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Soupis terénních výzkumů Státního archeologického ústavu na Slovensku v letech 1919–1938
<p>Soupis terénních výzkumů Státního archeologického ústavu na Slovensku vznikl jako výstup projektu <strong>Státní archeologický ústav na Slovensku (1919–1939) – prvorepubliková archeologie profesionální i pod vlajkou čechoslovakismu</strong>, který byl v rámci tématu Střední Evropa jako fenomén moderních dějin podpořen výzkumným programem Strategie AV21.</p> <p>Soupis se skládá z textové části (<em>soupis_StAU_2023-12-18.pdf</em>) a dat ve formátu CSV (<em>data_StAU_2023-12-18.csv</em>).</p>
Cooperative Alaska Forest Inventory (CAFI): V - Photo Collection 2022-2024
The CAFI is a repeated forest measurement project established in forest stands throughout interior and southcentral Alaska. The CAFI was launched in 1994 and measurements were done at a 5-year interval until 2015. The project was on hiatus between 2016 and 2019 but picked back up again in 2020 and will continue at a 10-year interval. Total of 205 permanent plots have been established and each plot has been measured up to 6 times. The CAFI is the most extensive forest monitoring program, both in spatial and temporal scale, in interior and southcentral Alaska today. This is the collection of photos taken at the sites.
Dominant contribution of Asgard archaea to eukaryogenesis (2024) Tobiasson, V., Koonin, E. PROCESSED DATA AND METADATA
<h1>Main data deposit for "Dominant contribution of Asgard archaea to eukaryogenesis". </h1> <p>Victor Tobiasson, Jacob Luo, Yuri I Wolf, Eugene V Koonin</p> <p>Computational Biology Branch, Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA</p> <p><strong>The Origin of eukaryotes is one of the key problems in evolutionary biology. The demonstration that the Last Eukaryotic Common Ancestor (LECA) already contained the mitochondrion, an endosymbiotic organelle derived from an alphaproteobacterium, and the discovery of Asgard archaea, the closest archaeal relatives of eukaryotes inform and constrain evolutionary scenarios of eukaryogenesis. We undertook a comprehensive analysis of the origins of the core eukaryotic genes tracing to the LECA within a rigorous statistical framework centered around evolutionary hypotheses testing using constrained phylogenetic trees. The results reveal dominant contributions of Asgard archaea to the origin of most of the conserved eukaryotic functional systems and pathways. A limited contribution from Alphaproteobacteria was identified, primarily relating to the energy transformation systems and Fe-S cluster biogenesis, whereas ancestry from other bacterial phyla was scattered across the eukaryotic functional landscape, without consistent trends. These findings suggest a model of eukaryogenesis in which key features of eukaryotic cell organization evolved in the Asgard ancestor, followed by the capture of the Alphaproteobacterial endosymbiont, and augmented by numerous but sporadic horizontal acquisition of genes from other bacteria both before and after endosymbiosis. </strong></p> <div> <div> <div>Version 0.3, updated 180325</div> <div> </div> <div> </div> <div>Main data repository for:</div> <div>Dominant contribution of Asgard archaea to eukaryogenesis (2024) </div> <div>Tobiasson, V., Koonin, E.</div> <div> </div> <div>Contains all final parsed data from the main Eukaryogenesis project </div> <div>investigating the evolutionary ancetries of eukaryotic protein families. </div> <div> </div> <div>Currently (non-static) available at: </div> <div>https://www.biorxiv.org/content/10.1101/2024.10.14.618318v2</div> <div>https://assets-eu.researchsquare.com/files/rs-5352492/v1/2f9c68ae-cf3e-420a-8d29-867b6fb1a878.pdf</div> <div> </div> <div>All code used to generate the data present within this repository available at: </div> <div>https://github.com/VictorTobiasson/eukgen </div> <div> </div> <div> </div> <div>### General information</div> <div> </div> <div>To identify associations between prokaryotic and eukaryotic protein families, separate</div> <div>hidden Markov model (HMM) databases for prokaryotes and eukaryotes were constructed </div> <div>using a custom, cascaded, sequence-to-profile clustering pipeline, implemented using </div> <div>mmseqs2, followed by a multistep data-reduction and multiple sequence alignment (MSA) </div> <div>procedure to generate HMM profiles using hhsuite. </div> <div> </div> <div>A prokaryotic database of 37 million protein sequences was curated from prokaryotic </div> <div>genomes obtained from the NCBI GenBank in November 2023 and supplemented with proteins </div> <div>extracted from 146 Asgard genome assemblies. To avoid inclusion of genes present only </div> <div>within a narrow subset of species, possibly resulting from horizontal transfer from </div> <div>eukaryotes post LECA, we reconstructed the “soft-core” pangenome for each of the 26 </div> <div>curated prokaryotic taxonomic classes. These pangenomes include only those genes that </div> <div>are present in at least 67% of the families within each class of Bacteria and Archaea. </div> <div>The initial eukaryotic database consisted of 30 million protein sequences from 993 </div> <div>species taken from EukprotV3 and cleaned using mmseqs2 to remove likely prokaryotic </div> <div>contaminants. </div> <div> </div> <div>Both databases were clustered and MSAs constructed for all non, singleton clusters </div> <div>and HMM profiles created. The resulting eukaryotic HMM dataset was queried against </div> <div>the prokaryotic dataset using hhblits to identify sets of homologous protein sequences. </div> <div>Each eukaryotic cluster and all its significant prokaryotic hits constituted an individual</div> <div> sequence set, hereinafter referred to as an Eukaryotic/Prokaryotic Orthologous Cluster </div> <div>(EPOC). The EPOCs constitute groups of homologous proteins from eukaryotes and prokaryotes </div> <div>(each EPOC contains a unique set of eukaryotic proteins, but some clusters of prokaryotic </div> <div>proteins can be present in multiple EPOCs) that were used for phylogenetic tree </div> <div>construction, annotation, and evolutionary hypothesis testing. </div> <div> </div> <div>To infer the most likely prokaryotic ancestry of the eukaryotic proteins in each EPOC, </div> <div>rather than relying on the tree topology directly, we employed a probabilistic approach </div> <div>for evolutionary hypothesis testing using constraint trees. We exhaustively sampled all </div> <div>arrangements of likely sister clades and obtained Expected Likelihood Weights (ELW) for </div> <div>the set of possible sister clade models. As the ELW metric is analogous to model selection </div> <div>confidence, here we take it to be proportional to the probability of a sampled prokaryotic </div> <div>clade to be the true sister group of the given eukaryotic clade among a set of competing </div> <div>sister clades. For each EPOC, our analysis dynamically accounts for long branch outliers </div> <div>and is robust to phylogenetically non-homogenous clades. This analysis is further capable </div> <div>of resolving eukaryotic paraphyly, treating each eukaryotic clade within a EPOC as a </div> <div>single datapoint for downstream analysis. Our resulting data contains EPOCs annotated </div> <div>using profiles generated from KEGG Orthology Groups (KOGs), each with an MSA generated </div> <div>using muscle5, a maximum likelihood tree inferred using IQtree2 and associated ELW values </div> <div>for all candidate prokaryotic sister phyla. The analysis of prokaryotic ancestry was </div> <div>performed only for those eukaryotic clades that included more than 5 distinct taxonomic </div> <div>labels, with at least one coming from Amorphea and one from Diaphoretickes, the two </div> <div>expansive eukaryotic clades considered to represent either the first or the second </div> <div>bifurcation in the evolution of eukaryotes. Thus, these clades likely represent genes </div> <div>mapping back to the LECA.</div> <div> </div> <div>For further details please see main publication or contact</div> <div>victor.tobiasson@nih.gov</div> <div>eugene.koonin@nih.gov</div> <div> </div> <div> </div> <div>### Included files</div> <div> </div> <div>Unless otherwise stated all files contained are tab separated and utf-8 encoded </div> <div>with the first row containing header information. </div> <div>All data entries encoding lists are “|” (pipe) separated. </div> <div>Fields without data values are filled with string entries of “none”.</div> <div> </div> <div>--- Databases ---</div> <div>euk72_ep.tar.gz</div> <div>prok2311_as.tar.gz</div> <div>Prok2311As_final_clusters.tsv</div> <div>Euk72Ep_final_clusters.tsv</div> <div>prok2311_as.hmmDB.tar.gz</div> <div>euk72_ep.hmmDB.tar.gz</div> <div> </div> <div>--- Annotation and Curation ---</div> <div>NCBI_taxonomy_species_addendum.tsv</div> <div>NCBI_taxonomy_class_addendum.tsv</div> <div>Euk72Ep_Prok2311As_final_classes.tsv</div> <div>Euk72Ep_Prok2311As_final_classes.GTDB.tsv</div> <div>KEGG_category_mapping.tsv</div> <div>KEGG_metadata.tsv</div> <div> </div> <div>--- EPOC data ---</div> <div>EPOC_data.tar.gz</div> <div>EPOC_annotation_KEGG.tsv</div> <div>EPOC_data.tsv</div> <div>EPOC_data.pangenomes_s10.tsv</div> <div>EPOC_data.pangenomes_s25.tsv</div> <div>EPOC_data.pangenomes_s67.tsv</div> <div>EPOC_data.GTDB.tsv</div> <div> </div> <div># euk72_ep.tar.gz</div> <div>Gunzip-ed .tar archive containing a single directory with 10 files </div> <div>constituting the initial eukaryotic mmseqs2 database with taxonomy annotation. </div> <div>Constructed from a pre-selected list of 72 eukaryotic proteomes downloaded from </div> <div>NCBI as well as a “clean” version of Eukprot, lacking highly prokaryotic-like </div> <div>contaminant sequences. </div> <div> </div> <div># prok2311_as.tar.gz</div> <div>Gunzip-ed .tar archive containing a single directory with 10 files constituting the </div> <div>initial prokaryotic mmseqs2 database with taxonomy annotation. Constructed from </div> <div>47545 complete genomes retrieved from NCBI in November 2023. </div> <div> </div> <div># prok2311_as.hmmDB.tar.gz</div> <div>Gunzip-ed .tar archive containing 6 files. Comprises an HHSuite Databse formatted </div> <div>from prok2311_as non--singleton clusters, contains 26286 profiles.</div> <div> </div> <div># euk72_ep.hmmDB.tar.gz</div> <div>Gunzip-ed .tar archive containing 6 files. Comprises an HHSuite Databse formatted </div> <div>from euk72_ep non-singleton clusters, contains 1631704 profiles.</div> <div> </div> <div># NCBI_taxonomy_species_addendum.tsv</div> <div>Taxonomy mapping file with manually curated ‘class’ level annotation for poorly </div> <div>annotated species. </div> <div> </div> <div>taxid: NCBI taxid</div> <div>proposed_class_id: Manually assigned NCBI taxid</div> <div>proposed_class_label: NCBI class name</div> <div>org_name: NCBI organism name</div> <div> </div> <div># NCBI_taxonomy_class_addendum.tsv</div> <div>Class revision file mapping poorly populated class level entries to higher order </div> <div>manually curated labels. Also includes information for small classes with shallow </div> <div>taxonomy which are deleted from the EPOC analysis at the level of tree construction.</div> <div> </div> <div>taxid: NCBI taxid</div> <div>ncbi_class: NCBI taxid of rank corresponding to ‘class’ following manual </div> <div>amendment as per NCBI_taxonomy_species_addendum.tsv</div> <div>revised_class_id: Manually assigned NCBI taxid of rank corresponding to ‘class’</div> <div>revised_class_label: Proposed cleartext name of manually revised revised_class_id </div> <div> </div> <div># Euk72Ep_Prok2311As_final_classes.tsv</div> <div>Final taxonomy at NCBI rank ‘class’ following revisions for all sequences in Euk72Ep or </div> <div>Prok2311As. These taxonomic labels are used for EPOC tree annotation. </div> <div> </div> <div>acc: mmseqs database header in either prok2311_as or euk72_ep databases</div> <div>taxid: NCBI taxid for organism</div> <div>superkingdom: Top level NCBI taxonomy classification Bacteria, Archaea or Eukarya, </div> <div>used to define Eukaryotic outgroups in EPOC analysis</div> <div>class: Cleartext name of manually revised NCBI rank ‘class’ identifier for annotation</div> <div> </div> <div># Euk72Ep_Prok2311As_final_classes.GTDB.tsv</div> <div>Final taxonomy at GTDB rank ‘phylum’ transferred using marker genes from GTDB release 220</div> <div> </div> <div>acc: mmseqs database header in either prok2311_as or euk72_ep databases</div> <div>taxid: NCBI taxid for organism</div> <div>superkingdom: Top level NCBI taxonomy classification Bacteria, Archaea or Eukarya, </div> <div>used to define Eukaryotic outgroups in EPOC analysis</div> <div>class: Cleartext name of assigne GTDB phylum</div> <div> </div> <div># Prok2311As_final_clusters.tsv</div> <div>Cluster mapping file for accessions within the initial Prok2311A database to the </div> <div>final clusters used for HMM creation </div> <div> </div> <div>cluster_acc: cluster representative</div> <div>acc: cluster member</div> <div> </div> <div># Euk72Ep_final_clusters.tsv</div> <div>Cluster mapping file for accessions within the initial Prok2311A database to the </div> <div>final clusters used for HMM creation</div> <div> </div> <div>cluster_acc: cluster representative</div> <div>acc: cluster member</div> <div> </div> <div># EPOC_data.tar.gz</div> <div>Gunzip-ed directory containing 16035 EPOC folders. Each folder is named corresponding </div> <div>to the eukaryotic cluster representative which generated its profile as an ID </div> <div>Matches the tree_name field in EPOC_data_prok2311As.tsv</div> <div>contains the following files:</div> <div> </div> <div><EPOC_ID>.merged.fasta: sequences for all members of the EPOC</div> <div><EPOC_ID>.merged.fasta.leaf_mapping: tsv separated file containing taxonomy and tree reduction data</div> <div><EPOC_ID>.merged.fasta.muscle: main cropped MSA for tree generation </div> <div><EPOC_ID>.merged.fasta.muscle.iqtree: IQtree2 output from tree generation</div> <div><EPOC_ID>.merged.fasta.muscle.treefile.annot: annotated newick tree file with final tree</div> <div><EPOC_ID>.merged.tree_data.tsv: final parsed tree data with columns matching EPOC_data_prok2311As.tsv</div> <div> </div> <div>EPOCs with more than one possible eukaryotic sister phyla also contains </div> <div>a folder "constraint_analysis" with constraint tree information used for </div> <div>ELW value calculation. </div> <div> </div> <div># EPOC_data.tsv</div> <div>Main resulting data from all Eukaryotic/Prokaryotic Orthologous Clusters (EPOCs) </div> <div>based on pangenomes defined as including 10% of species per class. This is the main</div> <div>data to be used for genereting the core dataset and for data visualistation</div> <div>Contains information regarding tree breakdown, LCA membership and phylogenetic </div> <div>distances between all detected LCAs. Equivalent to the stacked dataframes from all </div> <div>EPOC directories in EPOC_data </div> <div> </div> <div>tree_name: unique index for each EPOC </div> <div>euk_clade_rep: unique index for each annotated eukaryotic clade within each tree_name</div> <div>euk_clade_size: number of original sequences represented by euk_clade_rep</div> <div>euk_clade_weight: metric for taxonomic purity for each euk_clade_rep</div> <div>euk_leaf_clade: boolean indicating whether euk_clade_rep contains a single leaf</div> <div>euk_LCA: lowest taxa spanning all members in euk_clade_rep</div> <div>euk_scope: list of all taxonomic classes in euk_clade_rep</div> <div>euk_scope_len: length of euk_scope list</div> <div>prok_clade_rep: unique index for each annotated prokaryotic clade for each euk_clade_rep</div> <div>prok_clade_size: number of original sequences represented by prok_clade_rep</div> <div>prok_clade_weight: metric for taxonomic purity for each prok_clade_rep</div> <div>prok_leaf_clade: boolean indicating whether prok_clade_rep contains a single leaf</div> <div>prok_taxa: lowest taxa spanning all members in prok_clade_rep</div> <div>dist: tree-distance from lowest tree node containing all members of prok_clade_rep to lowest tree node containing all members of euk_clade_rep</div> <div>top_dist: graph-distance (node-distance) from lowest tree node containing all members of prok_clade_rep to lowest tree node containing all members of euk_clade_rep</div> <div>raw_stem_length: tree-distance from lowest tree node containing the union of all members of prok_clade_rep and euk_clade_rep to the tree node containing all members of euk_clade_rep</div> <div>median_euk_leaf_dist: median value for all tree distances from the tree node containing all members of euk_clade_rep to the individual leaves</div> <div>stem_length: raw_stem_length/median_euk_leaf_dist</div> <div>logL: log likelihood of best constraint tree constructed</div> <div>deltaL: log likelihood difference between constraint tree for prok_clade_rep and best constraint tree constructed</div> <div>bp-RELL: validation metric from IQtree -trees, see iqtree.org</div> <div>bp-RELL_accept: as above</div> <div>p-KH: as above</div> <div>p-KH_accept: as above</div> <div>p-SH: as above</div> <div>p-SH_accept: as above</div> <div>c-ELW: as above</div> <div>c-ELW_accept: as above</div> <div>p-AU: as above</div> <div>p-AU_accept: as above</div> <div> </div> <div># EPOC_data.pangenomes_s10.tsv</div> <div>Resulting data from all Eukaryotic/Prokaryotic Orthologous Clusters (EPOCs) calculated </div> <div>based on pangenomes defined as including 10% of species per class.</div> <div>Identical file structure to EPOC_data.tsv</div> <div> </div> <div># EPOC_data.pangenomes_s25.tsv</div> <div>Resulting data from all Eukaryotic/Prokaryotic Orthologous Clusters (EPOCs) calculated </div> <div>based on pangenomes defined as including 25% of species per class.</div> <div>Identical file structure to EPOC_data.tsv</div> <div> </div> <div># EPOC_data.pangenomes_s67.tsv</div> <div>Resulting data from all Eukaryotic/Prokaryotic Orthologous Clusters (EPOCs) calculated </div> <div>based on pangenomes defined as including 67% of species per class.</div> <div>Identical file structure to EPOC_data.tsv</div> <div> </div> <div># EPOC_data.GTDB.tsv</div> <div>Resulting data from all Eukaryotic/Prokaryotic Orthologous Clusters (EPOCs) calculated </div> <div>under revised taxonomy from GTDB based on data from Euk72Ep_Prok2311As_final_classes.GTDB.tsv</div> <div>Identical file structure to EPOC_data.tsv</div> <div> </div> <div># EPOC_data.alpha_replicates.tsv</div> <div>Resulting data from 20 repetitions of Eukaryotic/Prokaryotic Orthologous Clusters (EPOCs) calculated </div> <div>from a subset of Alphaproteobacterial-derived EPOCs. </div> <div>Identical file structure to EPOC_data.tsv with the addition of:</div> <div> </div> <div>rep: indicating technical replicate number, 0-19</div> <div> </div> <div># EPOC_annotation_KEGG.tsv</div> <div>Parsed HHblits output of HMM profiles generated from KEGG KOGs (KEGG Orthologous Groups) </div> <div>against eukaryotic profiles constituting each EPOC</div> <div> </div> <div>Query: query name equal to tree_name from EPOC_data</div> <div>Target: target name equal to kogid in KEGG_category_mapping and KEGG_metadata</div> <div>Prob: data from HHblits, see https://github.com/soedinglab/hh-suite/wiki</div> <div>E-value : as above</div> <div>P-value : as above</div> <div>Score: as above</div> <div>SS: as above</div> <div>Cols: as above</div> <div>Identities: as above</div> <div>Similarity: as above</div> <div>Sum_probs: as above</div> <div>Query-HMM-start: as above</div> <div>Query-HMM-end: as above</div> <div>Template-HMM-start: as above</div> <div>Template-HMM-end: as above</div> <div>Template_columns: as above</div> <div>Template_Neff : as above</div> <div>Pairwise_cov: calculated pairwise coverage from Query and Target start and end</div> <div>Description: category_name from KEGG_category_mapping</div> <div> </div> <div># KEGG_category_mapping.tsv</div> <div>Mapping of relevant KOG identifiers to their higher order categories as </div> <div>"Maps" "Modules" or "Reactions" as per KEGG see https://www.kegg.jp/kegg/pathway.html</div> <div> </div> <div>kogid: unique KOG identifier</div> <div>category_id: KEGG map, module, or reaction number</div> <div>category_name: cleartext name for KOG identifier</div> <div> </div> <div># KEGG_metadata.tsv</div> <div>File mapping KOGs to BRITE classification and to additional databases of chemical properties.</div> <div> </div> <div>kogid: unique KOG identifier</div> <div>name: cleartext name for KOG identifier</div> <div>brite_A: list of BRITE-A sets including KOG</div> <div>brite_B: list of BRITE-A sets including KOG</div> <div>brite_C: list of BRITE-A sets including KOG</div> <div>EC: list of Enzyme commission numbers associated with KOG, see https://enzyme.expasy.org/</div> <div>TC: list of transporter classification numbers associated with KOG, see https://www.tcdb.org/</div> <div>RN: list of KEGG reaction numbers associated with KOG</div> <div>CA: list of CAZY numbers associated with KOG, see http://www.cazy.org/</div> <div>GO: list of GO terms associated with KOG, see https://geneontology.org/</div> </div> <div> </div> </div>
AMOC reconstruction between 1981 and 2016 from hydrographic data using an empirical linear regression model from Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, https://doi.org/10.5194/os-17-285-2021, 2021.
<p>Dataset used to create Figure 8 in Worthington et al., 2021 (https://doi.org/10.5194/os-17-285-2021). Details of the data and methods can be found in the journal article.<br> <br> Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, <a href="https://doi.org/10.5194/os-17-285-2021">https://doi.org/10.5194/os-17-285-2021</a>, 2021.</p>
Darwin: an amino acid sequence collection of complete proteomes from eukaryotes with different phylogenetic affinities (v. 03_2020_137)
<p><strong>Background</strong></p> <p>Every time we find an interesting gene in an organism of interest, the first question is often “how widely is this gene distributed in the eukaryotic kingdom?”. Naturally, one could use NCBI BLAST search against the non-redundant sequence database provided by GenBank to answer this question. However, it can be cumbersome to parse the results and assign them to taxonomic units. It is also not straightforward to get an overview of which eukaryotic groups are represented in the results. Top BLAST hits can be crowded with sequences from closely-related organisms making it difficult gain an overview of the overall distribution across eukaryotes. To streamline this process, we developed an in-house database of complete eukaryotic proteomes. We tagged each sequence with a eukaryotic group handle (two-character symbol) and combined them into a single data set searchable by standalone BLAST on one’s own computer. We named this data set “Darwin” to reflect the diverse nature of the sequences it contains. </p> <p><strong>Methods</strong></p> <p>We downloaded predicted proteomes in FASTA format from different sources such as GenBank, Joint Genome Institute (Depart of Energy, USA), Broad Institute (Massachusetts Institute of Technology, USA), Phytozome and a number of other specialized websites catering for a specific organism such as the Arabidopsis Information Resource (TAIR), or the Saccharomyces Genome Database (SGD). All the organisms we included in Darwin are listed in Table 1. To reduce redundancy, we took care not to include the same species more than once unless subspecies were known to show wide diversity. Each sequence header was tagged with a eukaryotic group handle composed of two-character symbols (based on Keeling <em>et al</em>., 2005). These handles clearly appear in BLAST output and can be parsed easily. We combined sequences from all proteomes into a single data set and named it “Darwin”.</p> <p><strong>Results</strong></p> <p>The current version of Darwin (v. 03_2020_137) contains 2,601,132 amino acid sequences from 137 eukaryotes (Table 1, Data file 1). The sizes of the proteomes were diverse, ranging from ~4000 sequences in some alveolates to 60,000-76,000 in plants. Darwin represents most of the supergroups of eukaryotic kingdom described in Keeling <em>et al.,</em> (2005) except those in Rhizaria whose genomes were not available at the time of data set construction. The data set contains larger numbers of proteomes from fungi and plants reflecting areas of interest in our group. </p> <p><strong>Conclusions</strong></p> <p>Darwin is provided as a text fasta file that can be formatted for BLAST searches on standalone computers. The results from the BLAST searches can be parsed to determine how widely a gene of interest is distributed among different eukaryotes. Simple counting of the eukaryotic group handles would also yield an overview of the distribution across taxa. Darwin is also useful for rapidly finding out whether a gene is missing in particular taxa.</p> <p><strong>Reference</strong></p> <p>Keeling PJ, Burger G, Durnford DG, Lang BF, Lee RW, Pearlman RE, Roger AJ, Gray MW (2005) The tree of eukaryotes. <em>Trends Ecol. Evol.</em> <strong>20:</strong> 670-676</p>
WTC1.1 (WikiTailor corpus v. 1.1)
<p> </p><p><strong>Content:</strong></p><p>List of the 743 domains, their term vocabularies in 10 languages, and the Wikipedia articles associated to each domain extracted by the best model described in:</p><p><i> Cristina España-Bonet, Alberto Barrón-Cedeño and Lluís Màrquez. "Tailoring and Evaluating the Wikipedia for in-Domain Comparable Corpora Extraction." </i>Knowledge and Information Systems, Volume 65, pages 1365-1397. 2023. Springer-Verlag, London Ldt. https://doi.org/10.1007/s10115-022-01767-5</p><p><i> https://github.com/cristinae/WikiTailor</i></p><p> </p><p><strong>Files Description:</strong></p><ul><li>commonCats2015.enesdefrcaareuelrooc.tsv</li></ul><p>Multilingual domains listed one per line, languages are separated by a tab in the order en, es, de, fr, ca, ar, eu, el, ro and oc. For each language we include the pair "ID categoryName" separated by a blank space.</p><ul><li>[LAN].0.tar.bz</li></ul><p>A folder per domain for language [LAN] containing the vocabulary and IDs of the extracted articles by the Wikitailor model 50-WT100.</p><ul><li>extraction[LAN]0.tar.bz</li></ul><p>A folder per domain for language [LAN] containing the text of the extracted articles. The name of the file corresponds to the IDs in [LAN].0.tar.bz.</p><p> </p><p> </p>
LMD Inconel 718 V-tracks 2020-07-31
<p>Description of dataset 10.5281/zenodo.3980733</p> <p>Deposition of Inconel 718 single tracks with process parameters:<br> - Nominal power = 300 (W)<br> - Nominal velocities = 350, 600, 900 (mm/min)<br> - Angles = 20°, 45°, 90°<br> - Powder flux = 0.099 (g/s)<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient</p> <p>The dataset is constituted by:<br> - Melt pool images, in file Experiment_2020_7_31__16_19_31.zip, acquired at 200fps with 850 nm narrow band filter, 5ms exposure time. 400x400 px size<br> - trAll.csv containing:<br> - t: timestamp in ms. Synchronized with imAll.csv<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power<br> - V: Vnom in mm/min<br> - imAll.csv containing:<br> - t: timestamp in ms. Synchronized with trAll.csv (some frames may have been lost)<br> - I_mean: mean image intensity (only on red channel)<br> - I_mean_crop: mean image intensity computed on central cropped image area (180x180 pixels)<br> - M_I_mean: I_mean after application of 8-sample moving average<br> - M_I_mean_crop: I_mean_crop after application of 8-sample moving average<br> - fileName: associated image file name<br> - beamON: laserON signal obtained from thresholding on images (background noise = off, minimal intensity level = on)</p>
Datasets for paper 'Cabello, V., Renner, A., Giampietro, M. 2019. Relational analysis of the resource nexus in arid land crop production. Advances in Water Resources 130:258-629'
<p>Datasets produced for the paper Cabello, V., Renner, A., Giampietro, M. 2019.<em> </em>Relational analysis of the resource nexus in arid land crop production. <em>Advances in Water Resources </em>130:258-269</p>
PROBA-V Global Dataset 5 km - BHR
<p>In the framework of the Spot/PROBA-V Surface Aerosol Retrieval at MEP (<a href="mailto:SPAR@MEP">SPAR@MEP</a>) ESA project, the CISAR algorithm, originally developed by Rayference for the joint retrieval of surface reflectance, aerosol and cloud single scattering properties, has been applied to PROBA-V observation globally during 2019 at 5km resolution. CISAR retrieves simultaneously the surface reflectance (represented by the RPV model) and the aerosol optical depth (AOD) in all PROBA-V bands plus the AOD at 500nm, with their corresponding pixel-level uncertainty. The retrieval uncertainty results from the propagation of all input, prior and inversion uncertainty through the inversion process. The processing has been performed in the Mission Exploitation Platform (MEP), developed by VITO. This Global Dataset includes the surface reflectance products. Specification on the filename convention, format and content of the products can be found on the Product Specification Document (PSD).</p> <p>The products related to the <a href="https://zenodo.org/record/7462676">aerosol retrieval</a> products.</p>
PROBA-V Global Dataset 5 km - AOT/COT
<p>In the framework of the Spot/PROBA-V Surface Aerosol Retrieval at MEP (<a href="mailto:SPAR@MEP">SPAR@MEP</a>) ESA project, the CISAR algorithm, originally developed by Rayference for the joint retrieval of surface reflectance, aerosol and cloud single scattering properties, has been applied to PROBA-V observation globally during 2019 at 5km resolution. CISAR retrieves simultaneously the surface reflectance (represented by the RPV model) and the aerosol optical depth (AOD) in all PROBA-V bands plus the AOD at 500nm, with their corresponding pixel-level uncertainty. The retrieval uncertainty results from the propagation of all input, prior and inversion uncertainty through the inversion process. The processing has been performed in the Mission Exploitation Platform (MEP), developed by VITO. This Global Dataset includes products for the aerosol retrieval. Specification on the filename convention, format and content of the products can be found on the Product Specification Document (PSD).</p> <p>The products related to the <a href="https://zenodo.org/record/7457917">surface reflectance</a> products.</p>
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera V: Organic Soil Depth 2022
This dataset contains field-measured characteristics for post-fire residual organic soil samples and for additional organic soil depths collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).
Input Runoff Data for RAPID Model Pre-Processor (RRR) from GLDAS-v.2.0
<p>This database can be used as the input runoff files in the RAPID model [<em>David et al.,</em> 2011] pre-processor (RRR). The runoff files were acquired/derived from the GLDAS-v.2.0 [<em>Rodell et al.,</em> 2004] LSM outputs, available at;</p> <p><a href="http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi">http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi</a></p> <p>The GLDAS-v.2.0 outputs (from NOAH Land Surface Models) are available in 1º, 0.25º with 3-hour temporal resolution. The database contains the following files;</p> <p> GLDAS.2.0_NOAH<em><strong>res</strong></em>_3H_<em><strong>yyyy</strong></em>.tar.gz (Note: <em><strong>res</strong></em> = 10 or 025; <em><strong>yyyy</strong></em> = 2000 to 2009)</p> <p> </p> <p>Note: These runoff data were used by <em>Sikder et al.</em> [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins.</p> <p> </p> <p>Other necessary links associated with this database:</p> <p>RAPID model: <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>RAPID model pre-processor (rrr): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a></p> <p>GLDAS outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS</a></p> <p> </p> <p>References:</p> <p>David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a></p> <p>Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a></p> <p>Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a></p>
Data for "Formation of very large 'blocky alpha' grains in Zircaloy-4" by V. Tong and T.B. Britton published in Acta Materialia (2017)
<p>Data for "Formation of very large ‘blocky alpha’ grains in Zircaloy-4"</p> <p>Vivian S Tong, T Ben Britton<br> Department of Materials, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK</p> <p>For more information please contact: b.britton@imperial.ac.uk (Ben Britton)</p> <p>---</p> <p>Figures_data.xlsx contains the data for line graphs in the following figures on separate labelled sheets:<br> Figure 2(a)<br> Figure 2(b)<br> Figure 4(c)<br> Figure 6.</p> <p>Figures_data.xlsx also contains the HR-EBSD GND density data in Figures 3(b) and 3(d), which have been plotted on a log10 colour scale in the published figure.</p> <p>The EBSD orientation data have been exported as text files (.ctf) directly from Bruker Esprit 2.1 software.</p> <p>Orientations are described using Bruker EBSD software conventions, described in the paper "Tutorial: Crystal orientations and EBSD — Or which way is up?" by Britton et al.(http://dx.doi.org/10.1016/j.matchar.2016.04.008).</p> <p><br> EBSD data is provided for the following figures:<br> Figure 3(a)<br> Figure 3(c)<br> Figure 4(b), Figure 5(c), Figure 7(c) -- these are all the same dataset<br> Figure 5(b)<br> Figure 6 - EBSD maps of these two datsets were not shown, but this is the raw data from which twin fractions were calculated.<br> Figure 7(a)<br> Figure 7(b)</p>
ANEE Lexical Networks v. 2.0 - the dataset
<p>The Dataset of the <a href="https://urn.fi/urn:nbn:fi:lb-2024121101" target="_blank" rel="noopener">ANEE Lexical Networks v.2.0</a> created in the Semantic Domains in Akkadian Texts project at the University of Helsinki. This version 1.1 also includes the networks as GEXF files.</p>
PROBA-V Land Use @100m
<p>The PROBA-V Land Use @100m map for 2015-2020 covers the pan-tropical zone (23°S to 23°N) and consists of six annual maps, each with four bands. Bands 1 (B1) and 2 (B2) represent the land use at the beginning and end of each year, classified into four categories: "oil palm plantation" (value 1), "other perennial plantation" (value 2), "tropical forest" (value 3), and "other land use" (value 0). For full years (2016-2019), change detection runs from January 1 to December 16, with B1 and B2 corresponding to land use just before January 1 (as the first change is detected on this date) and after December 16, respectively. For the first year (2015) the period runs from May 1 to December 16, and for the last year (2020) from January 1 to April 30. Band 3 (B3) assigns a biweekly cutting date and band 4 (B4) assigns a biweekly planting date (value 1-24). </p>
Intrinsic disorder, phase separation and fibrillation by the Henipavirus V and W proteins | Talk- I PhasAGE International Conference
<p>The <strong>I PhasAGE international conference</strong> brought together members of the PhasAGE consortium as well as outstanding international speakers showcasing high impact achievements in the field of liquid-liquid phase separation in aging and late-onset diseases.</p> <p>For details on conference program please see: https://phasage.eu/phasage-conference-1/ </p>
PROBA-V Super-Resolution dataset
<p>The PROBA-V Super-Resolution dataset is the official dataset of <strong>ESA's Kelvins</strong> <strong>competition for "PROBA-V Super Resolution"</strong>. It contains satellite data from 74 hand-selected regions around the globe at different points in time. The data is composed of radiometrically and geometrically corrected Top-Of-Atmosphere (TOA) reflectances for the RED and NIR spectral bands at <strong>300m</strong> and <strong>100m</strong> resolution in Plate Carrée projection. The <strong>300m</strong> resolution data is delivered as <strong>128x128</strong> grey-scale pixel images, the <strong>100m</strong> resolution data as <strong>384x384</strong> grey-scale pixel images. Additionally, a quality map is provided for each pixel, indicating whether the pixels are concealed (i.e. by clouads, ice, water, missing information, etc.).</p> <p>The goal of the challenge can be described as <strong>Multi-Image Super-resolution</strong>: Construct a single high-resolution image out of a series of more frequent low resolution images.</p> <p>Detailed information about the related competition can be found at <a href="https://kelvins.esa.int/proba-v-super-resolution">https://kelvins.esa.int/proba-v-super-resolution</a>.</p> <p>A publication about the generation of this dataset exists as well: </p> <ul> <li><strong>Märtens M., Izzo D., Krzic A. and Cox D.</strong> "Super-resolution of PROBA-V images using convolutional neural networks." Astrodynamics 3.4 (2019): 387-402. (<a href="https://arxiv.org/pdf/1907.01821.pdf">arxiv version</a>)</li> </ul>
Treebank of Artemidorus Onir. book V
<p>Collection of Ancient Greek annotated trees of Artemidorus' Oneirocritica Book 5. Part of the Open Projects in Digital Classics at the College of Letters and Sciences of the State University of São Paulo in Araraquara, São Paulo, Brazil.<br> <br> The trees were annotated manually on Perseids Platform using the Arethusa tool. The treebank tagset and guidelines used were those from <a href="https://github.com/PerseusDL/treebank_data/blob/master/AGDT2/guidelines/Greek_guidelines.md">The Ancient Greek Dependency Treebank </a>with the morphological and syntactic layer, which was based on Bamman's and Crane's 2008 <em>Guidelines for the Syntactic Annotation of the Ancient Greek Dependency Treebank (1.1). </em>We translated it into Portuguese with a few additions from some specifications provided by a forum maintained in 2013 by Alpheios.net, <a href="https://web.archive.org/web/20160401072609/http://treebank.alpheios.net/book">which is no longer online</a>. The trees are visible in Perseids Collection as UNESP-trees at <a href="https://perseids-publications.github.io/unesp-trees">https://perseids-publications.github.io/unesp-trees</a>.</p>
Aligned translation of Artemidorus Onir. book V
<p>Aligned translation of Artemidorus' Oneirocritica Book 5 in 95 chapters and a prologue divided into four sections. Part of the Open Projects in Digital Classics at the College of Letters and Sciences of the State University of São Paulo in Araraquara, São Paulo, Brazil. That is a second version of the translation. It was aligned on the Ugarit Platform and is visible at <a href="http://ugarit.ialigner.com/userProfile.php?userid=15&tgid=9056">https://ugarit.ialigner.com/userProfile.php?userid=15&tgid=14643</a>. The Greek text source was the digitized Pack's 1963 edition from CTS Perseids: urn:cts:greekLit:tlg0553.tlg001.1st1K-grc1:5. The Portuguese text is the revised translation (urn:cts.greekLit:tlg:0553.tlg001.ferreira2:5) of a previous <a href="https://www.culturaacademica.com.br/catalogo/oneirokritika-de-artemidoro-de-daldis-seculo-ii-d-c/">ebook</a> published by Cultura Acadêmica in 2014 (digitized and available at https://furman-editions-in-progress.github.io/UNESP_FU/ as urn:cts:greekLit:tlg0553.tlg001.ferreira1:5).</p>
Global arboreal pollen database for Termination V (404-433 kyr BP)
<p>This dataset is a global compilation of arboreal pollen records covering Termination V (~404-433 kyr BP).<br> 1) Arboreal pollen proxy (Arboreal pollen (AP) or fern spores) percentages were computed for 19 pollen records covering large portions of Termination V (404-433 kyr BP) at a global scale (Sheet 1-19)<br> 2) The compiled arboreal pollen proxy percentages were resampled with a 2-kyr timestep from 405 to 433 kyr BP (Sheet 20)<br> 3) The resampled arboreal pollen proxy percentages were classified in four forest groups defined according to their main bioclimatic specificity (Eurosiberian-Boreoamerican (ES-BA), Subtropical-North, Tropical, Subtropical-South) (Sheet 21). The Eurosiberian-Boreoamerican group (ES-BA), as defined by Braun-Blanquet (1930), covers boreal and temperate forests. Therefore, both Tibetan Plateau pollen records (Heqing and ZB13-C2) are counted in the ES-BA group, the high altitude compensating for their relatively low latitudes. Although Tenaghi Philippon and Lake Ohrid records are on the geographical boundary with the Mediterranean region, they are also classified in the ES-BA group for they rather reflect a temperate forest cover. The Northern Hemisphere Subtropical group (Subtropical-N) encompasses warm temperate forests of the Northern Hemisphere and therefore extends beyond the geographical boundaries of the Mediterranean region (Mediterranean sensu lato). The Tropical group (T) refers to tropical and afromontane forests. Finally, the Southern Hemisphere Subtropical group (Subtropical-S) only includes the MD96-2048 record which is at the limit between tropical and subtropical forests (Table 2). More information on the forest groups is provided in the associated publication: <a href="https://doi.org/10.5194/cp-2021-143">https://doi.org/10.5194/cp-2021-14</a></p>
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.