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379 results for “data sharing”
D-JRP14-WP0.3 Data sharing in Full Force - recorded webinar
<p>A recorded presentation on the data sharing platform OwnCloud, used during the full force project. </p>
Data: Genomic signatures of admixture and selection are shared among populations of Zaprionus indianus across the western hemisphere
<p>Introduced species have become an increasingly common component of biological communities around the world. A central goal in invasion biology is therefore to identify the demographic and evolutionary factors that underlie successful introductions. Here we use whole genome sequences, collected from populations in the native and introduced ranges of the African fig fly, <i>Zaprionus indianus</i>, to quantify genetic relationships among them, identify potential sources of the introductions, and test for selection at different spatial scales. We find that geographically widespread populations in the western hemisphere are genetically more similar to each other than to lineages sampled across Africa, and that these populations share a mixture of alleles derived from differentiated African lineages. Using patterns of allele-sharing and demographic modelling we show that <i>Z. indinaus</i> have undergone a single expansion across the western hemisphere with admixture between African lineages predating this expansion. We also find support for selection that is shared across populations in the western hemisphere, and in some cases, with a subset of African populations. This suggests either that parallel selection has acted across a large part of <i>Z. indianus</i>'s introduced range; or, more parsimoniously, that <i>Z. indianus</i> has experienced selection early on during (or prior-to) its expansion into the western hemisphere. We suggest that the range expansion of <i>Z. indianus</i> has been facilitated by admixture and selection, and that management of this invasion could focus on minimizing future admixture by controlling the movement of individuals within this region rather than between the western and eastern hemisphere.</p>
Data from: Implications of shared predation for space use in two sympatric leporids
Spatial variation in habitat riskiness has a major influence on the predator–prey space race. However, the outcome of this race can be modulated if prey shares enemies with fellow prey (i.e., another prey species). Sharing of natural enemies may result in apparent competition, and its implications for prey space use remain poorly studied. Our objective was to test how prey species spend time among habitats that differ in riskiness, and how shared predation modulates the space use by prey species. We studied a one‐predator, two‐prey system in a coastal dune landscape in the Netherlands with the European hare (Lepus europaeus) and European rabbit (Oryctolagus cuniculus) as sympatric prey species and red fox (Vulpes vulpes) as their main predator. The fine‐scale space use by each species was quantified using camera traps. We quantified residence time as an index of space use. Hares and rabbits spent time differently among habitats that differ in riskiness. Space use by predators and habitat riskiness affected space use by hares more strongly than space use by rabbits. Residence time of hare was shorter in habitats in which the predator was efficient in searching or capturing prey species. However, hares spent more time in edge habitat when foxes were present, even though foxes are considered ambush predators. Shared predation affected the predator–prey space race for hares positively, and more strongly than the predator–prey space race for rabbits, which were not affected. Shared predation reversed the predator–prey space race between foxes and hares, whereas shared predation possibly also released a negative association and promoted a positive association between our two sympatric prey species. Habitat riskiness, species presence, and prey species' escape mode and foraging mode (i.e., central‐place vs. noncentral‐place forager) affected the prey space race under shared predation.
Data from: The Chord-Normalized Expected Species Shared (CNESS)-distance represents a superior measure of species turnover patterns
<p>1. Measures of β-diversity characterizing the difference in species composition between samples are commonly used in ecological studies. Nonetheless, commonly used dissimilarity measures require high sample completeness, or at least similar sample sizes between samples. In contrast, the Chord-Normalized Expected Species Shared (CNESS) dissimilarity measure calculates the probability of collecting the same set of species in random samples of a standardized size, and hence is not sensitive to completeness or size of compared samples. To date, this index has enjoyed limited use due to difficulties in its calculation and scarcity of studies systematically comparing it with other measures.</p> <p>2. Here, we developed a novel R function that enables users to calculate ESS (Expected Species Shared)-associated measures. We evaluate the performance of the CNESS index based on simulated datasets of known species distribution structure, and compared CNESS with more widespread dissimilarity measures (Bray-Curtis index, Chao-Sørensen index, and proportionality based Euclidean distances) for varying sample completeness and sample sizes.</p> <p>3. Simulation results indicated that for small sample size (m) values, CNESS chiefly reflects similarities in dominant species, while selecting large m values emphasizes differences in the overall species assemblages. Permutation tests revealed that CNESS has a consistently low CV (coefficient of variation) even where sample completeness varies, while the Chao-Sørensen index has a high CV particularly for low sampling completeness. CNESS distances are also more robust than other indices with regards to undersampling, particularly when chiefly rare species are shared between two assemblages.</p> <p>4. Our results emphasize the superiority of CNESS for comparisons of samples diverging in sample completeness and size, which is particularly important in studies of highly mobile and species-rich taxa where sample completeness is often low. Via changes in the sample size parameter m, CNESS furthermore cannot only provide insights into the similarity of the overall distribution structure of shared species, but also into the differences in dominant and rare species, hence allowing additional, valuable insights beyond the capability of more widespread measures.<br> </p>
Data from: Inter-individual spacing affects the finder's share in ring-tailed coatis (Nasua nasua)
<p>Social foraging models are often used to explain how group size can affect an individual's food intake rate and foraging strategies. The proportion of food eaten before the arrival of conspecifics, the finder's share, is hypothesized to play a major role in shaping group geometry, foraging strategy, and feeding competition. The variables which affect the finder's share in ring-tailed coatis were tested using a series of food trials. The number of grapes in the food trials had a strong negative effect on the finder's share and the probability that the finder was joined. The effect of group size on the finder's share and foraging success was not straightforward, and was mediated by socio-spatial factors. The finder's share increased when the time to arrival of the next individual was longer, the group was more spread out, and the finder was in the back of the group. Similarly, the total amount of food eaten at a trial was higher when more grapes were placed, arrival time was longer, and number of joiners was smaller. Individuals at the front edge of the group found far more food trials, but foraging success was higher at the back of the group where there were fewer conspecifics to join them. This study highlights the importance of social spacing strategies and group geometry on animal foraging tactics and the costs and benefits of sociality.</p>
Data sharing in Human ancient DNA studies
<p>The dataset contains information on data sharing regarding mitochondrial, Y chromosomal and autosomal polymorphisms in a total of 162 papers on ancient human DNA published between 1988 and 2013.</p>
Responses to questionnaire on data sharing behaviour
<p>This dataset contains the responses to a questionnaire based survey on data sharing behaviour of scholars working with human ancient DNA. </p>
Data files related to the article "Different meditation practices share the same neuronal correlates: increased gamma brainwave amplitude compared to control in three different meditation traditions
<p>Files description:</p> <p>- EEG datasets: the EEG data preprocessed in EEGLAB format</p> <p>- chanlocs.mat: contains channel characteristics for the study / used<br> to plot topographical maps of the data</p> <p> - 60110Hz_spec_data_imw_medit_combined.mat : contains spectrum data between 60 and 110 Hz for all 4 groups of subjects in combined instructed mind-wandering and meditation condition</p> <p> - 60110Hz_spec_data_medit.mat : contains spectrum data between 60<br> and 110 Hz for all 4 groups of subjects in meditation condition</p> <p> - 711Hz_spec_data_medit: contains spectrum data between 7 and 11 Hz for each group in meditation condition</p> <p>- boxplot_gamma.csv and boxplot_alpha.csv : median spectrum in the gamma and alpha ranged used to generate figure 4 B and 6 B</p> <p>- R_code_boxplot.r : code to generate boxplot using boxplot_gamma or boxplot_alpha.csv files</p>
Supplementary material 3: List of tested and analyzed data sharing tools (non-exhaustive) from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
List of tested and analyzed data sharing tools (non-exhaustive)
Supplementary material 2: Definitions and Concepts from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Definitions and concepts in the context of the main paper.
Supplementary material 1: List of selected tools. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
List of selected tools.
Figure 7. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 7. - Mobile app for sporadic observations reporting.
Figure 2. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 2. - The Plazi workflow (green) within EU BON.
Biomedical Journal Data Sharing Policies
<p>Raw data of data sharing policies in over 300 journals, supporting the article currently under review: "Reproducible and reusable research: Are journal data sharing policies meeting the mark?". </p> <p>Raw data and analysis of data sharing policies of 318 biomedical journals. The study authors manually reviewed the author instructions and editorial policies to analyze the each journal's data sharing requirements and characteristics. The data sharing policies were ranked using a rubric to determine if data sharing was required, recommended, or not addressed at all. The data sharing method and licensing recommendations were examined, as well any mention of reproducibility or similar concepts. The data was analyzed for patterns relating to publishing volume, Journal Impact Factor, and the publishing model (open access or subscription) of each journal.</p> <p>We evaluated journals included in Thomson Reuter’s InCites 2013 Journal Citations Reports (JCR) classified within the following World of Science schema categories: Biochemistry and Molecular Biology, Biology, Cell Biology, Crystallography, Developmental Biology, Biomedical Engineering, Immunology, Medical Informatics, Microbiology, Microscopy, Multidisciplinary Sciences, and Neurosciences. These categories were selected to capture the journals publishing the majority of peer-reviewed biomedical research. The original data pull included 1,166 journals, collectively publishing 213,449 articles. We filtered this list to the journals in the top quartiles by impact factor (IF) or number of articles published 2013. Additionally, the list was manually reviewed to exclude short report and review journals, and titles determined to be outside the fields of basic medical science or clinical research. The final study set included 318 journals, which published 130,330 articles in 2013. The study set represented 27% of the original Journal Citation Report list and 61% of the original citable articles. Prior to our analysis, the 2014 Journal Citations Reports was released. After our initial analyses and first preprint submission, the 2015 Journal Citations Reports was released. While we did not use the 2014 or 2015 data to amend the journals in the study set, we did employ data from all three reports in our analyses. In our data pull from JCR, we included the journal title, International Standard Serial Number (ISSN), the total citable items for 2013, 2014, and 2015, the total citations to the journal for 2013/14/15, the impact factors for 2013/14/15, and the publisher.</p>
Supplemental data for "The genome of a sea spider corroborates a shared Hox cluster motif in arthropods with reduced posterior tagma"
<div># List of files on Zenodo</div> <p> </p> <div>Data accompanying the manuscript have been uploaded on Zenodo (10.5281/zenodo.14185694). Here we</div> <div>present a brief description of each file and put them in meaningful groups.</div> <p> </p> <div>## referenced supplement</div> <p> </p> <div>(Cited) supplementary material from the manuscript. For more information, please refer to the figure/table legends and the supplementary file descriptions.</div> <p> </p> <div> <div>- add-file-01.pdf</div> <div>- add-file-02-table1-data_overview.tsv</div> <div>- add-file-03-table2-genome_progress.tsv</div> <div>- add-file-04-table3-Pycnognonum_microRNAs.tsv</div> <div>- add-file-05-table4-named_genes.tsv</div> <div>- add-file-06-table5-abdA.tsv</div> <div>- add-file-07-hox_tree.pdf</div> <div>- add-file-08-table6-r2_g3735-Alignment-HitTable.tsv</div> <div>- add-file-09-hro_tree.pdf</div> <div>- add-file-10-irx_tree.pdf</div> <div>- add-file-11-sine_tree.pdf</div> <div>- add-file-12-nk_tree.pdf</div> <div>- add-file-13-dbx.png</div> <div>- add-file-14-alignment.pdf</div> <div>- add-file-15-Plit_COI-alignment_distances.pdf</div> <div>- add-file-16-table7-isoseq.tsv</div> <div>- add-file-17-table8-chelicerate_repeat_content.tsv</div> <div>- add-file-18-table9-arthropod_genomes.tsv</div> <div>- add-file-19-table10-arthropod_repeat_content.tsv</div> <div>- add-file-20-table11-chelicerate_genomes.tsv</div> <div>- add-file-21-gene_analysis.zip</div> <div>- add-file-22-table12-self_synteny.tsv</div> </div> <p> </p> <div>## figures</div> <p> </p> <div>- figs.zip: archive of raw and processed figures for the manuscript in full resolution</div> <p> </p> <div>## analysis</div> <p> </p> <div>### genomic context</div> <p> </p> <div>The broader arthropod/chelicerate context for the _P. litorale_ genome assembly.</div> <p> </p> <div>- araneae.tsv: repeat makeup of published chelicerate assemblies</div> <div>- arthropoda.tsv: genome assembly statistics for arthropod genomes. From NCBI Genomes.</div> <div>- modern_taxids.txt: list of taxonomic IDs for species; made to be submitted to NCBI Taxonomy.</div> <div>- tax_report.txt: the full taxonomic report for each query species. Contains tax IDs for the entire lineage.</div> <div>- total_repeats.tsv: total repeat content of published chelicerate genomes.</div> <p> </p> <div>### Homeobox genes</div> <p> </p> <div>Files concerning the Homeobox gene cluster analysis. Each folder (hro, irx, nkx, sine) contains the</div> <div>candidate sequences from Aase-Remedios et al., the P. litorale sequences that matched, the multiple</div> <div>sequence alignment, the trimmed alignment, and the tree files.</div> <p> </p> <div>Additionally, the hox/ folder contains the analysis done for the AbdA gene, with searches performed</div> <div>against the de-novo assembled transcriptomes (.m8 files).</div> <p> </p> <div>Finally, the r2_g3735/ folder contains the analysis of the r2_3735 gene model, which is found in the</div> <div>Hox cluster area on the P. litorale genome. Sequence searches against NCBI nr and the developmental</div> <div>transcriptomes show that the gene has putative homologs in other taxa and is expressed throughout</div> <div>development.</div> <p> </p> <div>## processed (intermediate) data</div> <p> </p> <div>### 00-kmer-jellyfish.zip</div> <p> </p> <div>k-mer spectra analysis with GenomeScope and GenomeScope2.0</div> <p> </p> <div>### 00-seq-qc.zip</div> <p> </p> <div>quality control output for raw sequencing data (e.g. FastQC output)</div> <p> </p> <div>### 01-assembly</div> <p> </p> <div>- assembly_graph.gfa: Flye output</div> <div>- assembly_graph.gv: Flye output</div> <div>- assembly_info.txt: Flye output</div> <div>- assembly.fasta: Flye output</div> <div>- backmap.hifi.sort.bam.cov-hist.pdf: coverage histogram of the back-mapped PacBio data</div> <div>- backmap.ont.sort.bam.cov-hist.pdf: coverage histogram of the back-mapped ONT data</div> <div>- BUSCO.arthropoda_odb10.txt: BUSCO completeness report (arthropoda_odb10)</div> <div>- BUSCO.metazoa_odb10.txt: BUSCO completeness report (metazoa_odb10)</div> <div>- flye.log: Flye assembler log</div> <div>- quast_report.pdf: assembly QC</div> <p> </p> <div>### 02-scaffold</div> <p> </p> <div>`yahs` output files:</div> <p> </p> <div>- asm_hic.sorted.bam</div> <div>- flye-yahs.fa</div> <div>- yahs.out_scaffolds_final.agp</div> <div>- yahs.out_scaffolds_final.fa.hic</div> <div>- yahs.out_scaffolds_final.fa.assembly</div> <p> </p> <div>Juicebox (manual curating) results:</div> <p> </p> <div>- yahs.out_scaffolds_final.fa.review.assembly</div> <div>- 02-flye-yahs-juicebox.fa</div> <p> </p> <div>GAP `sort_scaffolds` pipeline outputs</div> <p> </p> <div>- 03-flye-yahs-juicebox-merge.fasta</div> <div>- plit_q_0_50000_0.5FracBest_unseen_scaffolds.txt</div> <div>- plit_q_0_50000_0.5FracBest_insertion_stats.tsv</div> <div>- plit_q_0_50000_0.5FracBest_appended_scaffolds.tsv</div> <div>- plit_q_0_50000_0.5FracBest_inserted_scaffolds.tsv</div> <p> </p> <div>### 03-contamination</div> <p> </p> <div>Refer to the [contamination analysis](https://github.com/galicae/plit-genome/blob/main/04-contam/README.md) for details.</div> <p> </p> <div>Decontaminating the draft genome from non-metazoan scaffolds:</div> <p> </p> <div>- plit_q_0_50000_0.5FracBest_output_filtered.fasta: input draft genome</div> <div>- contam_tax.m8: Alignment results of UniRef90 against draft genome (MMseqs2)</div> <div>- scaffolds_taxonomic_distribution.tsv: summary of contam_tax.m8; number of genes from each taxonomic level per scaffold.</div> <div>- scaffolds_taxonomic_distribution_collapsed_vir.tsv: scaffolds with predominantly viral hits</div> <div>- scaffolds_taxonomic_distribution_suspect.tsv: scaffolds whose genes are <90% metazoan</div> <p> </p> <div>Checking for widespread _Metridium_ contamination:</div> <p> </p> <div>- primary_mq30.txt: list of high-quality mapping reads (presumptive "metridial")</div> <div>- metridium_scaffolds.txt_summary: no. of presumptive _Metridium_ reads per draft scaffold</div> <div>- metridium_scaffolds.txt: filtered SAM file with all high-quality "_Metridium_" hits on draft scaffolds</div> <div>- metridium_contigs.sam_summary: no. of presumptive _Metridium_ reads per Flye contig</div> <p> </p> <div>### 04-annotation</div> <p> </p> <div>Repeat analysis with RepeatModeler/RepeatMasker:</div> <p> </p> <div>- draft.fasta.tbl: output of RepeatModeler in tabular form</div> <div>- pb.sam.flagstats: summary of mapping the repeat families to the PacBio data.</div> <div>- pycno-families.fa: output of RepeatModeler - the sequences of the _P. litorale_ repeat families</div> <div>- draft.fasta.out.gff: output of RepeatModeler - repeat locations on the draft genome</div> <p> </p> <div>Protein coding gene annotation:</div> <p> </p> <div>- annot-01-isoseq.gff: GFF file with the gene models proposed using Iso-seq isoforms</div> <div>- annot-01-braker.gff: GFF file with the gene models proposed from round 1 of BRAKER3 using developmental transcriptomes</div> <div>- annot-02-braker.gff3: GFF file with the gene models proposed from round 2 of BRAKER3, using developmental transcriptome reads that weren't used in round 1</div> <div>- annot-03-denovo.gff3: GFF file with the gene models proposed from the de novo transcriptomes</div> <div>- deep_denovo_assemblies.zip: the de-novo assembled transcriptomes from the deeply sequenced developmental time points. Also available on ENA.</div> <p> </p> <div>tRNAscan output</div> <p> </p> <div>- trnascan.bed</div> <div>- trnascan.out</div> <div>- trnascan.fasta</div> <div>- trnascan.stats</div> <p> </p> <div>MirMachine output</div> <p> </p> <div>- Pli_september.PRE.gff: MirMachine output with permissive threshold</div> <div>- Pli_september.PRE-1.gff: MirMachine output with strict threshold</div> <div>- Pli_september.PRE.fasta: predicted miRNA sequences</div> <p> </p> <div>## Results</div> <p> </p> <div>- draft_softmasked.fasta: draft genome with repetitive regions softmasked</div> <div>- draft.fasta: draft genome fasta</div> <div>- hox.gff3: the position of the Hox genes in GFF3 form.</div> <div>- merged_sorted_named_dedup_flagged.gff3: protein-coding gene models from all rounds of annotation after deduplication</div> <div>- transcripts.fa: TransDecoder-extracted putative transcripts</div> <div>- transcripts.fa.transdecoder.pep: TransDecoder predicted peptides</div> <div>- out.emapper.annotations: EggNOG-mapper functional annotation for the predicted peptides</div> <div>- out.emapper.best.annotations: filtered EggNOG-mapper annotation; best hit per gene kept</div> <div>- miRNA.fasta: FASTA sequences of predicted miRNAs</div> <div>- miRNA.lenient.gff: GFF of miRNA positions (permissive MirMachine cutoff)</div> <div>- miRNA.strict.gff: GFF of miRNA positions (strict MirMachine cutoff)</div>
Data from: A shared pattern of midfacial bone modelling in hominids suggests deep evolutionary roots for human facial morphogenesis
<p>Midfacial morphology varies between hominoids, in particular between great apes and humans for which the face is small and retracted. The underlying developmental processes for these morphological differences are still largely unknown. Here we investigate the cellular mechanism of maxillary development (bone modelling), and how potential changes in this process may have shaped facial evolution. We analysed cross-sectional developmental series of gibbons, orangutans, gorillas, chimpanzees and present-day humans (N=183). Individuals were organized into five age groups according to their dental development. To visualize each species' bone modelling pattern and corresponding morphology during ontogeny, maps based on microscopic data were mapped onto species-specific age group average shapes obtained using geometric morphometrics. The amount of bone resorption was quantified and compared between species. Great apes share a highly similar bone modelling pattern, whereas gibbons have a distinctive resorption pattern. This suggests a change in cellular activity on the hominid branch. Humans possess most of the great ape pattern, but bone resorption is high in the canine area from birth on, suggesting a key role of canine reduction in facial evolution. We also observed that humans have high levels of bone resorption during childhood, a feature not shared with other apes.</p>
Data underlying figures in Beyond Data: sharing related research outputs to make data reusable
<p>Data that underlies figures in forthcoming article Beyond Data: sharing related research outputs to make data reusable, to be published in the journal Learned Publishing. Data in these files was retrieved from the DataCite REST API and the DataCite metadata schema. Zipped file contains identical data in 2 formats: individual CSV files (one per figure, plus a file with the overall count of DataCite DOIs by resource type and year) and single XLSX file with multiple tabs. </p>
Data of "Using current research information systems to investigate data acquisition and data sharing practices of computer scientists"
<p>This study describes a methodology where departmental academic publications are used to analyse the ways in which computer scientists share research data.</p> <p>Without sufficient information about researchers’ data sharing, there is a risk of mismatching FAIR data service efforts with the needs of researchers. This study describes a methodology where departmental academic publications are used to analyse the ways in which computer scientists share research data. The advancement of FAIR data would benefit from novel methodologies that reliably examine data sharing at the level of multidisciplinary research organisations. Studies that use CRIS publication data to elicit insight into researchers’ data sharing may therefore be a valuable addition to the current interview and questionnaire methodologies.</p> <p><strong>Data was collected from the following sources:</strong></p> <p>All journal articles published by researchers in the computer science department of the case study’s university during 2019 were extracted for scrutiny from the current research information system. For these 193 articles, a coding framework was developed to capture the key elements of acquiring and sharing research data. Article DOIs are included in the research data.</p> <p>The scientific journal articles and theirs DOIs are used in this study for the purpose of academic expression.</p> <p>The raw data is compiled into a single CSV file. Rows represent specific articles and columns are the values of the data points described below. Author names and affiliations were not collected and are not included in the data set. </p> <p> </p> <p>The following data points were used in the analysis:</p> <p><strong>Data points</strong></p> <ul> <li><strong>Main study types</strong></li> <li>Literature-based study (e.g. literature reviews, archive studies, studies of social media)</li> <li>yes/no</li> <li>Novel computational methods (e.g. algorithms, simulations, software)</li> <li>yes/no</li> <li>Interaction studies (e.g, interviews, surveys, tasks, ethnography)</li> <li>yes/no</li> <li>Intervention studies (e.g., EEG, MRI, clinical trials)</li> <li>yes/no</li> <li>Measurement studies (e.g. astronomy, weather, acoustics, chemistry)</li> <li>yes/no</li> <li>Life sciences (e.g. “omics”, ecology)</li> <li>yes/no</li> <li><strong>Data acquisition</strong></li> <li>Article presents a data availability statement</li> <li>yes/no</li> <li>Article does not utilise data</li> <li>yes/no</li> <li>Original data was collected</li> <li>yes/no</li> <li>Open data from prior studies were used</li> <li>yes/no</li> <li>Open data from public authorities, companies, universities and associations</li> <li>yes/no</li> <li><strong>Data sharing</strong></li> <li>Article does not use original data</li> <li>yes/no</li> <li>Data of the article is not available for reuse</li> <li>yes/no</li> <li>Article used openly available data</li> <li>yes/no</li> <li>Authors agree to share their data to interested readers</li> <li>yes/no</li> <li>Article shared data (or part of) as supplementary material</li> <li>yes/no</li> <li>Article shared data (or part of) via open deposition</li> <li>yes/no</li> <li>Article deposited code or used open code</li> <li>yes/no</li> </ul>
Data belonging to: Decreasing relatedness among mycorrhizal fungi in a shared plant network increases fungal network size but not plant benefit
<p>Dataset beloning to the publication: "Decreasing relatedness among mycorrhizal fungi in a shared plant network increases fungal network size but not plant benefit" in Ecology letters (2021). R script used to analyse the data in the .csv files</p>
Data from: Participatory mapping reveals biocultural and nature values in the shared landscape of a Nordic UNESCO Biosphere Reserve
<p>1. Making the right decisions for sustainable development requires sound knowledge of the values and spatial distribution of the services co-produced by ecosystems and people. UNESCO's Man and the Biosphere programme and associated Biosphere Reserves (BRs) are key learning sites or model regions for sustainable development providing key entry points for transdisciplinary work on sustainable development. However, there is limited research exploring spatial distribution of socio-cultural Ecosystem Service (ES) values in BRs and how those values vary according to the BR zonation.</p> <p>2. We used a transdisciplinary approach to design and implement a public participation geographic information systems (PPGIS) survey in a recently designated BR to (i) asses the spatial distribution of ES values in the different zones, (ii) identify hotspots of ES values, (iii) identify spatial bundles of ES values, and (iv) assess the social-ecological characteristics that determine the distribution of those values.</p> <p>3. We found that stakeholders identify high biocultural ES values, mapping predominantly places for outdoor recreation, biodiversity, agricultural products, and cultural heritage. Buffer zones had high agricultural and cultural heritage values while extractive values were largely absent from cores zones. We identified five spatial ES-value bundles highlighting distinct places important for ES values related to: 'multifunctional landscapes' located close to settlements, 'cultural landscapes' associated with agricultural land, 'wild animal resources' along the coastlines, 'outdoor recreation and biodiversity' and 'passive cultural values' widely distributed in high and moderately populated areas.</p> <p>4. Accessibility to nature was highly important for ES values and people highly value nature close to where they live. We show the importance of biocultural values in the region, and agricultural landscapes were highly valued for multiple ES values beyond agricultural products alone.</p> <p>5. We show that BRs have become places that link cultural heritage, agricultural, and biodiversity values in multifunctional landscapes. We put our findings into the local context and suggest how they can inform land-use planning and management through policies aimed at maintaining key agricultural landscapes that provide social-ecological resilience. Additionally, we discuss the value of our study for the wider BR network and how similar work can contribute to monitoring of BR implementation.</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.