (B) and posterior (C) ends, lateral view. Scale bars: 100 μm (B, C).
Fig. 5 in Working towards a conservation plan for fish parasites: Cyprinid parasites from the south African cape fold freshwater ecoregion as a case study
Fig. 5. Pseudobarbus phlegethon (Barnard, 1938) (max. length: 65 mm) (A); Acanthogyrus sp. found from the body cavity (B). Scale bar: 500 μm.
Fig. 3 in Working towards a conservation plan for fish parasites: Cyprinid parasites from the south African cape fold freshwater ecoregion as a case study
Fig. 3. Labeobarbus seeberi (Gilchrist et Thompson, 1913) (max. length: 270 mm) (A); Myxobolus sp. (B) and Dactylogyrus sp. from the gills of L. seeberi, hamuli and marginal hooks (C), male copulatory complex (D) and vagina (E). Lateral view of Rhabdochona sp. 2 from the intestine, anterior end of female (F) and male (G), posterior end of male (H); metacercariae of Diplostomidae (I) from black cysts on skin. Scale bars: 10 μm (B); 50 μm (D, E); 100 μm (C, F, G, H, I).
Fig. 2 in Working towards a conservation plan for fish parasites: Cyprinid parasites from the south African cape fold freshwater ecoregion as a case study
Fig. 2. Cheilobarbus serra (Peters, 1864) (max. length: 350 mm) (A); adult Paradiplozoon sp. (B) and sclerites in attachment clamps (C, D) found on the gills; hamuli of Gyrodactylus sp. (E) and marginal hooks (F), and a pre-metamorphic stage of the copepod belonging to the Lernaeidae (G), both from the gills. Anterior (H) and posterior (I) ends of Rhabdochona sp. 1 (lateral view) from the intestine; (J) whole specimen of the Caryophyllidea. Scale bars: 10 μm (E, F); 100 μm (C, D, G, H, I); 500 μm (B); 1000 μm (J).
DH2.1 working docs: DH1.1 working version
The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
DH2.1 working docs: DH2.1 working version November 2021
The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
DH2.1 working docs: COL2020-08-01
The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
Dataset on Academics' Work and Well-being During the COVID-19 Pandemic
<p>The "Dataset on Academics’ Work and Well-being During the COVID-19 Pandemic" was collected through an online survey conducted between June 10 and 20, 2020, using a cloud-based survey platform.</p>
Grass Phylogeny Working Group III: data repository
<p><strong>Grass Phylogeny Working Group III: data repository</strong></p> <p>Phylogenetic analyses of the grass family (Poaceae) using nuclear and plastid data. The data set includes 1153 accessions corresponding to 1133 accepted species. Genomic data was obtained from different sources including target capture, shotgun, transcriptomes and annotated genomes. Nuclear markers (Angiosperm353 gene set) were assembled from short read data using HybPiper or a custom assembly pipeline optimized for low coverage shotgun data. Plastid genes were either retrieved from published plastome sequences or assembled here using getOrganelle. This data set also includes the results of a gene tree-species tree reconciliation analysis using GeneRax.</p> <p> </p> <p>Contact persons:</p> <p>Matheus E. Bianconi (matheus-enrique.bianconi@univ-tlse3.fr), Jan Hackel (jan.hackel@uni-marburg.de), Maria S. Vorontsova (m.vorontsova@kew.org)</p> <p> </p> <p>Content description</p> <p><strong>1. Metadata</strong></p> <ul> <li><code>gpwgIII_samples_metadata_taxonomy.tsv</code></li> </ul> <p>Tab-separated file with details for all 1,702 accessions used in this study. Columns: analysis_ID - ID in nuclear analyses; analysis_ID_plastome - ID in plastome analyses; acc_species - accepted species name; acc_species_author - taxonomic species authority; acc_genus - accepted genus name; acc_genus_author - taxomomic genus authority; publication - associated prior publication; data type - type of sequence data; isolate - laboratory isolate ID; voucher_ID - herbarium voucher ID; germplasm_ID - germplasm collection ID; repo_accession - accession number in public repository; plastome_accession - accession number of assembled plastome sequence; removed_nuclear - reason for removal from nuclear tree, if applicable; removed_plastome - reason for removal from plastome tree, if applicable; soreng2022_genus - genus name in Soreng et al. 2022, https://doi.org/10.1111/jse.12847; subtribe, tribe, subfamily, major.clade - classification according to Soreng et al. 2022.</p> <p><strong>2. Nuclear data</strong></p> <p><em>- Dataset1 ("main")</em><br>Number of samples: 1153<br>Number of genes: 331<br>Alignment trimming threshold: gt = 0.1 (removed sites > 90% missing data)<br>Genes per sample: > 166</p> <p><em>- Dataset2 ("strict trimming")</em><br>Number of samples: 1153<br>Number of genes: 315<br>Alignment trimming threshold: gt = 0.5 (removed sites > 50% missing data)<br>Genes per sample: > 158</p> <p><em>- Dataset3 (dataset 1 without shotgun samples)</em><br>Number of samples: 841<br>Number of genes: 331<br>Alignment trimming threshold: gt = 0.1 (removed sites > 90% missing data)<br>Genes per sample: > 166</p> <p><strong>2.1. Raw sequences</strong></p> <p>Raw Ang353 sequence assemblies for all samples (pre-trimming and filtering)</p> <ul> <li><code>raw_Ang353_sequences.zip</code></li> </ul> <p><strong>2.2 Nuclear gene alignments</strong></p> <p>Trimmed alignments from datasets 1, 2 and 3.</p> <ul> <li><code>alignments_dataset1_main_final.zip</code></li> <li><code>alignments_dataset2_strict_trimming_final.zip</code></li> <li><code>alignments_dataset3_no_shotgun_final.zip</code></li> </ul> <p><strong>2.3. Nuclear gene trees</strong><br>Gene trees inferred using RAxML (GTRCAT, 100 bootstraps) for the alignments from datasets 1, 2 and 3.</p> <ul> <li><code>gene_trees_dataset1_main_final.zip</code></li> <li><code>gene_trees_dataset2_strict_trimming_final.zip</code></li> <li><code>gene_trees_dataset3_no_shotgun_final.zip</code></li> </ul> <p><strong>2.4. Multigene species trees</strong><br>Multigene species trees obtained using Astral-Pro3 from gene trees for datasets 1, 2 and 3. </p> <ul> <li><code>astralpro_trees.zip</code>, which includes: <ul> <li>trees_Ang353_grasses_dataset1_main_gtrcat.astralpro</li> <li>trees_Ang353_grasses_dataset2_strict_trimming_gtrcat.astralpro</li> <li>trees_Ang353_grasses_dataset3_no_shotgun_gtrcat.astralpro</li> </ul> </li> </ul> <p><strong>3. Gene tree–species tree reconciliation</strong></p> <ul> <li><code>generax.zip</code></li> </ul> <p>Compressed zip archive with input files and results, including log files, of the GeneRax reconciliation analysis. One subfolder for each of the four analyses run: "all_tribes", "Andropogoneae", "Bambusoideae", "Triticeae".</p> <ul> <li><code>transfers_reconciliation_analyses.zip</code>, which includes: <ul> <li>transfers_all_all_tribes.tsv: Tab-separated file with all transfers inferred with the tribe-level Poaceae reconciliation analysis. Each line represents one transfer inferred.</li> <li>transfers_all_Andropogoneae.tsv: Tab-separated file with all transfers inferred with the Andropogoneae reconciliation analysis. Each line represents one transfer inferred.</li> <li>transfers_all_Bambusoideae.tsv: Tab-separated file with all transfers inferred with the Bambusoideae reconciliation analysis. Each line represents one transfer inferred.</li> <li>transfers_all_Triticeae.tsv: Tab-separated file with all transfers inferred with the Triticeae reconciliation analysis. Each line represents one transfer inferred.</li> <li>transfers_counts_all_tribes.tsv: Tab-separated file with aggregated transfer counts, in both directions for each reticulate connection, from the tribe-level Poaceae reconciliation analysis.</li> <li>transfers_counts_Andropogoneae.tsv: Tab-separated file with aggregated transfer counts, in both directions for each reticulate connection, from the Andropogoneae reconciliation analysis.</li> <li>transfers_counts_Bambusoideae.tsv: Tab-separated file with aggregated transfer counts, in both directions for each reticulate connection, from the Bambusoideae reconciliation analysis.</li> <li>transfers_counts_Triticeae.tsv: Tab-separated file with aggregated transfer counts, in both directions for each reticulate connection, from the Triticeae reconciliation analysis.</li> </ul> </li> </ul> <p><strong>4. Plastome data</strong></p> <p>Alignment and phylogenetic tree from plastome data.</p> <ul> <li><code>plastome_files.zip</code>, which includes <ul> <li>reduced_plastome_concat_CDS_trnLtrnF_trimmed.fna-out.fas: FASTA file with the final, concatenated DNA alignment of 71 plastome regions for 910 accessions, after data filtering.</li> <li>partitions.txt: Text file with positions of the 71 plastome regions in the concatenated alignment.</li> <li>plastome_concat_CDS_trnLtrnF_trimmed_TBE.raxml.support: Plastome tree with Transfer Bootstrap Expectation values as node labels.</li> <li>RAxML_bipartitions.plastome_concat_CDS_trnLtrnF_trimmed: Maximum likelihood plastome tree inferred with RAxML, with Felsenstein bootstrap values as node labels.</li> <li>RAxML_bootstrap.plastome_concat_CDS_trnLtrnF_trimmed: 100 rapid bootstrap pseudoreplicate plastome trees inferred with RAxML.</li> <li>RAxML_info.plastome_concat_CDS_trnLtrnF_trimmed: RAxML analysis log file.</li> <li>nuc_plastome_matching_tips.tab: Tab-separated file with accessions matched in nuclear-plastome comparison.</li> </ul> </li> </ul> <p><strong>5. Poaceae-specific reference Ang353 dataset</strong><br>Reference sequence dataset used for the assembly of Ang353 sequences in this study.</p> <ul> <li><code>target_Ang353_sequences_grasses.zip</code></li> </ul> <p><strong>6. Shotgun assembly script</strong></p> <p>Custom script used for the assembly of Ang353 sequences from shotgun data</p> <ul> <li><code>shotgun_assembler_script.zip</code>, which includes: <ul> <li>shotgun_assembler_Ang353_sequences.sh: script for assembly of short reads from shotgun data</li> <li>template_manifest_file.tsv: TAB-separated file to specify sample names and location of short read files (required by the assembly script)</li> <li>list_Ang353_genes_orthofinder.txt: list of Ang353 gene identifiers (required by the assembly script)</li> </ul> </li> </ul> <p><strong>7. Quartet metrics script</strong></p> <p>R script to calculate the Quartet Concordance (QC) and Quartet Differential (QD) metrics from the gene tree frequencies/proportions for each quartet at a branch, following Pease et al. 2018 (American Journal of Botany, <span><a href="https://doi.org/10.1002/ajb2.1016" target="_blank" rel="nofollow noopener noreferrer">https://doi.org/10.1002/ajb2.1016</a></span>).</p> <ul> <li><code>quartet_metrics.R</code></li> </ul>
Quantitative results of the analysis of human bioengineered tissues corresponding to the work "Generation of tissue-like models of human bilayered tissues functionalized with olive oil components"
<p>This file contains the raw dataset generated in the work entitled "GENERATION OF NOVEL TISSUE-LIKE MODELS OF HUMAN BILAYERED TISSUES FUNCTIONALIZED WITH BIOACTIVE COMPONENTS OBTAINED FROM OLIVE OIL". These results correspond to the quantification of the histological results obtained in this work.</p>
Appendix Figures A.1 - A.15 of the paper "Advanced classification of hot subdwarf binaries using artificial intelligence techniques and Gaia DR3 data". This work has been accepted for publication in the journal Astronomy & Astrphysics (A&A) on September 24, 2024.
<p><strong>Figure captions:</strong></p> <p> </p> <p><strong>Fig. A.1.</strong> Heatmap with the number of common stars (true positives) labeled as binary for the five methods used.</p> <p> </p> <p><strong>Fig. A.2.</strong> Heatmap with the number of common stars (true negatives) labeled as single for the five methods used.</p> <p> </p> <p><strong>Fig. A.3.</strong> Color-magnitude diagrams, showing the 2815 stars of our sample from Sect. 3. Colors indicate the label predictions by SOM (left panel) and CNN (right panel).</p> <p> </p> <p><strong>Fig. A.4.</strong> K-S test comparing radial SOM (black) and CNN (blue).</p> <p> </p> <p><strong>Fig. A.5.</strong> Spectra of the star "LAMOSTJ112914.11+471501.7" (blue color) and in the background (gray color) the 35 stars classified as binary by Solano et al. (2022) with VOSA tools.</p> <p> </p> <p><strong>Fig. A.6.</strong> Spectra of the star "HD14829" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).</p> <p> </p> <p><strong>Fig. A.7.</strong> Spectra of the star "Feige98" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).</p> <p> </p> <p><strong>Fig. A.8.</strong> Spectra of the star "PG0304+184" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).</p> <p> </p> <p><strong>Fig. A.9.</strong> Spectra of the star "PG1510+635" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).</p> <p> </p> <p><strong>Fig. A.10.</strong> Cluster 0 of spectra (blue color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.11.</strong> Cluster 4 of spectra (brown color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.12.</strong> Cluster 1 of spectra (yellow color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.13.</strong> Cluster 3 of spectra (red color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.14.</strong> Cluster -1 of spectra (pink color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.15.</strong> Cluster 2 of spectra (green color) with the other spectra in the background (gray color).</p>
Tweasel traffic collection for TrackHAR adapter work on websites (September 2024)
<p>To start creating TrackHAR adapters for trackers active on the web, we ran a traffic collection on the top 10,000 websites in Germany as per the Chrome User Experience Report (CrUX) for August 2024.</p> <p>The websites were accessed in a headless Chromium browser using Playwright from a French IP address between 2024-09-20 and 2024-09-26. All websites were accessed twice for 60 seconds each: The first time without any user interaction, the second time random user input was provided, meaning that it is possible/likely that consent was given when requested.</p> <p>Source code for the collection: <a href="https://github.com/tweaselORG/experiments/tree/4d00eba66a3b797805a0c5ee32bcdd1e04577217/web-monkey-september-2024">https://github.com/tweaselORG/experiments/tree/4d00eba66a3b797805a0c5ee32bcdd1e04577217/web-monkey-september-2024</a><br>Notes on the collection: <a href="https://github.com/tweaselORG/experiments/issues/3">https://github.com/tweaselORG/experiments/issues/3</a></p>
Lecturer Performance: The Influence of Transformational Leadership Style, Work Environment, Compensation, and Institutional Transformation
<p>This study aims to test and analyze the influence of transformational leadership style, work environment, compensation, and institutional transformation on the performance of lecturers at the Mandala Institute of Technology and Science. This study uses a quantitative approach. Data analysis uses descriptive statistical analysis and inferential statistical analysis that describes a certain characteristic or feature of a phenomenon that occurs and makes conclusions or generalizations about the population based on sample data. The sample used was 48 lecturers at the Mandala Institute of Technology and Science. The results of the study indicate that compensation affects lecturer performance, while transformational leadership style, work environment, and institutional transformation do not affect lecturer performance. The implications of this study indicate that increasing compensation can significantly improve lecturer performance at the Mandala Institute of Technology and Science, so it is important for institutions to focus on fairer and more adequate compensation policies. Conversely, improvements in transformational leadership style, work environment, and institutional transformation need to be adjusted to the specific context and needs of lecturers in order to have a more significant impact on performance.</p>
Quantitative results of the analysis of two types of bone particles corresponding to the work "A comprehensive analysis of two types of xenogeneic bone particles for use in maxillofacial bone regeneration therapies"
<p>This dataset corresponds to the quantitative data generated in the work entitled "A comprehensive analysis of two types of xenogeneic bone particles for use in maxillofacial bone regeneration therapies".</p> <p>Regeneration of maxillofacial bone structures is challenging. One of the strategies applied to bone damage repair is the use of bone filler particles, and different types of these particles have been tested. In this work, we analyzed the regenerative potential of deproteinized bone particles (DP) and collagen-based bone particles (CP) to determine the potential of each biomaterial in bone repair. Results of the structural analysis using scanning electron microscopy and 3D scanning showed that DP and CP were structurally similar, and consisted of a heterogeneous mixture of bone particles of different sizes and shapes. Then, ex vivo analyses using morphological evaluation, LIVE & DEAD and quantification of DNA released to the medium demonstrated that CP and DP were highly biocompatible when used in direct and in indirect contact with human cells, at 24, 48 and 72h of follow-up. Then, when both particles were grafted for 2 months on Wistar rats in which a critical defect had been generated at the mandible bone. Results of the computed tomography analysis showed a significant reduction of the bone defect in the CP group, but not in the DP group, as compared with negative controls devoid of any bone particles. Histological analysis of the graft area revealed that both particles were biocompatible in vivo, and a regenerative tissue with collagen fibers and mineralized spots was found in CP and DP, with higher number of mineralized spots in DP. Histochemistry and immunohistochemistry analyses confirmed the presence of collagen, proteoglycans and osteocalcin at the regeneration area of CP and DP. In general, these results confirm the biocompatibility of both types of particles and that both were able to induce maxillofacial bone regeneration, especially in the case of CP. Future studies should determine their clinical usefulness in patients with cleft palate, mandibular damage and other maxillofacial applications.</p>
Replication Data for the retroharmonize R Package Case Study: Working With Arab Barometer Surveys
<p>Replication datasets for the <a href="https://retroharmonize.dataobservatory.eu/articles/arabbarometer.html">retroharmonize Case Study: Working With Arab Barometer Surveys</a></p>
WORK-LIFE BALANCE OF THE EMPLOYED POPULATION DURING THE EMERGENCY SITUATION OF COVID-19 IN LATVIA
<p>All the employees face the challenge of finding the right work-life balance. The ability of employees to deal with the successful combining of work, family responsibilities, and personal life is crucial for both employers and family members of employees. During the COVID-19 emergency situation, many people around the world were forced to work remotely. Initially, there were observed some certain expectations about the possibility of working from home as a positive factor that will promote work-life balance. However, over time, negative tendencies were also revealed, as employees were only one call or message away from the employer, and uncertainty and leisure time with family often created more stress. As many organizations and individuals were not ready for this sudden change, many mistakes were made, which further raised the issue of work-life balance. The aim of the research was to evaluate the flexibility of reconciling work and private life of Latvian employees in various socio-demographic groups during the COVID-19 emergency situation in spring 2020, to investigate how family life influenced employees’ ability to perform work duties, to find out if employees had any additional housework responsibilities and how their workload changed concerning housework amount during the COVID-19 emergency situation. The research is based on the data obtained in the survey of the Latvian employed population, which was conducted within the framework of the Latvian National Research Programme Project “CoLife” in the second half of 2020. As a result, the hypothesis of the research that all groups of employees experienced work-life balance difficulties during the COVID-19 emergency situation has been partially confirmed, i.e. women in the 18-44 age group and respondents with minor children in the household more likely faced difficulties of work-life balance. The scientific research methods that were used in the research are the monographic method, content analysis, survey, data processing with SPSS to determine the mutual independence of the data from the questionnaires. </p>
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.