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7,849 results for “Conservancy”
Figura 2. Manilkara salzmannii. A in Conservation of Manilkara salzmannii in an Atlantic Forest fragment through the study of fruits and seeds
Figura 2. Manilkara salzmannii. A. tree in natural forest; B. mature fruits collected; C. mature fruit size (scale = 3cm); D. seed size (scale = 1 cm).
Figure 1 in Conservation of Manilkara salzmannii in an Atlantic Forest fragment through the study of fruits and seeds
Figure 1. Geographical location of the study area. A. Municipality of Macaíba, RN, Brazil; B. fragment with a population of Manilkara salzmannii.
The conservation burden of Intact Forest Landscapes (IFLs): A global database of management units and IFLs
<p><strong>Introduction</strong></p> <p>This dataset includes a global overview of publicly available forest Management Units (MUs), Intact Forest Landscapes (IFLs) and their overlap. This includes both the boreal forests of Canada and Russia, and the tropical forests in the Amazon, the Congo basin, South-East Asia. The dataset was developed for the paper "Feasibility and effectiveness of global Intact Forest Landscape protection through forest certification: The conservation burden of Intact Forest Landscapes" by Zwerts et al. (2024). A comprehensive list of MUs with % and absolute overlap with IFLs is presented in Table S1 of Zwerts et al. (2024).</p> <p><strong>Data collection</strong></p> <p>We collected and collated all publicly available MU and IFL data of Central Africa, Southeast Asia, the Amazon, and of the boreal forests in Canada and Russia. As such, we included MU data from Cameroon, Canada, the Central African Republic, the Democratic Republic of Congo, Equatorial Guinea, Gabon, Indonesia, Malaysia, the Republic of Congo and Russia. Together, these forests comprise the majority of all IFLs (Potapov et al., 2017). We utilized the 2020 intact forest landscape (IFL) dataset generated by Potapov et al. (2017). Both FSC-certified and non-FSC MUs were considered and FSC-certification status data was collected using the FSC public dashboard (FSC, 2023). All data was collected in March 2023. Our dataset is not exhaustive. To our knowledge, not all MU data is publicly available. For Southeast Asia no public MU data is available for Papua New Guinea and Peninsular Malaysia. For the Amazon, insufficient public MU data was available to create an accurate representation of the situation. This area was excluded from the main analysis in Zwerts et al., 2024. We included a distinction between FSC-certified and non-FSC MUs in Russia, even though the FSC has withdrawn all certificates in Russia in April 2023 following the invasion of Ukraine. We chose to retain the distinction between FSC and non-FSC MUs for the Russian data because of the uncertainty of the current situation and the significant influence of FSC-certification in the Russian management of IFLs.</p> <p><strong>Overlap analysis</strong></p> <p>All area was transformed to geodesic distance. Furthermore, several MU names were altered because of duplicate names. The number of hectares of MUs that overlap with IFLs was calculated in ArcGIS Pro 3.0.0, using the WGS_1984_Web_Mercator_Auxiliary_Sphere coordinate system. Using the intersect and multipart to singlepart tools every overlap fragment was isolated. For the results in Zwerts et al. (2024) the total overlap and the percentage of overlap was calculated in R. </p> <p><strong>Abstract of the related article</strong></p> <p>Intact Forest Landscapes (IFLs) are defined as forested areas of at least 500 km2 that show no signs of remotely sensed human activity. They are considered to be of high conservation value due to their role in maintaining biodiversity and mitigating climate change. In 2014, the members of the Forest Stewardship Council (FSC), one of the major global certification schemes for responsible forest management, took a conservation stand by restricting logging in FSC-certified IFLs. However, this move raised concerns about the economic viability of FSC-certified logging in these areas. To address these challenges, in 2022, FSC proposed an integrated landscape approach, considering local conditions and stakeholders' needs to balance IFL protection, economic sustainability, and community interests. Here, we leverage publicly available management unit (MU) data, to provide a global quantitative overview of IFLs designated for timber production. We use the concept of 'conservation burden' for the extent that MUs overlap with IFLs, representing the impact that IFL protection has on forest management operations if logging is disallowed. Our data indicates that currently FSC-certified MUs affect 0.6% of global IFLs. Too restrictive policies for logging in IFLs may discourage FSC-certification in global IFLs. Considering the environmental and social benefits of FSC certification, it warrants careful examination whether the benefits of protecting a limited subset of FSC-certified IFLs outweighs the cost of potentially reduced growth of the total FSC-certified area. Our data can provide a basis to facilitate stakeholder engagement for landscape-level IFL management.</p>
Conservation outcomes of dietary transitions across different values of nature - model outputs
<p>Archive of output reports produced for the MAgPIE v4.8.2 paper <strong>Conservation outcomes of dietary transitions across different values of nature</strong>.</p> <p><strong>Data</strong></p> <p>The data contain the MAgPIE model and post-processing outputs for all assessed scenarios, including the sensitivity test across the SSP1 and SSP3 scenarios.</p> <ul> <li>The file <em>rev9_healthyLscps_AOH_full_report.Rds </em>contains the outputs of our Area of Habitat (AOH) assessment for all assessed species and across all scenarios. The summary statistics shown in Figure 2 and Figure 3 have been derived from this data set. The data has also been used to create Extended Data Figures<span lang="EN-GB"> 5-</span>7<span lang="EN-GB"> and is shown in the Supplementary Information. </span>However, for Extended Data Figure<span lang="EN-GB"> 7</span> the data was <span lang="EN-GB">used in combination</span> with <span lang="EN-GB">rasterized </span>range polygons obtained from the <span lang="EN-GB">IUCN Red List Database (IUCN 2020).</span></li> <li><span lang="EN-GB">The file <em>rev9_healthyLscps_pollSuff_report_all.Rds </em>reports the global and regional cropland area (Mha) that is subject to insufficient and sufficient pollination supply. This is specified in the column ‘poll_class’, where 1 is insufficient and 2 is sufficient pollination supply. The data is shown in Figure 4, Extended Data Figure 8 and the Supplementary Information.</span></li> <li><span lang="EN-GB">The file <em>rev9_healthyLscps_RUSLE_report_all.Rds</em> reports estimated global and regional soil loss in Pg per year across all modelled scenarios. The data is displayed in Figure 4, Extended Data Figure 9 and the Supplementary Information.</span></li> <li><span lang="EN-GB">The file <em>rev9_healthyLscps_allSSP_report.rds </em>contains regional and global output variables from the MAgPIE model used in this study. The data is shown in Figure 1 and 5, as well as in Extended Data Figures 2-4 and the Supplementary Information.</span></li> <li><span lang="EN-GB">The file <em>rev9_healthyLscps_validation.rds </em>contains validation data for the MAgPIE model output variables.</span></li> </ul> <p> </p> <p><strong><span lang="EN-GB">Model code</span></strong></p> <p>The model code of the MAgPIE and SEALS models can be accessed via:</p> <p><em>MAgPIE model code</em><em>:</em></p> <ul> <li><a href="https://doi.org/10.5281/zenodo.13833444">https://doi.org/10.5281/zenodo.13833444</a> and <a href="https://github.com/magpiemodel/magpie">https://github.com/magpiemodel/magpie</a></li> </ul> <p><em>MAgPIE model documentation</em><em>:</em></p> <ul> <li><a href="https://rse.pik-potsdam.de/doc/magpie/4.3.5/">https://rse.pik-potsdam.de/doc/magpie/4.8.2/</a></li> </ul> <p><em>SEALS model code</em><em>:</em></p> <ul> <li><a href="https://github.com/jandrewjohnson/seals_dev/releases/tag/v1.0.0">https://github.com/jandrewjohnson/seals_dev/releases/tag/v1.0.0</a></li> </ul> <p><em>SEALS mode documentation:</em></p> <ul> <li><a href="https://justinandrewjohnson.com/earth_economy_devstack/seals_overview.html">https://justinandrewjohnson.com/earth_economy_devstack/seals_overview.html</a></li> </ul> <p> </p> <p><strong><span lang="EN-GB">Cited references</span></strong></p> <p><span>IUCN. (2020). <em>The IUCN Red List of Threatened Species. Version 2020-2</em>. https://www.iucnredlist.org.</span><em><span> </span></em><span>Downloaded on 25 November 2020</span></p>
Data for "Future global mangrove losses and the key role of protected areas in their conservation"
<p>Content (spatial resolution, data info)</p> <ol> <li>Mangrove table input to the machine learning model (1 table)</li> <li>Results of mangrove cumulative loss proportion under SSP1 and SPP5 scenarios (1km, 1 ZIP)</li> <li>All processing R codes can be accessed: https://github.com/2pangp/Mangrove-TidalFlat_Distribution</li> </ol>
Supplementary data for: Chromosome-scale genome assemblies of aphids reveal extensively rearranged autosomes and long-term conservation of the X chromosome
<p><strong><em>Myzus persicae </em>clone O v2 frozen release</strong></p> <p>Genome assembly: Myzus_persicae_O_v2.0.scaffolds.fa.gz</p> <p>BRAKER2 gene models: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3</p> <p>List of gene models containing internal stop codons (removed from the protein and cds fasta files): Myzus_persicae_O_v2.0.scaffolds.braker2.bad_genes.lst</p> <p>BRAKER2 protein sequences: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.cds.fa</p> <p>BRAKER2 coding sequences (longest transcript per gene only): Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.cds.LTPG.fa</p> <p><em>De novo </em>repeat library (ReapeatModeler merged with repbase insecta): Myzus_persicae_O_v2.0_repeat_lib.repeatmodeler_merged_repbase_insecta.fa</p> <p>RepeatMasker transposable element annotation using the <em>M. persicae de novo</em> repeat library: Myzus_persicae_O_v2.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff.out</p> <p>RepeatMasker transposable element annotation using the <em>M. persicae</em> <em>de novo r</em>epeat library (gff format): Myzus_persicae_O_v2.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff</p> <p><strong><em>Acyrthosiphon pisum</em> clone JIC1 v1 frozen release</strong></p> <p>Genome assembly: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.fa.gz</p> <p>BRAKER2 gene models: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff</p> <p>List of gene models containing internal stop codons (removed from the protein and cds fasta files): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.bad_genes.lst</p> <p>BRAKER2 protein sequences: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.cds.fa</p> <p>BRAKER2 coding sequences (longest transcript per gene only): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.cds.LTPG.fa</p> <p><em>De novo </em>repeat library (ReapeatModeler merged with repbase insecta): Acyrthosiphon_pisum_JIC1_repeat_lib.repeatmodeler_merged_repbase_insecta.fa</p> <p>RepeatMasker transposable element annotation using the <em>A. pisum</em> <em>de novo</em> repeat library: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.out</p> <p>RepeatMasker transposable element annotation using the <em>A. pisum de novo</em> repeat library (gff format): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff</p> <p><strong><em>Rhodnius prolixus</em> DNA zoo chromosome-scale genome assembly annotation</strong></p> <p><em>R. prolixus </em>chromosome-scale genome assembly was obtained here: <a href="https://www.dnazoo.org/assemblies/Rhodnius_prolixus">https://www.dnazoo.org/assemblies/Rhodnius_prolixus</a>.</p> <p>Genome assembly: Rhodnius_prolixus-3.0.3_HiC.fasta</p> <p>BRAKER2 gene models: Rhodnius_prolixus-3.0.3_HiC.braker2.gff</p> <p>BRAKER2 protein sequences: Rhodnius_prolixus-3.0.3_HiC.braker2.gff.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Rhodnius_prolixus-3.0.3_HiC.braker2.gff.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Rhodnius_prolixus-3.0.3_HiC.braker2.gff.cds.fa</p> <p><strong><em>Triatoma rubrofasciata</em> chromosome-scale genome assembly annotation</strong></p> <p><em>T. rubrofasciata </em>chromosome-scale genome assembly was obtained here: <a href="http://dx.doi.org/10.5524/100614">http://dx.doi.org/10.5524/100614</a></p> <p>Genome assembly: zhuichun_assembly.fasta</p> <p>BRAKER2 gene models: zhuichun_assembly.braker2.gff</p> <p>BRAKER2 protein sequences: zhuichun_assembly.braker2.gff.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): zhuichun_assembly.braker2.gff.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: zhuichun_assembly.braker2.gff.cds.fa</p> <p><strong>Hemiptera orthogroups and species tree</strong></p> <p>OrthoFinder was used to cluster proteomes of 14 Hemiptera into orthogroups for phylogenomic analysis. All proteomes were reduced to the longest transcript per gene. See here for full details:</p> <p>Species included, taxon IDs and data source:</p> <p>Mcer = Myzus cerasi v1.1 (<a href="https://bipaa.genouest.org/sp/myzus_cerasi/">https://bipaa.genouest.org/sp/myzus_cerasi/</a>)</p> <p>MperO = Myzus persicae clone O v2 (This study)</p> <p>Dnox = Diuraphis noxia Thorpe et. al. gene predictions (<a href="https://bipaa.genouest.org/sp/diuraphis_noxia/">https://bipaa.genouest.org/sp/diuraphis_noxia/</a>)</p> <p>Apis = Acyrthosiphon pisum JIC1 v1 (This study)</p> <p>Pnig = Pentalonia nigronervosa (This study)</p> <p>Rmai = Rhopalosiphum maidis v0.1 (<a href="http://gigadb.org/dataset/100572">http://gigadb.org/dataset/100572</a>)</p> <p>Rpad = Rhopalosiphum padi v1.0 (<a href="https://bipaa.genouest.org/sp/rhopalosiphum_padi/">https://bipaa.genouest.org/sp/rhopalosiphum_padi/</a>)</p> <p>Agly = Aphis glycines biotype 4 v2.1 (<a href="https://zenodo.org/record/3453468#.XnpL5JOgLRY">https://zenodo.org/record/3453468#.XnpL5JOgLRY</a>)</p> <p>BtabMEAM1 = Bemissia tabacci MEAM1 v1.2 (<a href="http://www.whiteflygenomics.org/cgi-bin/bta/index.cgi">http://www.whiteflygenomics.org/cgi-bin/bta/index.cgi</a>)</p> <p>Trub = Triatoma rubrofasciata (This study)</p> <p>Rpro = Rhodnius prolixus (This study)</p> <p>Ofas = Oncopeltus fasciatus OGS v1.0 (<a href="https://i5k.nal.usda.gov/Oncopeltus_fasciatus">https://i5k.nal.usda.gov/Oncopeltus_fasciatus</a>)</p> <p>Sfuc = Sogatella furcifera v1 (<a href="http://dx.doi.org/10.5524/100255">http://dx.doi.org/10.5524/100255</a>)</p> <p>Nlug = Nilaparvata lugens (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0521-0#Sec42">https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0521-0#Sec42</a>)</p> <p>Files:</p> <p>Proteomes included in the analysis: proteomes.tar.gz</p> <p>Orthogroups: Orthogroups.txt</p> <p>Gene counts per orthogroup, per species: Orthogroups.GeneCount.csv</p> <p>Single copy conserved orthogroups used for species tree: SingleCopyOrthogroups.txt</p> <p>Species tree alignment: SpeciesTreeAlignment.fa</p> <p>r8s configuration file (includes time calibrations and OrthoFinder ML species tree with branch lengths): species_tree_rooted.r8s.nex</p> <p>r8s time calibrated species tree: r8s_tree.nwk</p>
Data from: Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management
<p><span>1. Species' ranges are changing at accelerating rates. Species distribution models (SDMs) are powerful tools that help rangers and decision-makers prepare for reintroductions, range shifts, reductions, and/or expansions by predicting habitat suitability across landscapes. Yet, range-expanding or -shifting species in particular face other challenges that traditional SDM procedures cannot quantify, due to large differences between a species' currently-occupied range and potential future range. The realism of SDMs is thus lost and not as useful for conservation management in practice. Here, we address these challenges with an extended assessment of habitat suitability through an <i>integrated SDM database (iSDMdb)</i>.</span></p> <p><span>2. The<i> iSDMdb</i> is a spatial database of predicted sites in a species' prediction range, derived from SDM results, and is a single spatial feature that contains additional, user-friendly data fields that synthesise and summarise SDM predictions and uncertainty, human impacts, restoration features, novel preferences in novel spaces, and management priorities. To illustrate its utility<i>,</i> we used the endangered New Zealand sea lion (<i>Phocarctos hookeri</i>). We consulted with wildlife rangers, decision-makers, and sea lion experts to supplement SDM predictions with additional, more realistic, and applicable information for management. </span></p> <p><span>3. Almost half the data fields included in this database resulted from engaging with these end-users during our study. The SDM found 395 predicted sites. However, the <i>iSDMdb</i>'s additional assessments showed that the actual suitability of most sites (90%) was questionable due to human impacts. >50% of sites contained unnatural barriers (fences, grazing grasslands), and 75% of sites had roads located within the species' range of inland movement. Just 5% of the predicted sites were mostly (>80%) protected.</span></p> <p><span>4. Integrating SDM results with supplemental assessments provides a way to address SDM limitations, especially for range-expanding or -shifting species. SDM products for conservation applications have been critiqued for lacking transparency and interpretation support, and ineffectively communicating uncertainty. The <i>iSDMdb</i> addresses these issues and enhances the practical relevance and utility of SDMs for stakeholders, rangers, and decision-makers. We exemplify how to build an <i>iSDMdb</i> using open-source tools, and how to make diverse, complex assessments more accessible for end-users.</span></p>
Fig. 1 in Taxonomy and conservation of the genus Noronhia Thouars (Oleaceae) in Mauritius
Fig. 1. – Living plants of NoronHia Thouars on Mauritius. A. NoronHia broomeana Oliv. var. broomeana; B-C. Close-up of NoronHia macropHylla (Baker) Hong-Wa & Callm.; D. NoronHia obovata (Baker) Hong-Wa & Callm. [A: V. Florens & Baider CB 2367; B-C: Baider s.n., pers. obs.; D: Baider & V. Florens CB 2461] [Photos: A, D: V. Florens; B: C. Baider]
Fig. 2 in Taxonomy and conservation of the genus Noronhia Thouars (Oleaceae) in Mauritius
Fig. 2. – Distribution maps of NoronHia broomeana Oliv. var. broomeana (A), NoronHia macropHylla (Baker) Hong-Wa & Callm. (B), and NoronHia obovata (Baker) Hong-Wa & Callm. (C): Current localities (circles), historical localities (stars).
Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants.
<p>Datasets: <em>CommonClinvar</em> and <em>NewClinvar</em></p> <p>Authors: Emidio Capriotti and Piero Fariselli</p> <p>Article: Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants.<br> </p>
Fig. 1 in Conserving biodiversity through ecological restoration: the potential contributions of botanical gardens and arboreta
Fig. 1. − Restoration in action. A. Remnant native forest being restored by protection, enrichment planting of native species, and the elimination of fire through using fire breaks; B. Firebreaks and controlled burning protect native forest from annual fires that burn throughout much of Madagascar's central plateau; C. Thicket vegetation with a monoculture of common buckthorn (Rhamnus cathartica L.), one of the region's worst invasive species; D. View of a section of McDonald Woods that has been restored by removing buckthorn, planting seedlings of native species, and returning the prescribed fire regime.
Fig. 3 in The vascular plant diversity of Burkina Faso (West Africa) - a quantitative analysis and implications for conservation
Fig. 3. – Origin of introduced plant species in Burkina Faso. The majority of introduced species originates in the Americas.
Fig. 6 in The vascular plant diversity of Burkina Faso (West Africa) - a quantitative analysis and implications for conservation
Fig. 6. – Province species richness in relation to province characteristics. Species richness per province is shown dependent on 4 factors.
Fig. 1 in The vascular plant diversity of Burkina Faso (West Africa) - a quantitative analysis and implications for conservation
Fig. 1. – The provinces of Burkina Faso and their assignment to the phytogeographic zones used in this study. The classification of provinces to the PGZs is modified after WHITE (1983) and GUINKO (1984a). [1: Les Balé; 2: Bam; 3: Banwa; 4: Bazègua. 5: Bougouriba; 6: Boulgou; 7: Boulkiemdé; 8: Ganzourgou; 9: Gnagna; 10: Gourma; 11: Houet; 12: Ioba; 13: Kadiogo; 14: Kénédougou; 15: Comoé; 16: Komandjari; 17: Kompienga; 18: Kossi; 19: Koulpélogo; 20: Kouritenga; 21: Kourwéogo; 22: Léraba; 23: Loroum; 24: Mouhoun; 25: Nahouri; 26: Namentenga; 27: Nayala; 28: Oubritenga; 29: Oudalan; 30: Passoré; 31: Sanguié; 32: Sanmatenga; 33: Séno; 34: Sissili; 35: Soum; 36: Sourou; 37: Tapoa; 38: Tuy; 39: Yagha; 40: Yatenga; 41: Ziro; 42: Zondoma; 43: Zoundwéogo; 44: Poni; 45: Noumbiel]
Figs 6–13 in Taxonomic Review Of Euphydryas Maturna (Linnaeus, 1758) (Lepidoptera, Nymphalidae) With Description Of A New Subspecies From Dobrogea (Romania) And Notes On Conservation Biology
Figs 6–13. Genital slide of Euphydryas maturna and E. cynthia spp.: 6–7 = E. m. opulenta RÁKOSY et VARGA, ssp. n. (male, paratypes), 8–9 = E. m. partiensis VARGA et SÁNTHA, 1973 (male, paratypes), 10 = E. m. idunides (FRUHSTORFER, 1917) (male), 11 = E. m. maturna (LINNAEUS, 1758) (male), 12 = E. cynthia leonhardi (FRUHSTORFER, 1917), Rila Mts (male), 13 = E. cynthia drenovskyi (RÖBER,
Fig. 15 in Taxonomic Review Of Euphydryas Maturna (Linnaeus, 1758) (Lepidoptera, Nymphalidae) With Description Of A New Subspecies From Dobrogea (Romania) And Notes On Conservation Biology
Fig. 15. Variability in shape of processus posterior of Euphydryas maturna spp.: A–C = E. m. opulenta ssp. n. (Dobrogea); D–H = E. m. partiensis (E Hungary, Guthi forest); I–L = E. m. idunides
Fig. 18 in Taxonomic Review Of Euphydryas Maturna (Linnaeus, 1758) (Lepidoptera, Nymphalidae) With Description Of A New Subspecies From Dobrogea (Romania) And Notes On Conservation Biology
Fig. 18. The results of PCA. The points represent the samples; polygons indicate those samples that originate from the same population. The first two axes explained 43.2% of the total genetic variation
Analyses of human cancer driver genes uncovers evolutionarily conserved RNA structural elements involved in posttranscriptional control - associated datasets
<p>These datasets include raw data output and associated files from ScanFold and CMbuilder analyses of human cancer driver gene mRNA. ScanFold was used to predict RNA regions of unusual thermodynamic stability, and CMbuilder was used to evaluate covariation of the predicted structures in those regions. Please view the file <em>README_general_description_Zenodo_files.txt</em> for brief descriptions of the content.</p> <p>These data are associated with the manuscript entitled <em>Analyses of human cancer driver genes uncovers evolutionarily conserved RNA structural elements involved in posttranscriptional control</em>. The manuscript has currently been submitted for review in PLOS ONE.</p>
Global and regional health and food security under strict conservation scenarios.
<p>Data supporting Nature Sustainability paper 'Global and regional health and food security under strict conservation scenarios'. Zipped folder contains LandSyMM output used to generate results. The tif files are the biodiversity prioritisation areas used in the 50% and 30% strict protection scenarios generated as per the methods in Jung, M., Arnell, A., de Lamo, X. <em>et al.</em> Areas of global importance for conserving terrestrial biodiversity, carbon and water. <em>Nat Ecol Evol</em> <strong>5, </strong>1499–1509 (2021). https://doi.org/10.1038/s41559-021-01528-7. </p>
Fig. 1 in Conserving biodiversity through ecological restoration: the potential contributions of botanical gardens and arboreta
Fig. 1. − Restoration in action. A. Remnant native forest being restored by protection, enrichment planting of native species, and the elimination of fire through using fire breaks; B. Firebreaks and controlled burning protect native forest from annual fires that burn throughout much of Madagascar's central plateau; C. Thicket vegetation with a monoculture of common buckthorn (Rhamnus cathartica L.), one of the region's worst invasive species; D. View of a section of McDonald Woods that has been restored by removing buckthorn, planting seedlings of native species, and returning the prescribed fire regime. [A-B: Ankafobe forest, Madagascar, a restoration site run by the Missouri Botanical Garden; C-D:McDonald Woods, Chicago Botanical Garden, U.S.A.] [Photos: A: J. Leighton Reid; B: C. Birkinshaw; C-D: J. Steffen]
ScienceDex guides
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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.