Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5,145
datasets available to search
ShareScore release 0.7.1
Dataset results
5,145 results for “CO₂”
Figure 4 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic
Figure 4: Optimal quantum yield (FV/FM) of Laminaria digitata (top) and Hedophyllum nigripes (bottom) sporophytes in a temperature gradient (two weeks; left graph) and post-cultivation at 10 °C (one week; right graph). Horizontal lines represent the median; boxes, the interquartile range; whiskers, 1.5× of inter-quartile range (n = 5).
Figure 3 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic
Figure 3: Photographic documentation of Laminaria digitata and Hedophyllum nigripes sporophytes exposed to a temperature gradient after post-cultivation at 10 °C. Images are not to scale. Triangular cuts marked individual sporophytes per replicate.
Figure 2 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic
Figure 2: Relative growth rates (RGR; % d−1) of Laminaria digitata (top) and Hedophyllum nigripes (bottom) sporophytes in a temperature gradient over the experimental time (14 days; left side of the dotted line) and recovery at 10 °C (one week; right side of the dotted line; n = 5, mean ± SD). Each value denotes the RGR between the indicated time point and the measuring day before.
Figure 1 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic
Figure 1: Standardized growth rates (GR) based on surface area (%) of Laminaria digitata (white dots) and Hedophyllum nigripes (black dots) sporophytes over two weeks in a temperature gradient (n = 5, mean ± SD). Different letters denote significant differences within each species (ANOVA with Tukey's post hoc test: α <0.05, A– D = L. digitata; a–c = H. nigripes). Asterisks indicate significant differences between standardized GR of L. digitata and H. nigripes (two-way ANOVA with Tukey's post hoc test).
Figure 5 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic
Figure 5: Density of gametophytes of Laminaria digitata (A, C) and Hedophyllum nigripes (B, D) at day 7 (A, B) and day 14 (C, D) in temperature gradients between 0 and 25 °C (L. digitata) and 22 °C (H. nigripes) (n = 3–4, mean ± SD). Broken horizontal lines show the mean initial gametophyte density for each species after the acclimatization phase (day 0). †All gametophytes died.
GlobalHighCO: Global Daily Seamless 1 km Ground-Level CO Dataset over Land (2018–Present)
<p>GlobalHighCO is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived gapless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level CO dataset over land <strong>from 2019 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.93 and a root-mean-square error (RMSE) of 0.21 mg m<sup>-3</sup> on a daily basis.</p> <p><strong>More GHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
The high dose of biochar reduces polycyclic aromatic hydrocarbons losses during co-composting of sewage sludge and wheat straw
<p>The data presents the raw data of organic solvent extractable polycyclic aromatic hydrocarbons and freely dissolved polycyclic aromatic hydrocarbons and temperature during composting as well as physico-chemical properties of composted material.</p>
Data and scripts for Co-phylogeny, narrow host breadth and local conditions drive highly specialized bird-haemosporidian associations in West-Central African sky islands
<p>This document includes the raw datafiles, host and parasite phylogenies and r-code use to conduct analyses for "Co-phylogeny, narrow host breadth and local conditions drive highly specialized bird-haemosporidian associations in West-Central African sky islands". Please see the readme file to get more detailed information about each file.</p>
Consensus molecular environment of schizophrenia risk genes in co-expression networks shifting across age and brain regions
<p>This is the online data repository accompanying the following manuscript:<br><strong>Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions</strong></p> <p><em>Giulio Pergola<sup>1,2,3,*</sup>, Madhur Parihar<sup>1</sup>, Leonardo Sportelli<sup>1,2</sup>, Rahul Bharadwaj<sup>1</sup>, Christopher Borcuk<sup>2</sup>, Eugenia Radulescu<sup>1</sup>, Loredana Bellantuono<sup>2,5</sup>, Giuseppe Blasi<sup>2,4</sup>, Qiang Chen<sup>1</sup>, Joel E. Kleinman<sup>1,3</sup>, Yanhong Wang<sup>1</sup>, Srinidhi Rao Sripathy<sup>1</sup>, Brady J. Maher<sup>1,3,7</sup>, Alfonso Monaco<sup>5,9</sup>, Fabiana Rossi<sup>1,2</sup>, Joo Heon Shin<sup>1</sup>, Thomas M. Hyde<sup>1,3,6</sup>, Alessandro Bertolino<sup>2,4,*</sup>, Daniel R. Weinberger<sup>1,7,8,*</sup></em></p> <p> </p> <p><strong>Affiliations:</strong></p> <p><em>1)Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (USA)<br>2)Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy<br>3)Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>4)Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy<br>5)Istituto Nazionale di Fisica Nucleare (INFN), Bari, Italy<br>6)Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>7)Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>8)Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>9)Dipartimento Interateneo di fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy</em></p> <p> </p> <p><strong>Abstract:</strong></p> <p><em>Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.</em></p> <p> </p> <p><strong>Citation:</strong> <em>Giulio Pergola et al. ,Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions.Sci. Adv.9, eade2812(2023).DOI:10.1126/sciadv.ade2812</em></p> <p> </p> <p><strong>Data Files:<br>DLPFC hit.genes_kb_200__online.version.zip: </strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For Sankey plots, hover mouse over the links to see the list of genes. Also supports zoom, drag and selection.<br><strong>DLPFC hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>DLPFC all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes<br><strong>DLPFC all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP hit.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only<br><strong>HP hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes<br><strong>HP all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>Modulewise SCZ enrichment(1.0).xlsx:</strong><br>Excel file contains module level SCZ enrichment results for all networks<br><strong>wide_form_test_slidingwindow_NC_SchizoNew(v1.4)_final.xlsx:</strong><br>Excel file contains WGCNA output for sliding window networks<br><strong>wide_form_WGCNA(v3.7.1)_final.xlsx:</strong><br>Excel file contains WGCNA output for our generated networks and from previously published networks<br><strong>libdnetworks(NC).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for age-parsed/nonparsed NC networks (DLPFC, HP, CAUDATE, DENTATE). For fixed window and sliding window study.<br><strong>libdnetworks(SCZ).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for nonparsed SCZ networks (DLPFC, HP, CAUDATE, DENTATE). For the sliding window study.<br><strong>sample_matched_HP_DG_qsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. QSVA removed pipeline. For Cell population enrichment study.<br><strong>sample_matched_HP_DG_noqsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. No QSVA removed pipeline. For Cell population enrichment study.<br><strong>stemcell.preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the iPSC network. For replication in human iPSC data study. Neuronal samples averaged for each “RealGenome”.<br><strong>SCZ.ref.list.sciadv.ade2812.rds</strong>: List of All Biotypes/ Protein Coding Schizophrenia reference genelist for following bins: PGC3, 0 kbp, 20 kbp, 50 kbp, 100 kbp, 150 kbp, 200 kbp, 250 kbp, 500 kbp.</p> <p> </p> <p>Accompanying code can be found at: <a href="https://github.com/LieberInstitute/Brain_WGCNA">https://github.com/LieberInstitute/Brain_WGCNA</a><br>Data from this repository is also available at: <a href="https://nets.libd.org/age_wgcna/">https://nets.libd.org/age_wgcna/</a></p> <p> </p> <p>For any data inquiries please contact:<br><strong>Giulio Pergola: </strong><a href="mailto:Giulio.Pergola@libd.org"><strong>Giulio.Pergola@libd.org</strong></a></p> <p> </p>
Resource Description Framework (RDF) Modeling of Named Entity Co-occurrences in Biomedical Literature and Its Integration with PubChemRDF
<p>This Zenodo record contains the co-occurrence RDF data generated in the work described in the paper “<strong>A resource description framework (RDF) model of named entity co-occurrences in biomedical literature and its integration with PubChemRDF</strong>” by Li et al., published in the Journal of Cheminformatics (<a href="https://doi.org/10.1186/s13321-025-01017-0" target="_blank" rel="noopener">https://doi.org/10.1186/s13321-025-01017-0</a>). It also contains the SPARQL query examples, the RDF schema in SHACL and ShEx, and the validation scripts.</p> <p>All content in this Zenodo record is for archival purposes. The latest version of the co-occurrence RDF data and other PubChemRDF data can be accessed via the PubChem FTP site (<a href="https://ftp.ncbi.nlm.nih.gov/pubchem/RDF/" target="_blank" rel="noopener">https://ftp.ncbi.nlm.nih.gov/pubchem/RDF/</a>). The up-to-date RDF schema in various formats is available on the PubChemRDF Schema page (<a href="https://pubchem.ncbi.nlm.nih.gov/docs/rdf-schema" target="_blank" rel="noopener">https://pubchem.ncbi.nlm.nih.gov/docs/rdf-schema</a>). A set of SPARQL query examples can be found on the PubChemRDF use case pages (<a href="https://pubchem.ncbi.nlm.nih.gov/docs/rdf-use-cases" target="_blank" rel="noopener">https://pubchem.ncbi.nlm.nih.gov/docs/rdf-use-cases</a>).</p>
Data supporting "Slowest-first translation scheme: Structural asymmetry along protein sequences and co-translational folding"
<p>Contains data for a set of 16,200 non-redundant protein structures taken from the Protein Data Bank. Associated code can be found at https://github.com/jomimc/FoldAsymCode.</p>
nal; E', F', internal, G'-J', Pal. 1341 (peripheral 8); G', H', external; I', J', internal; K'-N', Pal. 1343 (peripheral 9); K', L', external; M', N', internal; O'-R', Pal. 1345 (peripheral 10); O', P', external; Q', R', internal; S'-V', Pal. 1348 (peripheral 11); S', T', external; U', V', internal views; W', reconstruction of carapace Thick lines correspond to scute sulci, dotted lines denote plate sutures, and oblique lines indicate missing plate portions. Abbreviations: Ce, cervical; co, costal; Ma, marginal; ne, neural; nu, nuchal; per, peripheral; Pl, pleural; py, pygal; sp, suprapygal; Spr, supracaudal; Ve, vertebral. Scale bars: A-V', 1 cm; W', 2.5 cm. in Fossil turtles from the early Miocene localities of Mokrá-Quarry (Burdigalian, MN4), South Moravian Region, Czech Republic
nal; E', F', internal, G'-J', Pal. 1341 (peripheral 8); G', H', external; I', J', internal; K'-N', Pal. 1343 (peripheral 9); K', L', external; M', N', internal; O'-R', Pal. 1345 (peripheral 10); O', P', external; Q', R', internal; S'-V', Pal. 1348 (peripheral 11); S', T', external; U', V', internal views; W', reconstruction of carapace Thick lines correspond to scute sulci, dotted lines denote plate sutures, and oblique lines indicate missing plate portions. Abbreviations: Ce, cervical; co, costal; Ma, marginal; ne, neural; nu, nuchal; per, peripheral; Pl, pleural; py, pygal; sp, suprapygal; Spr, supracaudal; Ve, vertebral. Scale bars: A-V', 1 cm; W', 2.5 cm.
sal; W, visceral; X-Z, Pal. 1308 (costal 5); X, Y, dorsal; Z, visceral; A'-C', Pal. 1309 (costal 6); A', B', dorsal; C', visceral; D'-F', Pal. 1310 (costal 8); D', E', dorsal; F', visceral; G'-J', Pal. 1312 (peripheral 1); G', H', dorsal; I', J', visceral; K'-N', Pal. 1313 (peripheral 7); K', L', dorsal; M', N', visceral; O'-R', Pal. 1314 (peripheral 8); O', P', dorsal; Q', R', visceral views; S', reconstruction of carapace. Thick lines indicate to scute sulci, dotted lines sutures and oblique lines denote missing plate portions. Abbreviations: Ce, cervical; co, costal; Ma, marginal; ne, neural; nu, nuchal; per, peripheral; Pl, pleural; py, pygal; sp, suprapygal; Ve, vertebral. Scale bars: 1 cm. in Fossil turtles from the early Miocene localities of Mokrá-Quarry (Burdigalian, MN4), South Moravian Region, Czech Republic
sal; W, visceral; X-Z, Pal. 1308 (costal 5); X, Y, dorsal; Z, visceral; A'-C', Pal. 1309 (costal 6); A', B', dorsal; C', visceral; D'-F', Pal. 1310 (costal 8); D', E', dorsal; F', visceral; G'-J', Pal. 1312 (peripheral 1); G', H', dorsal; I', J', visceral; K'-N', Pal. 1313 (peripheral 7); K', L', dorsal; M', N', visceral; O'-R', Pal. 1314 (peripheral 8); O', P', dorsal; Q', R', visceral views; S', reconstruction of carapace. Thick lines indicate to scute sulci, dotted lines sutures and oblique lines denote missing plate portions. Abbreviations: Ce, cervical; co, costal; Ma, marginal; ne, neural; nu, nuchal; per, peripheral; Pl, pleural; py, pygal; sp, suprapygal; Ve, vertebral. Scale bars: 1 cm.
Fig. 2 in Karyotype characterization of Mugil incilis Hancock, 1830 (Mugiliformes: Mugilidae), including a description of an unusual co-localization of major and minor ribosomal genes in the family
Fig. 2. Metaphase plates of Mugil incilis after (a) C-banding and (b) AgNO -staining. Arrows indicate chromosome pair number 1.
Sentiment analysis of tech media articles using VADER package and co-occurrence analysis (01.2016-04.2021)
<p>Sentiment analysis of tech media articles using VADER package and co-occurrence analysis</p> <p>Sources with weights:</p> <ul> <li>Euractiv 5%</li> <li>The Conversation 5%</li> <li>Politico Europe 5 %</li> <li>IEEE Spectrum 5 %</li> <li>Techforge 5%</li> <li>Fastcompany 5%</li> <li>The Guardian (Tech) 12%</li> <li>Arstechnica 5%</li> <li>Reuters 5%</li> <li>Gizmodo 9%</li> <li>ZDNet 9%</li> <li>The Register 12%</li> <li>The Verge 9%</li> <li>TechCrunch 9%</li> </ul> <p>Methodology</p> <p>The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers.</p> <p>As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise.</p> <p>The process included the following main steps:</p> <ul> <li>The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology)</li> <li>The articles containing the given social issue and co-occurring term are identified</li> <li>The identified articles are divided into paragraphs</li> <li>Social issue and co-occurring words are removed from the paragraph</li> <li>The VADER sentiment analysis is carried out for every identified and modified paragraph</li> <li>The average for the given word pair is calculated for the final result</li> </ul> <p>Therefore, the procedure has been repeated for 100 words for all identified social issues.</p> <p>The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down.</p> <p> </p> <p>*Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.</p>
Terrestrial paleoclimate reconstruction of the UK Neogene (?Langhian to Piacenzian) comparing CREST, CRACLE and the Co-existence Approach
<p><strong>Abstract </strong></p> <p>The first detailed reconstruction of the terrestrial paleoclimate development of the UK Neogene (?Langhian to Piacenzian) is presented. The paleoclimate data are derived from the paleobotanical record using two probability-based reconstruction techniques CREST (Climate REconstruction SofTware) (Chevalier et al. 2014) and CRACLE (Climate Reconstruction Analysis using Coexistence Likelihood Estimation) (Harbert & Nixon 2015) that use Bayesian and likelihood estimation probability respectively. The results of these reconstructions are presented alongside reconstructions using the widely-applied Co-existence Approach (CA) (Utescher et al. 2014) for comparison. While all three techniques use the climate requirements of their Nearest Living Relatives as the basis of their reconstruction, they use different database observations. CREST and CRACLE use the GBIF (Global Biodiverstiy Information Facility) (GBIF, 2021) as well as WorldClim inputs for the 19 bioclimate variables used by BIOCLIM (<a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>). Meanwhile, the CA uses the Palaeoflora database, meaning the input for the three models is different. The reconstructions for the UK Neogene palaeoclimate come from 4 localities (12 samples total) spanning the Middle Miocene (Langhian) to Pliocene (Piacenzian): Trwyn y Parc, Anglesey (Middle Miocene), Brassington Formation, Derbyshire (Serravallian-Tortonian), Coralline Crag Formation (latest Zanclean-earliest Piacenzian) and Red Crag Formation (Piacenzian-Gelasian) of southeast England. We present CREST and CRACLE reconstructions of Mean Annual Temperature (MAT), Mean Temperature of Warmest Quarter (MTWQ), Mean Temperature of Coldest Quarter (MTCQ), Mean Annual Precipitation (MAP) and precipitation seasonality (CoV ×100). The CA does not reconstruct MTWQ, MTCQ or precipitation seasonality. Instead, the CA reconstructs Warmest Month Mean Temperature (WMMT) and Coldest Month Mean Temperature (CMMT). The proportion of rainfall falling in the wettest months of the year (RMPwet(%)) was used as a proxy for precipitation seasonality following the methodology of Jacques et al. (2011) and Utescher et al. (2015). The CREST R-code output provides 0.5 and 0.95 (2-σ) uncertainties as well as an optimum and mean for each variable. The CRACLE R-code output provides both parametric and non-parametric joint likelihoods (P-CRACLE and N-CRACLE) with 0.95 (2-σ) uncertainties and a mean that is based on P-CRACLE. The CA generates a minimum and maximum likelihood which together comprise the coexistence interval. The Neogene climate reconstruction of the UK shows a cooling trend from the Langhian to the Pliocene-Pleistocene boundary. CREST and CRACLE produce trends and values consistent with Co-existence Approach data with 0.95 uncertainties overlapping with the CA coexistence interval.</p> <p><strong>File Descriptions </strong></p> <p>Table S1 displays the complete reconstruction for the UK Neogene using CREST, CRACLE and the Co-existence Approach.<br> Table S2 displays detailed site information including: modern and paleo latitude and longitude, dating technique, modern climatology and fossil assemblage diversity (number of fossil taxa versus number of NLRs used for climate reconstruction). Modern climatology has been included to serve as a comparison to the reconstructed Neogene climate. This data has been extracted from WorldClim 2.1 (Fick & Hijmans, 2017).<br> Data Set S1 contains the list of fossil spore and pollen taxa per site and associated Nearest Living Relatives (NLRs), where identifiable, used as the input for CREST, CRACLE and the Co-existence Approach. Relic taxa are included and highlighted in red.<br> Data Set S2 is included to show the effect relic taxa have on paleoclimate reconstructions. The relic taxa are removed following the protocol of Utescher et al. (2014) whereby known relic taxa are removed from analyses to avoid biased reconstructions. Relic taxa removed from analyses include <em>Cathaya</em>, <em>Cryptomeria</em>, <em>Pinus sylvestris</em> and <em>Sciadopitys </em>when present.<br> Data Set S3 is included to show the effects of removing family-level identifications in CRACLE reconstructions. Removing families is shown to generate a less informative reconstruction. Including both genera- and family-level classifications of NLR (Nearest Living Relative) is recommended, however we suggest identifying NLRs (Nearest Living Relatives) to genera-level wherever possible.</p>
Pléiades co- and post-eruption survey in Cumbre Vieja volcano, La Palma, Spain
<p><strong>Introduction</strong>: </p> <p>This repository consists of a series of topographic surfaces of the Cumbre Vieja volcano (La Palma, Spain), presented as a series of Digital Elevation Models (DEMs) obtained from multiple Pléiades stereoscopic surveys acquired from the 22<sup>nd</sup> of September 2021 until the 14<sup>th</sup> of January 2022. We also present a series of grids showing the difference of elevation between the pre-eruption surface and the co- and post-eruption surface, which reveal the lava thickness of the eruption. This was used to calculate the lava volume and effusion rate or Time Average Discharge Rate (TADR) at the time of the Pléiades surveys.</p> <p> </p> <p><strong>Data</strong>: </p> <p>1 – Pléiades stereo images: </p> <p>A pre-eruption Pléiades stereopair was collected from 2013. A total of ten stereopairs were collected between the 23<sup>th</sup> of September 2021 and 2<sup>nd</sup> of October 2021 as part of the CIEST<sup>2</sup> initiative (https://www.poleterresolide.fr/ciest-2-nouvelle-generation-2/). Four additional pairs were acquired between the 11<sup>th</sup> of December 2021 and the 14<sup>th</sup> of January 2022 as part of the Dinamis initiative (https://dinamis.data-terra.org/). However, only some of these stereopairs were acquired with sufficiently large cloud-free areas around the eruption site. The following Pléiades stereo images were processed and are presented in this repository: </p> <p> </p> <table> <tbody> <tr> <td> <p>Date </p> </td> <td> <p>Sensor </p> </td> <td> <p>IDs </p> </td> </tr> <tr> <td> <p>2013-06-30, 12h02m </p> </td> <td> <p>PHR1B </p> </td> <td> <p>5944045101 & 5944046101 </p> </td> </tr> <tr> <td> <p>2021-09-26, 11h58m </p> </td> <td> <p>PHR1B </p> </td> <td> <p>5962414101 & 5962415101 </p> </td> </tr> <tr> <td> <p>2021-10-02, 12h02m </p> </td> <td> <p>PHR1B </p> </td> <td> <p>5988066101 & 5988067101 </p> </td> </tr> <tr> <td> <p>2022-01-01, 12h02m </p> </td> <td> <p>PHR1A </p> </td> <td> <p>6122469101 & 6122470101 </p> </td> </tr> <tr> <td> <p>2022-01-14, 12h02m </p> </td> <td> <p>PHR1B </p> </td> <td> <p>6135055101 & 6135057101 </p> </td> </tr> </tbody> </table> <p>Table 1: Date, sensor and image ID of the Pléiades stereoimages used in this repository. </p> <p> </p> <p>2 – Lidar pre-eruption surface: </p> <p>A lidar survey acquired in 2016 by the Spanish Mapping Agency (IGN, Spain) was downloaded through the portal: <a href="http://centrodedescargas.cnig.es/CentroDescargas/catalogo.do?Serie=LIDAR">http://centrodedescargas.cnig.es/CentroDescargas/catalogo.do?Serie=LIDAR</a>#. Specifically, we used the Digital Surface Model (DSM) product, available in 2x2 m Ground Sampling Distance (GSD). This means that trees and human structures were removed based using classification of the multiple returns of the lidar pulses. The coordinate reference system is REGCAN (UTM zone 28N, EPSG: 32628), and the heights are orthometric, using the height reference system REDNAP, built upon the geoid EGM08. Using the REDNAP geoid model, we converted the heights to meters above ellipsoid (WGS84), since the Pléiades data is acquired with satellite attitudes referred to the ellipsoid WGS84. </p> <p> </p> <p><strong>Methods</strong>:</p> <p>The Pléiades stereoimages were processed using the Ames StereoPipeline (ASP, Shean et al., 2016, see ASP branch in repository), yielding a DEM in 2x2m GSD and an orthoimage in 0.5x0.5m GSD. The processing was done using as only input the stereoimages and their orientation information, as Rational Polynomial Coefficients (RPCs). The <em>parallel_stereo </em>routine performs all the steps needed in the correlation of the stereoimages, yielding a pointcloud which is then interpolated using the routine <em>point2dem</em>. Besides default parameters, the <em>parallel_stereo</em> parameters used for creation of the DEMs were the standard parameters, plus the following ones: </p> <p><em>--corr-tile-size 2048 --sgm-collar-size 256 --corr-seed-mode 3 --corr-max-levels 2 --corr-timeout 900 --cost-mode 3 --subpixel-mode 9 --corr-kernel 7 7 --subpixel-kernel 15 15</em></p> <p>Once the DEM was created, DEM co-registration was applying in order to align and minimize positional biases between the pre-eruption DEM and the Pléiades DEMs. We followed the co-registration method of Nuth & Kääb (2011), implemented by David Shean’s co-registration routines (<a href="https://github.com/dshean/demcoreg">https://github.com/dshean/demcoreg</a>, Shean et al., 2016). The co-registration involved a horizontal and vertical shift of the Pléiades DEMs, as well as a planar tilt correction. The horizontal offset obtained from the DEM co-registration was also applied to the Pléiades orthoimages.</p> <p>Lava outlines were manually digitized from the co-registered Pléiades orthoimages, excluding kipukas and major building constructions which were not covered by the lavas. The lava outlines are available as GeoPackages in the “GPKG” branch of the repository.</p> <p>Lava volume calculations were done using the average lava thickness, multiplied by the area covered by the lavas. The uncertainty in volume was assumed to be the Normalized Mean Absolute Deviation (NMAD, Höhle and Höhle, 2009), multiplied by the lava area. The TADR was calculated as the total volume divided by the time, in seconds, between the start of the eruption, defined as 2021-09-19 11:58:00 local time, and the acquisition of the Pléiades images. For the total TADR, we used the volume extracted from the Pléiades images from the 1<sup>st</sup> of January 2022, divided by the observed time of beginning and end of the eruption, defined as 2021-12-13 22:21:00, local time. The TADR values shown in this repository do not account for submarine lavas nor tephra deposits.</p> <p>In addition, another set of DEMs were produced automatically as soon as the images were made available by the on-demand processing service DSM-OPT provided by <a href="mailto:ForM@Ter">ForM@Ter</a> (https://en.poleterresolide.fr/on-demand-processing/#/mns). This processing is based on Micmac (D. Michéa and J.-P. Malet / EOST; E. Pointal, IPGP, Rupnik, 2017). The DEMs produced correspond to the file created automatically “A2_dsm_denoised.tif”. They were obtained in 1x1 m GSD, and they were cropped over the area of interest. These DEMs have not been co-registered. These data are available in the “MM” branch in the repository. </p> <p> </p> <p><strong>Results: Lava area, volumes and effusion rate:</strong> </p> <p> </p> <table> <tbody> <tr> <td> <p>Date </p> </td> <td> <p>Lava Area (km2) </p> </td> <td> <p>Lava thickness (m) </p> </td> <td> <p>Lava volume (10e+6 m3) </p> </td> <td> <p>TADR </p> <p>(m3 s-1) </p> </td> </tr> <tr> <td> <p>2021-09-26, 11h58m </p> </td> <td> <p>2.6 </p> </td> <td> <p>11.4±1.1 </p> </td> <td> <p>29.8±2.8 </p> </td> <td> <p>49.2±4.7 </p> </td> </tr> <tr> <td> <p>2021-10-02, 12h02m </p> </td> <td> <p>4.3 </p> </td> <td> <p>10.0±1.4 </p> </td> <td> <p>43.0±6.1 </p> </td> <td> <p>38.2±5.4 </p> </td> </tr> <tr> <td> <p>2022-01-01, 12h02m </p> </td> <td> <p>12.25 </p> </td> <td> <p>16.6±1.1 </p> </td> <td> <p>203.3±13.9 </p> </td> <td> <p>27.5±1.9 </p> </td> </tr> </tbody> </table> <p>Table 2: results of lava area, thickness, lava volume and TADR since the start of the eruption. </p> <p> </p> <p><strong>Repository structure:</strong></p> <p>zenodo_lapalma/<br> ├── ASP<br> │ ├── 20130630_1202_lapalma_PL_2x2m_UTM28N_ASP_DEM.tif<br> │ ├── 20210926_1158_lapalma_PL_2x2m_UTM28N_ASP_DEM.tif<br> │ ├── 20210926_1158_lapalma_PL_2x2m_UTM28N_thickness.tif<br> │ ├── 20211002_1202_lapalma_PL_2x2m_UTM28N_ASP_DEM.tif<br> │ ├── 20211002_1202_lapalma_PL_2x2m_UTM28N_thickness.tif<br> │ ├── 20220101_1202_lapalma_PL_2x2m_UTM28N_ASP_DEM.tif<br> │ ├── 20220101_1202_lapalma_PL_2x2m_UTM28N_thickness.tif<br> │ ├── 20220114_1202_lapalma_PL_2x2m_UTM28N_ASP_DEM.tif<br> │ └── 20220114_1202_lapalma_PL_2x2m_UTM28N_thickness.tif<br> ├── GPKG<br> │ ├── 20210925_1202_lapalma_PL_UTM28N_outline.gpkg<br> │ ├── 20211002_1202_lapalma_PL_UTM28N_outline.gpkg<br> │ └── 20220101_1202_lapalma_PL_UTM28N_outline.gpkg<br> └── MM<br> ├── 20130630_1202_PL_1x1m_UTM28N_MM_DEM.tif<br> ├── 20210926_1158_PL_1x1m_UTM28N_MM_DEM.tif<br> ├── 20211002_1230_PL_1x1m_UTM28N_MM_DEM.tif<br> ├── 20220101_1202_PL_1x1m_UTM28N_MM_DEM.tif<br> └── 20220114_1202_PL_1x1m_UTM28N_MM_DEM.tif</p> <p> </p> <p><strong>Acknowledgements</strong>: </p> <p>Pléiades images were provided under the CIEST² initiative (CIEST2 is part of ForM@Ter (<a href="https://en.poleterresolide.fr/">https://en.poleterresolide.fr/</a> ) and supported by ISDeform National Service of Observation) for the reference image acquired in 2013 and from the 23<sup>rd</sup> of September to the 2<sup>nd</sup> of October 2021, and through the Dinamis program (CNES, France) from the 12<sup>th</sup> of December 2021 to the 14<sup>th</sup> of January 2022 (image Pléiades©CNES2013,©CNES2021,©CNES2022, distribution AIRBUS DS) </p> <p> </p> <p><strong>Dataset Attribution</strong> </p> <p>This dataset is licensed under a <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons CC BY-NC 4.0 International License</a> (Attribution-NonCommercial).<br> Attribution required for copies and derivative works:</p> <p>The underlying dataset from which this work has been derived includes Pleiades material ©CNES (2013,2021,2022), distributed by AIRBUS DS, and data provided by the Spanish Mapping Agency (IGN, Spain), all rights reserved.</p> <p> </p> <p><strong>Dataset Citation</strong> </p> <p>Belart and Pinel (2022). “Pléiades co- and post-eruption survey in Cumbre Vieja volcano, La Palma, Spain”. Dataset distributed on Zenodo: 10.5281/zenodo.5833771</p> <p> </p> <p><strong>References:</strong> </p> <p>Höhle, J. and Höhle, M.: Accuracy assessment of digital elevation models by means of robust statistical methods, ISPRS J. Photogramm. Remote Sens., 64, 398–406, https://doi.org/10.1016/j.isprsjprs.2009.02.003, 2009.</p> <p>Nuth, C. and Kääb, A.: Co-registration and bias corrections of satellite elevation datasets for quantifying glacier thickness change, The Cryosphere, 5, 271–290, https://doi.org/10.5194/tc-5-271-2011, 2011.</p> <p>Rupnik, E., Daakir, M., & Deseilligny, M. P.: MicMac – a free, open-source solution for photogrammetry. Open Geospatial Data, Software and Standards, 2(1), 1-9, 2017.</p> <p>Shean, D. E., Alexandrov, O., Moratto, Z. M., Smith, B. E., Joughin, I. R., Porter, C., and Morin, P.: An automated, open-source pipeline for mass production of digital elevation models (DEMs) from very-high-resolution commercial stereo satellite imagery, ISPRS J. Photogramm. Remote Sens., 116, 101–117, https://doi.org/10.1016/j.isprsjprs.2016.03.012, 2016. </p>
Dataset for "Structural Linkage Unit-Dependent Catalytic Activity for CO Oxidation over Cerium Oxysulfate Cluster Assemblies"
<p>Dataset relating to the publication "Structural Linkage Unit-Dependent Catalytic Activity for CO Oxidation over Cerium Oxysulfate Cluster Assemblies".</p> <p>Raw input and output data as well as pre- and postprocessing scripts and figures.</p>
Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback [Dataset]
<p>Dataset used for the paper submitted to RO-MAN 2022</p> <p>Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback<br> Giorgio Nicola, Enrico Villagrossi, Nicola Pedrocchi</p>
Data from: Divergent physiological acclimation responses to warming between two co-occurring salamander species and implications for terrestrial survival
<p>Small differences in physiological responses are known to influence demographic rates such as survival. We tested for differences in the physiological acclimation responses of two closely-related salamander species that often co-occur, <em>Ambystoma maculatum </em>and <em>A. opacum</em>. Specifically, we measured changes in critical thermal maxima (CT<sub>max</sub>), standard metabolic rates (SMRs), and respiratory surface area water loss (RSAWL) following exposure to three temperature treatments under laboratory conditions. While the magnitude of RSAWL and CT<sub>max</sub><em> </em>acclimation responses to warming did not differ between the study species, SMR was maintained across acclimation temperatures among <em>A. maculatum, </em>but declined among <em>A. opacum </em>acclimated to warmer temperatures<em>. </em>Metabolic compensation may facilitate maintained <em>A.</em> <em>maculatum </em>activity levels during warm periods following the relatively cool spring breeding season. In contrast, metabolic suppression may allow <em>A. opacum</em> to conserve energy when exposed to surface conditions during fall breeding and nest guarding. We simulated how these different SMR responses would likely alter post-metamorphic survival in our study species using previously collected data representing six weeks under relatively warm seminatural conditions. Our simulation indicated that, following warming and under identical study conditions, metabolic compensation may allow juvenile <em>A. maculatum </em>to maintain survival likelihoods, whereas metabolic depression may cause juvenile <em>A. opacum </em>to experience increased survivorship. These findings underscore that comparable physiological responses among ecologically similar, sympatric species cannot be assumed. Further, results of this study suggest that metabolic responses may play an important role in amphibian species persistence as temperatures increase due to habitat modification and climate change.</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.