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11,710 results for “interaction”
Agonum_marginatum, Fig_1 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.
<p>New version as *png</p> <p>Agonum_marginatum,</p> <p>Fig_1 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.</p> <p> </p>
Whole genome sequencing of Turkish genomes reveals functional private alleles and impact of genetic interactions with Europe, Asia and Africa.
<p>BACKGROUND:</p> <p>Turkey is a crossroads of major population movements throughout history and has been a hotspot of cultural interactions. Several studies have investigated the complex population history of Turkey through a limited set of genetic markers. However, to date, there have been no studies to assess the genetic variation at the whole genome level using whole genome sequencing. Here, we present whole genome sequences of 16 Turkish individuals resequenced at high coverage (32×-48×).</p> <p>RESULTS:</p> <p>We show that the genetic variation of the contemporary Turkish population clusters with South European populations, as expected, but also shows signatures of relatively recent contribution from ancestral East Asian populations. In addition, we document a significant enrichment of non-synonymous private alleles, consistent with recent observations in European populations. A number of variants associated with skin color and total cholesterol levels show frequency differentiation between the Turkish populations and European populations. Furthermore, we have analyzed the 17q21.31 inversion polymorphism region (MAPT locus) and found increased allele frequency of 31.25% for H1/H2 inversion polymorphism when compared to European populations that show about 25% of allele frequency.</p> <p>CONCLUSION:</p> <p>This study provides the first map of common genetic variation from 16 western Asian individuals and thus helps fill an important geographical gap in analyzing natural human variation and human migration. Our data will help develop population-specific experimental designs for studies investigating disease associations and demographic history in Turkey.</p>
Additive interfacial chiral interaction in multilayers for stabilization of small individual skyrmion at room temperature
<p>International audience Facing the ever-growing demand for data storage will most probably require a new paradigm. Nanoscale magnetic skyrmions are anticipated to solve this issue as they are arguably the smallest spin textures in magnetic thin films in nature. We designed cobalt-based multilayered thin films where the cobalt layer is sandwiched between two heavy metals providing additive interfacial Dzyaloshinskii-Moriya interactions, which reach a value close to 2 mJ m-2 in the case of the Ir|Co|Pt asymmetric multilayers. Using a magnetization-sensitive scanning x-ray transmission microscopy technique, we imaged small magnetic domains at very low field in these multilayers. The study of their behavior in perpendicular magnetic field allows us to conclude that they are actually magnetic skyrmions stabilized by the large Dzyaloshinskii-Moriya interaction. This discovery of stable sub-100 nm individual skyrmions at room temperature in a technologically relevant material opens the way for device applications in a near future. on</p>
PHI-base: the Pathogen-Host Interactions Database, version 5.1
<p><strong>Download the dataset here: <a title="Download PHI-base 5.1" href="https://zenodo.org/records/16738930/files/phi-base_v5.1.zip?download=1">phi-base_v5.1.zip</a></strong></p> <p>The Pathogen–Host Interactions Database (PHI-base) is an online database that catalogues experimentally-verified pathogenicity, virulence and effector genes from fungal, oomycete, and bacterial pathogens, which infect animal, plant, fungal, and insect hosts. PHI-base is a valuable resource in the discovery of genes in medically and agronomically important pathogens, which may be potential targets for chemical intervention.</p> <p>Information in PHI-base is manually curated by domain experts and is supported by strong experimental evidence (for example, gene disruption and gene complementation experiments), as well as references to the literature in which the original experiments are described. Annotations are made using terms from ontologies and controlled vocabularies, including the <a href="https://www.geneontology.org/">Gene Ontology</a> (GO), <a href="https://pubmed.ncbi.nlm.nih.gov/21030441/">Brenda Tissue Ontology</a> (BTO), and the <a href="https://obofoundry.org/ontology/phipo.html">Pathogen–Host Interaction Phenotype Ontology</a> (PHIPO).</p> <p>PHI-base 5 includes data that was curated using a new curation process described in <a href="https://doi.org/10.7554/eLife.84658">Cuzick et. al</a> (2023). Data releases for PHI-base 5 do not use the same schema as data releases from PHI-base 4, but all data records from PHI-base 4 that can be made compatible with the new schema are included with this release. Data releases from PHI-base 4 and PHI-base 5 will occur in parallel until such time that all data from PHI-base 4 can be migrated to PHI-base 5. The PHI-base 4 data releases are available on Zenodo at <a href="https://zenodo.org/doi/10.5281/zenodo.5356870">https://zenodo.org/doi/10.5281/zenodo.5356870</a>.</p> <p>For more information about the planned transition from PHI-base 4 to PHI-base 5, see the <a href="https://phi5.phi-base.org/#/help">Help</a> and <a href="https://phi5.phi-base.org/#/announcements">Announcements</a> page on the PHI-base 5 website.</p> <h2>Release statistics</h2> <p>This version of the PHI-base 5 dataset contains the following types of information:</p> <table style="border-collapse: collapse; border-width: 1px; width: 40.2168%; height: 362.8px;"> <thead> <tr style="height: 19.6px;"> <th style="border-width: 1px; width: 84.846%; height: 19.6px;">Data type</th> <th style="border-width: 1px; width: 15.4054%; height: 19.6px;">Count</th> </tr> </thead> <tbody> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Genes</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">9457</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Interactions</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">31094</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Pathogen species</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">303</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Host species</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">237</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Diseases</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">343</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">References</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">5202</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;"><strong>Annotations</strong></td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;"> </td> </tr> <tr style="height: 10px;"> <td style="border-width: 1px; width: 84.846%; height: 10px;">Pathogen-host interaction phenotype</td> <td style="border-width: 1px; width: 15.4054%; height: 10px;">18260</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Gene-for-gene phenotype</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">452</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Pathogen phenotype</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">9413</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Host phenotype</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">14</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">GO biological process</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">1453</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">GO cellular component</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">85</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">GO molecular function</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">152</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Post-translational modification</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">6</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">Physical interaction</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">53</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">WT RNA expression</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">36</td> </tr> <tr style="height: 19.6px;"> <td style="border-width: 1px; width: 84.846%; height: 19.6px;">WT protein expression</td> <td style="border-width: 1px; width: 15.4054%; height: 19.6px;">2</td> </tr> </tbody> </table> <h2>File contents</h2> <ul> <li> <p><strong>phi-base_v5.1.xlsx</strong>: the PHI-base dataset as an Excel spreadsheet. This format follows the layout of the PHI-base 5 website, with sheets corresponding to the sections of gene pages on the website. This format is designed for use by non-technical users.</p> </li> <li> <p><strong>phi-base_v5.1.json</strong>: the PHI-base dataset in JSON format. This is modelled on the export format used by PHI-Canto, the curation tool used by PHI-base. This format is primarily intended for programmatic usage and has additional information (e.g. metadata for curation sessions) that is not included in the spreadsheet format.</p> </li> <li> <p><strong>phi-base.schema.json</strong>: a <a href="https://json-schema.org/">JSON Schema</a> file for the JSON format of the dataset. This is included as documentation for the fields in the JSON file, but can also be used to validate the dataset.</p> </li> </ul>
Amino Acids Modulate Liquid-Liquid Phase Separation in vitro and in vivo by Regulating Protein-Protein Interactions
<p>The metadata, plots and microscopy images for the manuscript "Amino Acids Modulate Liquid-Liquid Phase Separation in vitro and in vivo by Regulating Protein-Protein Interactions".</p>
Asaia spp. accelerate development of the yellow fever mosquito, Aedes aegypti, via interactions with the vertically transmitted larval microbiome
<p><strong><span>Background:</span></strong><em> Aedes aegypti</em> mosquitoes are the primary vectors of yellow fever, dengue, chikungunya and Zika virus. Control programs primarily rely on insecticide application, which encounter challenges related to efficacy and resistance evolution. Alternative strategies, such as the sterile insect technique, highly depend on efficient mass-rearing of healthy insects prior to mass release. Based on effects seen in other mosquito species, we tested the hypothesis that acetic acid bacteria <span>of the </span><em>Asaia</em> <span>genus are</span> mutualist<span>s</span> for developing <em>Ae. aegypti</em> larvae. We tested for beneficial interactions across three <em>Asaia </em>species and whether <em>Asaia</em> inoculation benefited both axenic and conventionally reared larvae. To better understand the underlying mechanisms, we characterized the larval microbiome<span> </span>using culture-based methods and 16S rRNA gene amplicon sequencing.</p> <p><strong>Results:</strong><span> <span>Even</span></span> though <em>Asaia </em>bacteria were transient members of the gut community in conventionally reared insects<span>, t</span>wo <em>Asaia </em>species accelerated larval development relative to controls.<span> Despite their transient nature, </span>the two mutualist <em>Asaia</em> species had lasting impacts on the larval microbiome, mostly by altering the relative abundance of the most dominant bacteria genera <em>Klebsiella</em> and <em>Pseudomonas</em> and other minor components<span>.</span> Axenic larvae that were inoculated with <em>Asaia </em>were dominated by this group, but always exhibited slower development than conventionally reared insects.</p> <p><strong>Conclusions:</strong> These results reveal <em>Asaia</em> as a poor mutualist for <em>Ae. aegypti</em>, with its<em> </em>positive effect on the host mediated by interactions with other bacteria. A practical application of <em>Asaia </em>for improving mass-rearing efficiency results from the acceleration of development time to pupation by a day.</p>
Data from: Selective social interactions and speed-induced leadership in schooling fish
<p>Experimental datasets for the manuscript:</p> <div>Puy, A., Gimeno, E., Torrents, J., Bartashevich, P., Miguel, M. C., Pastor-Satorras, R., & Romanczuk, P. (2024). Selective social interactions and speed-induced leadership in schooling fish. <em>Proceedings of the National Academy of Sciences</em>, <em>121</em>(18), e2309733121.</div> <div> </div> <p>The datasets provide trajectories of fish. There are 2 recordings with N=39 fish (60 minutes duration) and 6 recordings with N=8 fish (30 minutes duration). The columns are as follows:</p> <ul> <li>Time [frame]: Time of the trajectory in frames.</li> <li>X_0 [px]: Position in the x-coordinate in pixels of the trajectory of individual 0.</li> <li>Y_0 [px]: Position in the y-coordinate in pixels of the trajectory of individual 0.</li> <li>X_1 [px]: Position in the x-coordinate in pixels of the trajectory of individual 1.</li> <li>Y_1 [px]: Position in the y-coordinate in pixels of the trajectory of individual 1.</li> <li>...</li> </ul> <p>Conversion to international units:</p> <ul> <li>50 frames = 1 s.</li> <li>2745 px= 100 cm.</li> </ul>
Raw data for PIP2 interaction with TRPC3, explored through computation and electrophysiology
<p>The transient receptor potential canonical type 3 (TRPC3) channel plays a pivotal role in regulating neuronal excitability within the brain via its constitutive activity. The channel is intricately regulated by lipids and has previously been demonstrated to be positively modulated by PIP2. Using molecular dynamics simulations and patch clamp techniques, we reveal that PIP2 predominantly interacts with TRPC3 at the L3 lipid binding site, located at the intersection of pre-S1 and S1 helices. We propose a novel signal transduction pathway from the L3 through the re-entrant loop to a salt bridge between the TRP helix and S4-S5 linker. Notably, we find that both stimulated and constitutive TRPC3 activity require PIP2. These structural insights into the function of TRPC3 are invaluable for understanding the role of the TRPC subfamily in health and disease in native tissue.</p>
Role of Volcano-Tectonic Interactions During Early-Phase Magma-Assisted Continental Rifting: Supplementary Model Files
<p>Input and output model files for the manual script titlted "Role of Volcano-Tectonic Interactions During Early-Phase Magma-Assisted Continental Rifting" submitted to Journal of Geophysical Research: Solid Earth.</p>
The Effects of Plant-Microbe-Environment Interactions on Mineral Weathering Patterns in a Granular Basalt
<p>Data used in the Milici et al. <em>Geobiology </em>article "The Effects of Plant-Microbe-Environment Interactions on Mineral Weathering in Granular Basalt". The data result from a greenhouse experiment in which 14 genotypes of Alfalfa <em>(Medicago</em> sativa) were grown in an unweathered granular basaltic tephra, exposed to an early successional soil microbial community, and replicated across three different soil moisture treatments. This experiment seeks to identify the roles of vascular plants and soil microbes on mineral weathering. Please see the article for full project description. </p> <p>General File Descriptions:</p> <p>"AllPerformanceGeochem.csv" contains both the performance and geochemistry data associated with each plant grown in the experiment and is used for the majority of the analyses.</p> <p>"FullCensusTimeSeries.csv" contains the growth and survival data for the plants across the entire 3 month duration of the experiment and is used only to calculate survival rate and growth rate.</p> <p>"pottingsoilmass.csv" contains the data for alfalfa grown in potting soil and is used to compare how much the basalt limited plant growth relative to a potting soil control. </p> <p>These data are cleaned and formatted for analysis via the code in the github repository linked to this data repository. </p> <p> </p>
Nanoparticle clustering in supraparticles to control magnetic long-range interactions
<p>This data publication is based on the metadata and datasets underlying the manuscript: Nanoparticle clustering in supraparticles to control magnetic long-range interactions</p> <p>To tailor superparamagnetic iron oxide nanoparticles (SPIONs) to the specific needs of diverse application fields, it is essential to understand not only their intrinsic properties but also their interactions with each other. Theoretical models predicting/explaining the magnetization behavior of macroscopic samples containing millions of SPIONs are intricate due to the complexity of the underlying relaxation mechanisms in alternating fields. This study introduces supraparticles (SPs) as model architectures to empirically investigate magnetic interactions within and between large SPION clusters (> 100 nanoparticles). For this purpose, nanoparticle dispersions containing SPIONs and silica nanoparticles (SiO<sub>2</sub> NPs) as non‐magnetic building blocks are spray‐dried to form binary SPs. Selective salt‐induced agglomeration of the two building block types before spray‐drying is utilized to tailor SP architectures, including control over SPION cluster size, shape, and proximity. Magnetic particle spectroscopy (MPS), operating under ambient conditions, reveals altered magnetization behavior for different cluster structures. Not only the nearest SPION neighbors, but the whole cluster structure up to several micrometers is decisive for the magnetization behavior. This highlights the importance of long‐range magnetic interactions. This work presents a versatile approach for designing model architectures to advance empirical interaction studies between SPIONs in macroscopic samples.</p>
Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks
<p>Gene-environment interaction (GxE) analysis elucidates the interplay between genetic predispositions and environmental influences, offering significant potential for precision medicine. With the increasing use of electronic health records (EHR) linked to genetic data in large-scale biobanks, genome-wide association studies (GWAS) have expanded to encompass complex traits with intricate structures, such as time-to-event and ordinal categorical traits. Although these complex traits convey more phenotypic information, most existing scalable genome-wide GxE analysis approaches only focus on quantitative or binary traits. In this work, we propose a scalable and accurate analysis framework, SPAGxE<sub>CCT</sub>, that is applicable to a wide variety of trait types. We extend SPAGxE to SPAGxE+, which can account for sample relatedness. In addition, we extend SPAGxE<sub>CCT</sub> to SPAGxEmix<sub>CCT</sub>, which accounts for population stratification and is applicable to include individuals from multiple ancestries or admixed populations. We applied SPAGxE<sub>CCT</sub>, SPAGxE+, and SPAGxEmix<sub>CCT</sub> to analyze time-to-event traits in UK Biobank. For the SPAGxE<sub>CCT</sub> analyses, 281,149 White British individuals were included. For the SPAGxE+ analyses, 337,367 WB individuals with sample relatedness were included. For the SPAGxEmix<sub>CCT</sub> analyses, 338,044 individuals from all ancestries were included. SPAGxE<sub>CCT</sub>, SPAGxE+, and SPAGxEmix<sub>CCT</sub> are computationally efficient to analyze large datasets with hundreds of thousands of individuals, can accurately control type I error rates while remaining powerful to identify novel GxE findings.</p>
Global Biotic Interactions: Interpreted Data Products hash://md5/e76bf914309ad27dce6ab911d8854590 hash://sha256/ba79836caab5b7ba2d7d659123d27c89f4ad990bd50f97ded935edee9fbe9f87
<p>Global Biotic Interactions: Interpreted Data Products</p> <p>Global Biotic Interactions (GloBI, https://globalbioticinteractions.org, [1]) aims to facilitate access to existing species interaction records (e.g., predator-prey, plant-pollinator, virus-host). This data publication provides interpreted species interaction data products. These products are the result of a process in which versioned, existing species interaction datasets ([2]) are linked to the so-called GloBI Taxon Graph ([3]) and transformed into various aggregate formats (e.g., tsv, csv, neo4j, rdf/nquad, darwin core-ish archives). In addition, the applied name maps are included to make the applied taxonomic linking explicit. </p> <p>Citation<br>--------</p> <p>GloBI is made possible by researchers, collections, projects and institutions openly sharing their datasets. When using this data, please make sure to attribute these *original data contributors*, including citing the specific datasets in derivative work. Each species interaction record indexed by GloBI contains a reference and dataset citation. Also, a full lists of all references can be found in citations.csv/citations.tsv files in this publication. If you have ideas on how to make it easier to cite original datasets, please open/join a discussion via https://globalbioticinteractions.org or related projects.</p> <p>To credit GloBI for more easily finding interaction data, please use the following citation to reference GloBI:</p> <p>Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.</p> <p>Bias and Errors<br>--------</p> <p>As with any analysis and processing workflow, care should be taken to understand the bias and error propagation of data sources and related data transformation processes. The datasets indexed by GloBI are biased geospatially, temporally and taxonomically ([5], [6]). Also, mapping of verbatim names from datasets to known name concept may contains errors due to synonym mismatches, outdated names lists, typos or conflicting name authorities. Finally, bugs may introduce bias and errors in the resulting integrated data product.</p> <p>To help better understand where bias and errors are introduced, only versioned data and code are used as an input: the datasets ([2]), name maps ([3]) and integration software ([6]) are versioned so that the integration processes can be reproduced if needed. This way, steps take to compile an integrated data record can be traced and the sources of bias and errors can be more easily found.</p> <p>This version was preceded by [7]. </p> <p>Contents<br>--------</p> <p>README:<br>this file</p> <p>citations.csv.gz:<br>contains data citations in a in a gzipped comma-separated values format.</p> <p>citations.tsv.gz:<br>contains data citations in a gzipped tab-separated values format.</p> <p>datasets.csv.gz:<br>contains list of indexed datasets in a gzipped comma-separated values format.</p> <p>datasets.tsv.gz:<br>contains list of indexed datasets in a gzipped tab-separated values format.</p> <p>verbatim-interactions.csv.gz<br>contains species interactions tabulated as pair-wise interaction in a gzipped comma-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources.</p> <p>verbatim-interactions.tsv.gz<br>contains species interactions tabulated as pair-wise interaction in a gzipped tab-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources. </p> <p>interactions.csv.gz:<br>contains species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources.</p> <p>interactions.tsv.gz:<br>contains species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources.</p> <p>refuted-interactions.csv.gz:<br>contains refuted species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources.</p> <p>refuted-interactions.tsv.gz:<br>contains refuted species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources.</p> <p>refuted-verbatim-interactions.csv.gz:<br>contains refuted species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources. </p> <p>refuted-verbatim-interactions.tsv.gz:<br>contains refuted species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources. </p> <p>interactions.nq.gz:<br>contains species interactions expressed in the resource description framework in a gzipped rdf/quads format.</p> <p>dwca-by-study.zip:<br>contains species interactions data as a Darwin Core Archive aggregated by study using a custom, occurrence level, association extension.</p> <p>dwca.zip:<br>contains species interactions data as a Darwin Core Archive using a custom, occurrence level, association extension.</p> <p>neo4j-graphdb.zip:<br>contains a neo4j v3.5.32 graph database snapshot containing a graph representation of the species interaction data.</p> <p>taxonCache.tsv.gz:<br>contains hierarchies and identifiers associated with names from naming schemes in a gzipped tab-separated values format.</p> <p>taxonMap.tsv.gz:<br>describes how names in existing datasets were mapped into existing naming schemes in a gzipped tab-separated values format.</p> <p>References<br>-----</p> <p>[1] Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. doi: 10.1016/j.ecoinf.2014.08.005.</p> <p>[2] Poelen, J. H. (2020) Global Biotic Interactions: Elton Dataset Cache. Zenodo. doi: 10.5281/ZENODO.3950557.</p> <p>[3] Poelen, J. H. (2021). Global Biotic Interactions: Taxon Graph (Version 0.3.28) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4451472</p> <p>[4] Hortal, J. et al. (2015) Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity. Annual Review of Ecology, Evolution, and Systematics, 46(1), pp.523–549. doi: 10.1146/annurev-ecolsys-112414-054400.</p> <p>[5] Cains, M. et al. (2017) Ivmooc 2017 - Gap Analysis Of Globi: Identifying Research And Data Sharing Opportunities For Species Interactions. Zenodo. Zenodo. doi: 10.5281/ZENODO.814978.</p> <p>[6] Poelen, J. et al. (2022) globalbioticinteractions/globalbioticinteractions v0.24.6. Zenodo. doi: 10.5281/ZENODO.7327955.</p> <p>[7] GloBI Community. (2024). Global Biotic Interactions: Interpreted Data Products hash://md5/946f7666667d60657dc89d9af8ffb909 hash://sha256/4e83d2daee05a4fa91819d58259ee58ffc5a29ec37aa7e84fd5ffbb2f92aa5b8 (0.7) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11552565</p> <p>Content References<br>-----</p> <p>hash://sha256/5f4906439eba61f936b3dd7455a62c51656a74206f82d3f654e330fda6fbbe45 citations.csv.gz<br>hash://sha256/c8100368dae39363b241472695c1ae197aaddc6e3d6c0a14f3f5ee704b37f3f6 citations.tsv.gz<br>hash://sha256/e6f4aa897c5b325e444315e021b246ffed07fef764b0de6c0f1b2688bbdf9d0f datasets.csv.gz<br>hash://sha256/e6f4aa897c5b325e444315e021b246ffed07fef764b0de6c0f1b2688bbdf9d0f datasets.tsv.gz<br>hash://sha256/f11dc825609cdb1d4a3e9ba8caca9bf93c90dd6f660c7f6a0c8aa01c035a5e1f dwca-by-study.zip<br>hash://sha256/7f16aacacae74e8b0cdef04c612ba776f508ff7ffe385abc57583e37aec8fe53 dwca.zip<br>hash://sha256/b65e4c9a3615f1386bb97e45fb907d053df55476149aa6d71e6f398351218d0d interactions.csv.gz<br>hash://sha256/0c28032392f82d753690be126805e6334ca46bdc4b5e2102a79b15ce0cc0ba90 interactions.nq.gz<br>hash://sha256/8a7031250c288ba0da3d5cdbedc19d54c2f16ba3aa70d49826a7369b6edeca04 interactions.tsv.gz<br>hash://sha256/d0c0fbf536cc63c004d057efc14600ba8cc5874f401b08f51837273b7854f1bb neo4j-graphdb.zip<br>hash://sha256/50e77636f8b58c040e38b6a70ba7cc8288b190ef252dc0d4eb2f12f4c541e82f README<br>hash://sha256/a74e2a39cfe133ae9de1eeea94f5dda8cbd58cfe61a8ccf91b7c540757719c74 refuted-interactions.csv.gz<br>hash://sha256/37b06e274e41ca749399763989816854101238ade9863365f384a2764c639e9d refuted-interactions.tsv.gz<br>hash://sha256/23315b6cd3fdc91f9c1d5d5bc39fa52cf1cef7a4e97d9d023d452751df13f30e refuted-verbatim-interactions.csv.gz<br>hash://sha256/ff82e40cee4f8a8852d0c241f5027f66157a2b8a9090ffa3a0a329a206828d96 refuted-verbatim-interactions.tsv.gz<br>hash://sha256/f072fbc7affb6e29978c7540af6cdccd3a219a23b0a4765b5bae56bd20df0d88 taxonCache.tsv.gz<br>hash://sha256/cd28c81bb2432646a81ad216bc11818f7568ce81826e0074d9a33579da2c1426 taxonMap.tsv.gz<br>hash://sha256/a1d14aa47806c624cf7e3a8c8236643dcf19ed1835c79c65958f7317ebfb9566 verbatim-interactions.csv.gz<br>hash://sha256/2284434219d5fdab1e2152955f04363852c132b76709c330d33e31517817a82e verbatim-interactions.tsv.gz</p> <p>hash://md5/d6ebf42729d988e15cb30adfa6112234 citations.csv.gz<br>hash://md5/42877ae68e51871b8eb7116e62f6b268 citations.tsv.gz<br>hash://md5/3e437580296fdeff3b6f35d1331db9d1 datasets.csv.gz<br>hash://md5/3e437580296fdeff3b6f35d1331db9d1 datasets.tsv.gz<br>hash://md5/fe88720fd992771bd64bfa220ad6a7d3 dwca-by-study.zip<br>hash://md5/cbe132a9288feaef2f3e0c0409b8dc2f dwca.zip<br>hash://md5/051f6db667c4b84616223c2776464dbf interactions.csv.gz<br>hash://md5/b66857f8750e56ba9abe484b1f72eac4 interactions.nq.gz<br>hash://md5/300839c346184b2fedc4e1fb31bcc29c interactions.tsv.gz<br>hash://md5/e79cf5ffee919672f99ea338f3661566 neo4j-graphdb.zip<br>hash://md5/898678f47561d7ef53722bc32957dcd9 README<br>hash://md5/65a185f19df304e53f92a7275f2de291 refuted-interactions.csv.gz<br>hash://md5/bc37a4354f8a2402e9335ae44f28cbd7 refuted-interactions.tsv.gz<br>hash://md5/42e817c31e2ca05e582be94e6ec283c5 refuted-verbatim-interactions.csv.gz<br>hash://md5/93639b70a1d8e47fd194b6384c0287a7 refuted-verbatim-interactions.tsv.gz<br>hash://md5/e32482b3697aa928a5fcb58a570191df taxonCache.tsv.gz<br>hash://md5/75251510925875d3fdc1952cc4b98043 taxonMap.tsv.gz<br>hash://md5/6a0c6224f4a4c3dca9994d70ad0b2fd2 verbatim-interactions.csv.gz<br>hash://md5/905acb49a700e5b5a292be02c917e710 verbatim-interactions.tsv.gz</p>
Data for Publication: "Automated Investigation of Metal-Ligand Interactions by a Newly Established Robotic Workflow for Titrations"
<p>This dataset contains the whole primary and raw (original) data for the manuscript "Automated investigation of metal-ligand interactions by a newly established robotic workflow for titrations".</p>
RNA-Protein Interaction Prediction Using Network-Guided Deep Learning
<p>RNA-protein interactions are critical to various life processes, including fundamental translation and gene regulation. Identifying these interactions is vital for understanding the mechanisms underlying life processes. Then, ZHMolGraph is an advanced pipeline that integrates graph neural network sampling strategy and unsupervised large language models to enhance binding predictions for novel RNAs and proteins.</p> <div> </div>
Global Biotic Interactions: Taxon Graph hash://sha256/0b58753e4ff5519442689d866c0f1d19ffa7d97f917144df1d1cd56ea756921d hash://md5/b23bd0210c88ca10c3e3253091f4fdfa
<p>Global Biotic Interactions: Taxon Cache and Taxon Map</p> <p>Global Biotic Interactions (GloBI) provides access to existing species interaction datasets (Poelen et al. 2014, http://globalbioticinteractions.org). As part of the dataset integration and aggregation, a best effort is made to resolve, match and link taxonomic names and associated vernacular/common names, hierarchies and thumbnails. </p> <p>The data archives included in this publication contain established taxonomic links (taxonMap.tsv.gz) and taxonomic information (taxonCache.tsv.gz) that GloBI retrieved and integrated from taxonomic name sources and web services associated with http://itis.gov, http://globalnames.org, http://eol.org and others open data services. </p> <p>While GloBI is not a naming authority and the primary goal of the name matching process is to detect incorrect or outdates names, the archives may serve as an example of how to publish denormalized taxonomic records and their interrelatioships in a pragmatic way.</p> <p>For related discussion threads, see https://github.com/globalbioticinteractions/globalbioticinteractions/issues/145 , https://github.com/globalbioticinteractions/globalbioticinteractions/issues/274 , https://github.com/globalbioticinteractions/globalbioticinteractions/issues/70 , https://github.com/EOL/tramea/issues/10 and https://github.com/globalbioticinteractions/globalbioticinteractions/issues/274 .</p> <p>Files<br> <br> README <br> this file</p> <p> taxonCache.tsv.gz <br> Taxonomic name, ids, hierarchies, common names and thumbnail associated to taxa known to GloBI. <br> <br> taxonCache.tsv.sha256<br> sha256 hash of taxonCache.tsv</p> <p> taxonCacheFirst10.tsv<br> Header and 10 following lines from taxonCache.tsv</p> <p> taxonCacheFirst10.tsv.sha256<br> sha256 hash of taxonCacheFirst10.tsv<br> <br> taxonMap.tsv.gz <br> Links between taxon name and ids across various taxon providers. </p> <p> taxonMap.tsv.sha256 <br> sha256 hash of taxonMap.tsv</p> <p> taxonMapFirst10.tsv<br> Header and 10 following lines from taxonMap.tsv<br> <br> taxonMapFirst10.tsv.sha256<br> sha256 hash of taxonMapFirst10.tsv</p> <p> prefixes.tsv<br> Term prefixes and their associated uri schemes. </p> <p> names.tsv.gz<br> Corpus of names used to resolve and link. Generated using https://github.com/globalbioticinteractions/elton .</p> <p> names.tsv.sha256<br> sha256 hash of names.tsv</p> <p> namesUnresolved.tsv.gz<br> Names that are not (yet) linked to name sources using https://github.com/globalbioticinteractions/nomer .</p> <p> namesUnresolved.tsv.sha256<br> sha256 hash of namesUnresolved.tsv </p> <p>Column Descriptions</p> <p> taxonCache.tsv.gz </p> <p> 1 | id<br> 2 | name<br> 3 | rank<br> 4 | commonNames<br> 5 | path<br> 6 | pathIds <br> 7 | pathNames<br> 8 | externalUrl<br> 9 | thumbnailUrl<br> <br> taxonMap.tsv.gz</p> <p> 1 | providedTaxonId<br> 2 | providedTaxonName<br> 3 | resolvedTaxonId<br> 4 | resolvedTaxonName</p> <p> names.tsv.gz</p> <p> 1 | providedTaxonId<br> 2 | providedTaxonName</p> <p> namesUnresolved.tsv.gz</p> <p> 1 | providedTaxonId<br> 2 | providedTaxonName</p> <p>References</p> <p>Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.</p> <p>Updates</p> <p>org.globalbioticinteractions.taxon v0.3, 2018-03-02</p> <p>This taxon archive version was created by taking GloBI taxon v0.2 (Jan 2018) and appending a semi-automatically created WikiData taxon mapping and taxon cache.</p> <p>org.globalbioticinteractions.taxon v0.3.1, 2018-04-05</p> <p>This taxon archive version was created by taking GloBI taxon v0.2 (Jan 2018) and appending an automatically created WikiData taxon mapping and taxon cache using Apache Spark scripts at https://github.com/bio-guoda/guoda-datasets/tree/master/wikidata .</p> <p>org.globalbioticinteractions.taxon v0.3.2, 2018-05-21</p> <p>This taxon archive version includes the following:</p> <p>1. all lines in taxonMap.tsv.gz v0.3.1 that passed all validate-term-link tests defined in nomer v0.0.7 (see https://doi.org/10.5281/zenodo.1249964 or https://github.com/globalbioticinteractions/nomer/releases/tag/0.0.7).</p> <p>2. all lines in taxonCache.tsv.gz. v0.3.1 that passed all validate-term tests defined in nomer v0.0.7 </p> <p>3. all lines in 1. that did *not* pass the validate-term test, were re-resolved using nomer v0.0.7 commands "append globi-enrich" and "append globi-globalnames". Only SAME_AS and SYNONYM_OF matches were used to generate new entries for taxonCache and taxonMap.</p> <p>4. in addition, elton v0.4.5 (see https://doi.org/10.5281/zenodo.1212599 or https://github.com/globalbioticinteractions/elton/releases/tag/0.4.5) was used to generate an up-to-date names list by running the "update" and "names" commands on 18-19 May 2018. Of the resulting names, only id/names pairs that were unknown to the taxon graph were resolved using the "append globi-enrich" and "append globi-globalnames" commands of nomer v0.0.7. Only matches classified as SAME_AS and SYNONYM_OF were used to generate new entries for taxonCache and taxonMap.</p> <p>5. the updated versions of taxonMap.tsv.gz and taxonCache.tsv.gz were produced by appending result of 1., 2., 3. and 4. , removing duplicate lines and sorting the result. </p> <p>6. finally, the resulting taxonMap.tsv.gz. and taxonCache.tsv.gz files were validated using the nomer v0.0.7 validate-term-link and validate-term commands, respectively. The result indicated that all lines (other than the header) passed the validation tests.</p> <p>org.globalbioticinteractions.taxon v0.3.3, 2018-06-12</p> <p>This taxon archive version includes the following:</p> <p>1. normalizing taxonomic ranks using nomer's taxon rank matcher</p> <p>2. include more manual taxonomic name mappings provided by Brian Hayden and collaborators.</p> <p>3. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.1286023 . </p> <p>4. remove mapping to NCBI taxa with name "Small" (and associated OTT).</p> <p><br>org.globalbioticinteractions.taxon v0.3.4, 2018-06-27</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.1286023</p> <p>Please note that nomer and elton rely on web accessible apis like taxonomy resolution services and data portals. This dependence on external web-only accessible services might make reproduction of the results tricky due to network outages, server failures, upgrades, downgrades, data loss and/or abandonment of informatics projects/ datasets. </p> <p>org.globalbioticinteractions.taxon v0.3.5, 2018-06-28</p> <p>1. remove dubious provided name from taxon map. Names include "no name", "unidentified".<br>2. remove dubious mappings to Pavlova (e.g., Unidentified Amoebozoa -> Pavlova). Related to 1.<br>3. remove dubious mappings to resolve taxa that include names like "unidentified" or "organic species"<br>4. removed dubious mappings to "Boiga dendrophila"<br>5. removed dubious mappings from "Chaetognatha" (arrowworm) to a suspected homonym Lepidoptera GBIF:3257692 and IRMNG:1252651<br>6. removed dubious mappings from "small sharks" to multiple NCBI/OTT terms with name "Small"</p> <p>Please note that nomer and elton rely on web accessible apis like taxonomy resolution services and data portals. This dependence on external web-only accessible services might make reproduction of the results tricky due to network outages, server failures, upgrades, downgrades, data loss and/or abandonment of informatics projects/ datasets.</p> <p>org.globalbioticinteractions.taxon v0.3.6, 2018-09-10</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.1286023</p> <p>org.globalbioticinteractions.taxon v0.3.7, 2018-10-18</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.1286023<br>2. remove dubious mapping to Vertebrata (WORMS:370321 , http://www.marinespecies.org/aphia.php?p=taxdetails&id=370321). Also see https://github.com/globalbioticinteractions/globalbioticinteractions/issues/361 .<br>3. remove dubious mapping to NCBITaxon:1585532 (Beta vulgaris/Cercospora beticola mixed EST library). Also see https://github.com/globalbioticinteractions/globalbioticinteractions/issues/346 and https://github.com/Planteome/samara/issues/50 </p> <p>org.globalbioticinteractions.taxon v0.3.8, 2018-11-15</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.1286023</p> <p>org.globalbioticinteractions.taxon v0.3.9, 2018-11-23</p> <p>1. label deprecated EOL ids by applying patches in http://doi.org/10.5281/zenodo.1495266 to taxonMap.tsv.gz and taxonCache.tsv.gz . Related to https://github.com/globalbioticinteractions/globalbioticinteractions/issues/383 .<br>2. remove all Encyclopedia of Life thumbnail urls from taxonCache. Related to https://github.com/globalbioticinteractions/globalbioticinteractions/issues/381 .<br>3. remove Encyclopedia of Life external urls associated with deprecated ids from taxonCache. </p> <p><br>org.globalbioticinteractions.taxon v0.3.10, 2018-11-26</p> <p>1. Remove suspicious name mappings related to Humpback scorpionfish (Scorpaenopsis gibbosa) by applying patch published in Poelen, Jorrit H. (2018). Global Biotic Interactions: Taxon Graph Patches (Version 0.2. [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1560662 </p> <p>org.globalbioticinteractions.taxon v0.3.11, 2018-12-21</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.1286023<br>2. remove suspicious name mappings using: ```zcat taxonMap.tsv.gz | grep -v -i -P "\tnone\t" | grep -v -P "(GBIF|IRMNG):.*\tBrachyura$" | grep -v -P "Gamarus" | grep -v -P "^EOL:1047365\ttrachurus trachurus" | grep -v -P "Loros\t.*Psittacidae" | grep -v -P "(GBIF|IRMNG).*Lucifer$" | grep -v -P "GBIF.*Diadema$" | gzip > taxonMapUpdated.tsv.gz```</p> <p>org.globalbioticinteractions.taxon v0.3.12, 2019-06-05</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteractions.taxon v0.3.13, 2019-06-12</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteractions.taxon v0.3.14, 2019-08-19</p> <p>1. revisit deprecated EOL ids by applying patches in http://doi.org/10.5281/zenodo.3371634 to taxonMap.tsv.gz and taxonCache.tsv.gz . Related to https://github.com/jhpoelen/eol-globi-data/issues/403 .</p> <p>org.globalbioticinteractions.taxon v0.3.15, 2019-08-26</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteractions.taxon v0.3.16, 2019-09-22</p> <p>1. revisit deprecated EOL ids by applying patches in http://doi.org/10.5281/zenodo.3457626 to taxonMap.tsv.gz and taxonCache.tsv.gz of http://doi.org/10.5281/zenodo.3378125. Related to https://github.com/globalbioticinteractions/globalbioticinteractions/issues/408 .</p> <p>org.globalbioticinteractions.taxon v0.3.17, 2019-09-27</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteractions.taxon v0.3.18, 2019-10-30</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteractions.taxon v0.3.19, 2019-11-07</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteractions.taxon v0.3.20, 2020-01-17</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteractions.taxon v0.3.21, 2020-03-11</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteractions.taxon v0.3.22, 2020-04-14</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteractions.taxon v0.3.23, 2020-05-22</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteractions.taxon v0.3.24, 2020-06-23</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteractions.taxon v0.3.25, 2020-08-19</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteraction.taxon v0.3.26, 2020-10-01</p> <p>1. adding links to Plazi treatment via nomer append plazi (see https://github.com/globalbioticinteractions/nomer/issues/23)<br>by applying patches available via https://doi.org/10.5281/zenodo.4062711 .</p> <p>org.globalbioticinteraction.taxon v0.3.27, 2020-10-22</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteraction.taxon v0.3.28, 2021-01-19</p> <p>1. update taxonCache and taxonMap using patch 20210114-01 available via Poelen, Jorrit H. (2021). Global Biotic Interactions: Taxon Graph Patches (Version 0.6) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4451462 .</p> <p>org.globalbioticinteractions.taxon v0.3.29, 2021-01-26</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteractions.taxon v0.3.30, 2021-03-10</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteractions.taxon v0.3.31, 2021-03-31</p> <p>1. update taxonCache and taxonMap using patch 20210331-01 available via Poelen, Jorrit H. (2021). Global Biotic Interactions: Taxon Graph Patches (Version 0.7) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4655153 .</p> <p>org.globalbioticinteractions.taxon v0.3.32, 2021-05-12</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558<br>2. remove suspicious mappings from Fungal to some virus name described in https://www.gbif.org/species/4904189 Fungal see https://github.com/globalbioticinteractions/mangal/issues/1#issuecomment-833956239 .</p> <p>org.globalbioticinteractions.taxon v0.3.33, 2021-06-23</p> <p>1. remove suspicious viral name mappings as reported in https://github.com/globalbioticinteractions/globalbioticinteractions/issues/672 by updating taxonMap.tsv.gz using patch 20210623-01 available via Poelen, Jorrit H. (2021). Global Biotic Interactions: Taxon Graph Patches (Version 0.8) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.5021824 .</p> <p>org.globalbioticinteractions.taxon v0.3.34, 2021-09-24</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteractions.taxon v0.3.35, 2021-11-19</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.3240558</p> <p>org.globalbioticinteractions.taxon v0.3.36, 2022-03-29</p> <p>1. update taxonCache and taxonMap using automated scripts available at https://doi.org/10.5281/zenodo.6394931</p> <p>org.globalbioticinteractions.taxon v0.4.0, 2023-03-21</p> <p>1. update elton, nomer, and globi taxon graph versions<br>2. attempt to align all names, including those aligned previously. Replaced incremental name alignment. Incremental name alignment was a optimization needed because of web api performance. Now, no web apis are used, so the optimization is no longer needed.<br>take names from https://globalbioticinteractions.org/data verbatim-interactions.tsv.gz instead of parsing verbatim names from their sources</p> <p>org.globalbioticinteractions.taxon v0.4.1, 2023-03-23</p> <p>update taxon graph build script to fit into existing taxonMap/taxonCache schema<br>fix various bugs<br>remove internal validation until a more up-to-date validation method is available</p> <p>org.globalbioticinteractions.taxon v0.4.2, 2022-10-14</p> <p>update taxonCache and taxonMap using automated scripts available at globalbioticinteractions. (2023). globalbioticinteractions/taxon-graph-builder: 0.0.7 (0.0.7). Zenodo. https://doi.org/10.5281/zenodo.10037579</p> <p>org.globalbioticinteractions.taxon v0.4.3, 2022-10-26</p> <p>apply patch 20231026-01 to address https://github.com/globalbioticinteractions/globalwebdb/issues/1 and https://discuss.eol.org/t/questionable-link-in-trophic-web-for-white-tailed-jackrabbit/2296</p> <p>org.globalbioticinteractions.taxon v0.4.4, 2022-10-26</p> <p>apply patch 20231026-02 to continue to work towards addressing https://github.com/globalbioticinteractions/globalwebdb/issues/1 and https://discuss.eol.org/t/questionable-link-in-trophic-web-for-white-tailed-jackrabbit/2296</p> <p>org.globalbioticinteractions.taxon v0.4.5, 2022-10-26</p> <p>apply patch 20231026-03 to continue to work towards addressing https://github.com/globalbioticinteractions/globalwebdb/issues/1 and https://discuss.eol.org/t/questionable-link-in-trophic-web-for-white-tailed-jackrabbit/2296</p> <p>org.globalbioticinteractions.taxon v0.4.6, 2024-06-17</p> <p>apply patch 20240617 to work towards addressing suspicious Candidatus name mapping reported in https://github.com/globalbioticinteractions/globalbioticinteractions/issues/968</p> <p>org.globalbioticinteractions.taxon v0.5.0, 2024-07-05</p> <p>1. update taxonCache and taxonMap using automated scripts available via Taxon Graph Builder v0.1.0 https://github.com/globalbioticinteractions/taxon-graph-builder/releases/tag/0.1.0 and/or https://doi.org/10.5281/zenodo.1286023 . </p> <p>org.globalbioticinteractions.taxon v0.5.1, 2024-07-08</p> <p>1. update taxonCache and taxonMap using automated scripts available via Taxon Graph Builder v0.1.1 https://github.com/globalbioticinteractions/taxon-graph-builder/releases/tag/0.1.1 and/or https://doi.org/10.5281/zenodo.12687693 . </p> <p>org.globalbioticinteractions.taxon v0.5.2, 2024-07-11</p> <p>1. update taxonCache and taxonMap using automated scripts available via Taxon Graph Builder v0.1.2 https://github.com/globalbioticinteractions/taxon-graph-builder/releases/tag/0.1.2 and/or https://doi.org/10.5281/zenodo.12687693 . </p> <p>org.globalbioticinteractions.taxon v0.5.3, 2024-07-24</p> <p>1. update taxonCache and taxonMap using automated scripts available via Taxon Graph Builder v0.1.2 https://github.com/globalbioticinteractions/taxon-graph-builder/releases/tag/0.1.2 and/or https://doi.org/10.5281/zenodo.12687693 . </p> <p><br>org.globalbioticinteractions.taxon v0.5.4, 2025-02-12</p> <p>1. update taxonCache and taxonMap using automated scripts available via Taxon Graph Builder v0.1.2 https://github.com/globalbioticinteractions/taxon-graph-builder/releases/tag/0.1.2 and/or https://doi.org/10.5281/zenodo.12687693 . </p>
Data for "Detection of metabolite-protein interactions in complex biological samples by high-resolution relaxometry: towards interactomics by NMR"
<p>Raw NMR data for relaxometry experiments, divided by donor sample. For every donor sample 2 or 3 different samples were used in order to record data at 19 different magnetic fields.</p> <p>Data from fast field-cycling relaxometry. All the data is in one xlsx file, divided by donor sample.</p> <p>Relaxometry results for alanine, lactate, creatinine and glutamine, obtained from the fitting of their relaxation decays recorded at 19 different fields, divided by donor sample.</p>
Data set of article entitled: "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study"
<p>Data set of the immagies showed in figure 3 of the article entiteled "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study". All files contain the matrix of the dispersion relations of the two analysed Magnonic Crystals: the SAMPLE A and the SAMPLE B for different value of the interfacial Dzyaloshinskii-Moriya interaction (constant D). The first row is the set of values of k-vector, while the first column is the set of value of the frequencies. The other elements of the matrix are the values of the pixel related to the first row and first column. These elements are been obtanied by the Fast Fourier Transform in time and space of the micromagnetic simulations .</p>
Data from: Reproducibility of the Quantification of Reversible Wall Interactions in VOC Sampling Lines
<p>Dataset from the following publication (<a href="https://doi.org/10.3390/atmos12020280">https://doi.org/10.3390/atmos12020280</a>). In the paper, a method to quantify the amount of substance segregated by reversible interactions on sampling lines is proposed. The areic amount of a VOC (Acetone) interacting with the pipe is measured for a commercial test pipe (Sulfinert®) as the amount of substance per unit area of the internal surface of the test pipe segregated from the flowing gas mixture. The areic amount is function of numerical integrals estimated under different conditions and reproducibility is evaluated. The data used to estimate the integrals described in this work is organised in folders. Each folder correspond to a sample. Sample information is available on Table 3 of the paper.</p>
Influence of biogenic emissions from boreal forests on aerosol-cloud interactions
<p>Datasets that support the major results of the study "Influence of biogenic emissions from boreal forests on aerosol-cloud interactions".</p> <p>Acknowledgements: </p> <p>The work was supported by Academy of Finland via Center of Excellence in Atmospheric Sciences (project no. 272041), Flagship program for Atmospheric and Climate Competence Center (ACCC, 337549, 337552, 337550) and grants 317380, 320094 and 334792, 328290, 302958, 1325656, 316114, 325647, 1325681 and 341271, European Research Council Advanced Grants (227463-ATMNUCLE, 742206-ATM-GTP,) and Starting Grants (638703-COALA, 714621-GASPARCON), the Arena for the gap<br> analysis of the existing Arctic Science Co-Operations (AASCO) funded by Prince Albert Foundation Contract No 2859, and “Quantifying carbon sink, CarbonSink+ and their interaction with air quality” INAR project funded by Jane and Aatos Erkko Foundation. This work was partly supported by the Office of Science (BER), U.S. Department of Energy via BAECC<br> (Petäjä, DE-SC0010711), BAECC-SNEX (Moisseev), European Commission via projects This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 821205 (Understanding and reducing the long-standing uncertainty in anthropogenic aerosol radiative forcing, FORCeS) and ACTRIS, ACTRIS-TNA,<br> ACTRIS2, ACTRIS-IMP, BACCHUS, eLTER, ICOS, PEGASOS and Nordforsk via Cryosphere-Atmosphere Interactions in a Changing Arctic Climate, CRAICC, The BAECC SNEX was also supported by NASA Global Precipitation Measurement (GPM) Mission ground validation program. The deployment of AMF2 to Hyytiälä was enabled and supported by ARM. Argonne National<br> Laboratory's work was supported by the U.S. Department of Energy, Assistant Secretary for Environmental Management, Office of Science and Technology, under contract DE-AC02-06CH11357. The authors gratefully acknowledge the support of AMF2, SMEAR2 and the BAECC community for their support in initiating the BAECC campaign, its implementation,<br> operation, data analysis and interpretation. </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.