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.
12,346
datasets available to search
ShareScore release 0.9.0
Dataset results
12,346 results for “derivatives”
Average yearly sea ice coverage for various PAL LTER sub-regions West of the Antarctic Peninsula derived from passive microwave satellite data, 1979 - 2023.
Annual sea ice coverage derived from passive microwave satellite measurements and extracted for various PAL LTER subregions. Several different sea-ice metrics are provided including: monthly sea-ice extent, sea-ice area, and open-water area (km^2) extracted for the greater WAP region (from the AP to 80W) and for 3 PAL LTER grid regions: the original PAL grid (000-900 lines), the PAL 'DSR' grid (200-600 lines), and the PAL 'new' grid (-200 to 600 lines).
Depth of the sea floor along the 5-minute resolution cruise track of the Antarctic Circumnavigation Expedition (ACE) derived from GEBCO 2019 bathymetry data.
<p><strong>Dataset abstract</strong></p> <p>Depth of the seabed along the five-minute averaged cruise track (Landwehr et al., 2020; DOI: 10.5281/zenodo.3752691) of the Antarctic Circumnavigation Expedition (ACE) was calculated from the the General Bathymetric Chart of the Oceans (GEBCO; GEBCO Compilation Group, 2019) 2019 gridded 30-arc second bathymetry data. The nearest gridded value from the bathymetry dataset was used to find the depth at the averaged position.</p> <p>Provided within this dataset is the average position of the vessel during a five-minute time period (where the time given is the middle time of the averaging interval).</p> <p><strong>Dataset contents</strong></p> <ul> <li>cruise_track_gebco2019_depth_5min.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> <li>ace_cruise_track_gebco2019_depth_5min_change_log.txt, metadata, text</li> </ul> <p><strong>Change log</strong></p> <p><strong>v1.1</strong> - Added additional data coverage and therefore track coverage from 2016-11-17 - 2016-11-22 inclusive. Updated README.txt with information about data coverage. Added change_log file.</p> <p><strong>v1.0</strong> - Initial release of depth along cruise track data set.</p> <p><strong>Dataset license</strong></p> <p>This GEBCO sea floor depth along the cruise track dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Derived Data supporting "On the Seasonal Cycles of Tropical Cyclone Potential Intensity" (Gilford et al. 2017, JoC)
<p>Derived monthly mean tropical cyclone potential intensities (and associated variables) using the Bister and Emanuel 2002 PI algorithm, ftp://texmex.mit.edu/pub/emanuel/TCMAX; from MERRA2 (averaged over 1980-2016) and ERA-I data (averaged over 1980-2013), on 2.5x2.5 degree grids and with the ERA-I land-sea mask already applied. This data supported the publication of Gilford et al. (2017, JoC). When using this data, please include the citation:</p> <p>Daniel M. Gilford, Susan Solomon, and Kerry Emanuel, 2017: On the Seasonal Cycles of Tropical Cyclone Potential Intensity. <em>J. Climate, </em><strong>30</strong>, 6085–6096. doi: <a href="http://journals.ametsoc.org/doi/10.1175/JCLI-D-16-0827.1">10.1175/JCLI-D-16-0827.1</a>.</p> <p> </p>
Historical Tropical Cyclone Along-track Potential Intensity (and Derived Quantities) for Six Ocean Basins from Reanalyses
<p>Supporting derived data for Shields et al. (2020, GRL).</p> <p>Derived tropical cyclone potential intensities and associated variables across the North Atlantic (NA), Eastern North Pacific (EP), North Indian (NI), South Indian (SI), South Pacific (SP), and Western North Pacific (WP) ocean basins, from MERRA2, ERA-I, and MERRA2 with SSTs replaced by HadISSTs. NA/WP basins also have potential and observed intensities calculated with NCEP/NCAR and ERA-20C reanalyses over 1950-2016 and 1950-2010, respectively.</p> <p>All files are netcdf format, organized by basin, with suffixes on data variables to indicate reanalysis:</p> <ul> <li>"_m": MERRA2 (Gelaro et al. 2017)</li> <li>"_h": MERRA2-HadISSTs (Rayner et al. 2003)</li> <li>"_e": ERA-I (Dee et al. 2011)</li> <li>"_n": NCEP/NCAR (Kalnay et al. 2016)</li> <li>"_c": ERA-20C (Stickler et al. 2014)</li> </ul> <p>When using this data, please include the citation:</p> <blockquote> <p><strong>Shannon Shields, Allison Wing, and Daniel M. Gilford, 2020: A Global Analysis of Interannual Variability of Potential and Actual Tropical Cyclone Intensities. Geophys. Res. Lett.</strong></p> </blockquote> <p>Potential intensities calculated with the Bister and Emanuel (2002) algorithm (<strong>pcmin.m</strong>) by Kerry Emanuel (revised by Daniel Gilford, Gilford et al. 2019), available freely at: ftp://texmex.mit.edu/pub/emanuel/TCMAX</p> <p>MERRA2, ERA-I, and MERRA2 with SSTs replaced by HadISSTs calculations were performed by Daniel Gilford; NCEP/NCAR and ERA-20C calculations were performed by Dr. Suzana Camargo (many thanks!).</p> <p>Please direct any questions or comments to daniel[dot]gilford[at]rutgers[dot]edu.</p>
MAR2PROTECT - Coconut shell derived activated carbon for effective separation of greenhouse gases - DATASET
<p>The need for innovative and efficient adsorptive materials with enhanced structural characteristics that facilitate the selective capture of greenhouse gases (GHGs) is critical. Porosity and surface area play an important role in the adsorptive capture and separation of GHGs, enabling the design of processes that reduce GHGs emissions. This study shows how residual coconut shell (CS) biomass can be reused for the design of novel biomaterials (CS-CO<sub>2</sub>, CS-ZnCl<sub>2</sub>) with structural characteristics that promote the selective adsorption of GHGs. Additionally, the results are compared with those obtained with activated carbon monoliths (ACM) and a Metal-Organic Framework (MOF Fe-BTC) to understand the impact of different porous solid matrices on adsorptive GHG capture. In this context, the adsorption performance of difluoromethane (R-32), pentafluoroethane (R-125), 1,1,1,1-tetrafluoroethane (R-134a), 1,1,1,1-trifluoroethane (R-143a), carbon dioxide (CO<sub>2</sub>), and methane (CH<sub>4</sub>) on CS-CO<sub>2</sub>, CS-ZnCl<sub>2</sub>, ACM and Fe-BTC were measured by gravimetry at 283.15 K, 303.15 K and 323.15 K. The experimental data are correlated using the dual-site Langmuir adsorption model, and the selectivities of the commercial mixtures R-410A, R-407C, R-404A and CO<sub>2</sub>/CH<sub>4</sub> are calculated using the Ideal Adsorption Solution theory (IAST). CS-ZnCl<sub>2</sub> has a higher selectivity for R-125 over R-32 in the separation of R-410A at low pressure, and also a higher selectivity for R-407C due to its larger pore volume. In the separation of the R-404A refrigerant blend, CS-CO<sub>2</sub> adsorbs predominantly R-134a and R-143a over R-125. Finally, the ACM material preferentially adsorbs CO<sub>2</sub> over CH<sub>4</sub>, owing to its large and elongated micropores that favour the adsorption of the smaller molecule. This study introduces novel and innovative materials to enhance the separation of GHGs mixtures, contributing to a reduction in their emissions.</p>
Characterization of a loss-offunction NSF attachment protein beta mutation in monozygotic triplets affected with epilepsy and autism using cortical neurons from proband-derived and CRISPR-corrected induced pluripotent stem cell lines
<p>RNA-seq data of matured cortical neurons (8-weeks old) derived from the induced pluripoent stem cells (iPSC) of control parents (CtrlF and CtrlM) and corrected proband. There are three replicates (Rep1, Rep2, Rep3) for each sample with Forwad read (R1_001.fastq.gz)</p> <p>CtrlF: Control Father sample</p> <p>CtrlM: Control mother sample</p> <p>NDD_01_Corr_Het: Heterozygous correction of NAPB mutation (c.354+2T>G) in NDD_01 proband</p> <p>NDD_05_Corr_Hom: Homozygous correction of NAPB mutation (c.354+2T>G) in NDD_05 proband</p>
Building footprints Oldenburg derived from aerial imagery
<p>This data set contains about 78000 georeferenced polygons representing all building footprints within the administrative boundaries of the city of Oldenburg, Lower Saxony, Germany. These geometries were created by a deep learning-based image segmentation. The model for this was trained at the State Office of Lower Saxony for Geoinformation and Surveying (LGLN).</p> <p>We publish this data under the CC0 license. <br>You can do whatever you want with it. There are no restrictions.<br><br>If you do something great with the data set, we'd love to hear about it: <a href="mailto:ki-gebaeudeerkennung@geolabs.atlassian.net">ki-gebaeudeerkennung@geolabs.atlassian.net</a><br>If you use this dataset, you are welcome to reference it - but you don't have to.<br>Would you like builiding footprints for another area? We'd love to hear about it.</p>
3D Data Derivatives of Grotta di Fumane: GigaMesh-processed, Annotations and Segmentations
<p><strong>Overview:</strong></p> <p>This repository contains derivatives of the Open Access publication by Falcucci & Peresani [FP22]. Our derived dataset (n = 62) is used to demonstrate our segmentation algorithm [BHM23], as shown in [BLM22], [BLM23], [LBM23], and will serve as a benchmark dataset for future analyses. To date, and to the best of our knowledge, our dataset is the first dataset of annotated lithic artifacts. In addition to the annotated dataset, we will also provide the segmented [BLM23] and GigaMesh preprocessed datasets [Mar+10; MK13] (n = 732) in separate folders. </p> <p><strong>Repository description: </strong></p> <p>A detailed description of the data can be found in 3D_Data_Derivatives_of_GdF_overview.pdf.</p> <p>For information on the archaeological interpretation of the artifacts, please refer to the original data publication by Falcucci and Peresani (2022). In our publications, we have expanded the CSV file from Falcucci and Peresani (2022) to document the use of the extended dataset:</p> <ul> <li> <p>Annotated: All artifacts that are annotated are marked with a 1.</p> </li> <li> <p>GT_PLY: All artifacts that are annotated and included in this publication are referenced by their respective file, such as 31_gt_labels.ply.</p> </li> <li> <p>Bullenkamp_et_al_2022: Artifacts utilized in [BLM22] are marked with a 1 .</p> </li> <li> <p>Bullenkamp_et_al_2023: Artifacts utilized in [BLM23] are marked with a 1.</p> </li> <li> <p>Linsel_et_al_2023: Artifacts utilized in [LBM23] are marked with a 1.</p> </li> </ul>
Protosemitic Root Derivations
<p>The dataset provided here culminated in a directed network graph (gexf), and is the result of a guided research seminar on Semitic Philology by Adam Anderson on behalf of Jason Moser, who at the time was a Ph.D. candidate at UC Berkeley in the spring of 2019. The primary focus of the resulting network graph was to show potential pathways for comparative linguistics within the Semitic languages, a branch of the Afro-asiatic language family, based on commonly shared roots and their etymologies. </p> <p>When we concluded this study in 2019, the results were by no means finalized or conclusive, which means that many of the proposed root derivations in the graph are only a working hypothesis, and that there is a higher likelihood for inaccuracies that need to be corrected and supervised by specialists in each of these languages. We move ahead with a novel dataset publication, in order to put the results of this work in Wikidata lexemes, which in turn will allow for the scholarly curation and emendation to take place with attributions.</p> <p>Our use of the term Protosemitic is more in line with Common-Semitic, thereby expressing a working hypothesis for the original Protosemitic roots and lemmas and the most likely related roots in different Semitic languages. Whether a root is inherited from Semitic, borrowed from another language, a Wanderwort or loanword, we don't claim to know. That said, we are fairly certain that the majority of the data shared in this version are based on true roots, which can be substantiated in the list sources. While there is a longstanding series of dictionaries for Akkadian verbal roots (e.g. AHw, CDA, CAD, etc.), the nominal roots have yet to receive the same attention. We therefore consulted a list of sources and datasets, both published and unpublished, including John Huehnergard’s Semitic Philology 140 syllabus from 2008, which we include below. The next version of this dataset will include Q-item ids and references for the etymology claims in Wikidata.</p> <p>In preparation for the publication of this dataset in Wikidata, we worked closely with Timo Homburg, who helped formulate the claims and RDF tripple statements and uploaded the dataset as well. We would like to thank Timo for extending his time and skillsets to help make this data more accessible.</p> <h1>Datasets:</h1> <p><strong>Network graph file:</strong> protosemitic_3996.gexf</p> <p><strong>Semitic Root Derivations:</strong> SemiticRootDerivations.csv</p> <h2>Network Properties for the Nodes:</h2> <p>* Statistics generated in Gephi</p> <ol> <li> <p>Id = Hexadecimal with the first three letters corresponding to the source language (e.g. GEZ000018 is the 18th root in Ge’ez in our dataset)</p> </li> <li> <p>Label</p> </li> <li> <p>Lang_source</p> </li> <li> <p>In-Degree*</p> </li> <li> <p>Out-Degree*</p> </li> <li> <p>Degree*</p> </li> <li> <p>Weighted In-Degree*</p> </li> <li> <p>Weighted Out-Degree*</p> </li> <li> <p>Eccentricity*</p> </li> <li> <p>Closeness Centrality*</p> </li> <li> <p>Harmonic Closeness Centrality*</p> </li> <li> <p>Betweenness Centrality*</p> </li> <li> <p>Bridging Coefficient*</p> </li> <li> <p>Bridging Centrality*</p> </li> <li> <p>Authority*</p> </li> <li> <p>Hub*</p> </li> <li> <p>Modularity Class*</p> </li> <li> <p>PageRank*</p> </li> <li> <p>Component ID*</p> </li> <li> <p>Strongly-Connected ID*</p> </li> </ol> <h2>Network Properties for the Edges:</h2> <ol> <li> <p>Source = source ID</p> </li> <li> <p>Target = target ID</p> </li> <li> <p>Type = directed edges</p> </li> <li> <p>Id = simple integer count</p> </li> <li> <p>Label = blank</p> </li> <li> <p>Interval = blank</p> </li> <li> <p>Weight = 1 (if unmerged), or 2 (if merged)</p> </li> <li> <p>Lang_source = (proto-)Semitic</p> </li> <li> <p>Entry_source = lemma for (proto-)Semitic language (e.g. format: lemma[gloss]POS)</p> </li> <li> <p>Lang_target = Semitic language (e.g. geez = Ge’ez)</p> </li> <li> <p>Entry_target = lexeme for Semitic language (e.g. format: lemma[gloss]POS)</p> </li> </ol> <h2>Semitic Root Derivations:</h2> <ol> <li> <p>lang_from = source language (e.g. (proto-)Semitic)</p> </li> <li> <p>entry_from_id = unique ID (hexadecimal) for source language lemma</p> </li> <li> <p>entry_from = lemma for the source language</p> </li> <li> <p>PS-root = root derived from Protosemitic</p> </li> <li> <p>PS_Lid = Wikidata lexeme ID for the Protosemitic root (when applicable)</p> </li> <li> <p>lang_to = target language (e.g. geez)</p> </li> <li> <p>entry_to_id = unique ID (hexadecimal) for target language lemma</p> </li> <li> <p>entry_to = lemma for the target language</p> </li> <li> <p>Akk_root = most likely Akkadian root</p> </li> <li> <p>Akk_Lid = Wikidata lexeme ID for the Akkadian root (when applicable)</p> </li> </ol> <h1>List of sources consulted:</h1> <ul> <li> <p>Bennett, P. R. Comparative Semitic Linguistics. A Manual, 1998.</p> </li> <li> <p>Bergstrasser, G. Introduction to the Semitic Languages, 2 ed. 1983.</p> </li> <li> <p>Brockelmann, C. Review of Essai comparatif sur le vocabularie et la phonétique du Chamito-Sémitique, by M. Cohen (BiOr 7, 58-61), 1950.</p> </li> <li> <p>Cohen, E. Sentential Complementation in Akkadian (JAOS 122, 803-806), 2000.</p> </li> <li> <p>Cunchillos, J.-L. A Concordance of Ugaritic Words. Versiùn espaûola, 2003.</p> </li> <li> <p>Cunchillos, J.-L. The Texts of the Ugaritic Data Bank, 2003.</p> </li> <li> <p>del Olmo Lete, G. & Sanmartín, J. A Dictionary of the Ugaritic Language (HdO 112), 2015.</p> </li> <li> <p>Demeke, G. Origin of Amharic (Afroasiatic Studies 3), 2014.</p> </li> <li> <p>Deutscher, G. & Kouwenberg, N.J.C. The Akkadian Language in its Semitic Context, (PIHANS 106), 2006.</p> </li> <li> <p>Fox, J. Semitic Noun Patterns (HVSS 52), 2013.</p> </li> <li> <p>Goetze, A. Sequence of Two Short Syllables in Akkadian (OrNS 16, 233-238), 1946.</p> </li> <li> <p>Gordon, C. H. Ugaritic Textbook (OA 38), 1965.</p> </li> <li> <p>Grebaut, Sylvain - Les Pluriels Brises des Formations Trilitteres Qetl, Qatl, Qatal. Cahiers Pour l'Enseignement, 1947.</p> </li> <li> <p>Hospers, J. H. ed. A Basic Bibliography for the Study of the Semitic Languages, Vol. 1, 1973.</p> </li> <li> <p>Huehnergard, J. (North-)West Semitic Dialectology, 2008.</p> </li> <li> <p>Huehnergard, J. & Woods, C. Akkadian and Eblaite (CEWAL 218-280) 2004.</p> </li> <li> <p>Huehnergard, J. Afro-Asiatic (CEWAL 138-159), 2004.</p> </li> <li> <p>Huehnergard, J. Analogical Change Handout, 2008.</p> </li> <li> <p>Huehnergard, J. Semitic Languages (CANE 4, 2117-34), 1995,</p> </li> <li> <p>Huehnergard, J. Semitic Philology 140 (Unpublished Course Outline), 2008.</p> </li> <li> <p>Huehnergard, J. Trees and Waves: On the Classification of the Semitic Languages, 2008 (preprint).</p> </li> <li> <p>Knudsen, E. - Stress in Akkadian (JCS 32, 3-16), 1980.</p> </li> <li> <p>Kogan and Krebernik (eds.). Etymological Dictionary of Akkadian, 2000.</p> </li> <li> <p>Kouwenberg, N.J.C. - Reflections on the Gt-Stem in Akkadian (ZA 95, 77-103) 2005.</p> </li> <li> <p>Krebernik, M. & Streck, M.P. Der Irrealis im Altbabylonischen (JBVO 5, 51-78), 2001.</p> </li> <li> <p>Lambdin, T. O. Introduction to Classical Ethiopic (Ge'ez) (HSS 24), 1978.</p> </li> <li> <p>Leslau's Geez Dictionary also has comparative notes for etymological purposes.</p> </li> <li> <p>Lipinski, E. Glossary of Selected Linguistic Terms (OLA 80, 575-592), 1997.</p> </li> <li> <p>Mankowski, P. V. Akkadian Loanwords in Biblical Hebrew (HVSS 47), 2000.</p> </li> <li> <p>Meyer, W. R. Zum Terminativ-Adverbialis im Akkadischen, die Modaladverbien auf -ish (OrNS 64 3, 161-186), 1995.</p> </li> <li> <p>Militarev, A. & Kogan, L. Semitic Etymological Dictionary, Vol. I: Anatomy of Man and Animals (AOAT 278/1), 2000.</p> </li> <li> <p>Militarev, A. & Kogan, L. Semitic Etymological Dictionary, Vol. II: Animal Names (AOAT 278/2), 2005.</p> </li> <li> <p>Moscati, S. et al. An Introduction to the Comparative Grammar of the Semitic Languages (PLO NS 6), 1964.</p> </li> <li> <p>Pennachietti, F. Un articolo prepositivo in neosudarabico (RSO 44, 285-293), 1969.</p> </li> <li> <p>Schniedewind, W.M. & Hunt, J.H. A Primer on Ugaritic, 2007.</p> </li> <li> <p>Soden, Wolfram von - Untersuchungen zur Babylonische Metrik, Teil 1 (ZA 71, 161-204), 1982; Teil 2 (ZA 74, 213-284), 1984.</p> </li> <li> <p>von Soden, W. Akkadisches Handwörterbuch (AHw). This dictionary has comparative notes, and an English version can be found in the Concise Dictionary of Akkadian (CDA)</p> </li> <li> <p>Weninger, Stefan, ed. - The Semitic Languages, An International Handbook (HSK 36), 2011.</p> </li> <li> <p>Whiting, R. M. The Dual Personal Pronouns in Akkadian (JNES 31, 331-337), 1972.</p> </li> </ul> <p> </p>
Laboratory-measured and X-ray CT-derived volumetric composition of a permafrost core
<p>This dataset contains data on the volumetric composition of a permafrost core which has been drilled in a Yedoma upland in northeast Siberia (72.36613 N, 126.27272 E) in September 2017. This dataset supplements a research article to be submitted to the scientific journal <em>The Cryosphere</em>. It contains the following files:</p> <p><strong><em>volumetric_contents_sampleRes_lab+CT.csv</em> </strong><br> Contains the volumetric contents of total ice, organic, and mineral measured in the laboratory at AWI Potsdam at a coarse resolution. It further contains the volumetric contents of gas, excess ice, and two sediment phases (A,B) derived from a CT scan at UFZ Halle, downsampled to the resolution of the laboratory samples.</p> <p><em><strong>volumetric_contents_highRes_CT.csv</strong></em><br> Contains the volumetric contents of gas, excess ice, and two sediment phases (A,B) derived from a CT scan at UFZ Halle at the original resolution of 50µm.</p> <p><em><strong>regression analysis_paper.py</strong></em><br> This pyhton script uses the above listed input files to perform and evaluate a regression analysis<strong><em> </em></strong>of the CT data against the laboratory data. The regression result is the composition of the CT-derived sediment phases (A,B) in terms of pore ice, organic, and mineral. The script furthermore computes evaluation metrics of the lab-CT comparison, and computes volumetric contents of pore ice, total ice, organic, and mineral at the high resolution of the original CT data.</p> <p><em><strong>volumetric_contents_sampleRes_all.csv</strong></em><br> This file can be reproduced by the files listed above and contains, in addition to the data contained in <em>volumetric_contents_sampleRes_lab+CT.csv</em>, the volumetric contents of pore ice, total ice, mineral, and organic as predicted by the regression model at the same (coarse) resolution as the laboratory samples.</p> <p><em><strong>volumetric_contents_highRes_all.csv</strong></em><br> This file can be reproduced by the files listed above and contains, in addition to the data contained in <em>volumetric_contents_highRes_CT.csv</em>, the volumetric contents of pore ice, total ice, mineral, and organic as predicted by the regression model at the same (high) resolution as the original CT data.</p> <p>More details can be found in the article describing the study.</p>
Modern aridity in the Altai-Sayan Mountain Range derived from multiple millennial proxies
<p><em>1500-year stable carbon and oxygen isotopes in larch tree-ring cellulose from the Altai-Sayan Mountain Range region </em>(49-51N, 87-89 E)</p> <p><em>Regional summer (June-July-August) precipitation reconstruction for the Altai-Sayan Mountain Range region based on d<sup>13</sup>C in tree-ring cellulose (d<sup>13</sup>C<sub>cell </sub>) combined with Co/Inc and Rb/Sr from Teletskoe Lake core sediments (TLs).</em></p> <p><em>Regional summer air temperature (June-July-August) reconstruction based on d<sup>18</sup>O<sub> </sub>in tree-ring cellulose (d<sup>18</sup>O<sub>cell</sub>), tree-ring width (TRW), latewood density (MXD) and elemental concentrations (Ca, Ti, Br/Sr) in the Teletskoe Lake core sediments (TLs).</em></p>
Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets
<p><strong>Sydney morphology and land surface dataset</strong></p> <p>This dataset for Sydney, Australia, represents land cover, building morphology, vegetation morphology and other parameters appropriate for input into local or mesoscale urban climate models.</p> <p>The dataset is provided in netCDF4 and GeoTiff formats.</p> <p>Associated manuscript:</p> <blockquote> <p><a href="https://doi.org/10.3389/fenvs.2022.866398">A transformation in city-descriptive input data for urban climate models</a></p> </blockquote> <p>Citation for the open dataset:<br> - Lipson, M., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets (v1.01), <a href="https://doi.org/10.5281/zenodo.6579061">https://doi.org/10.5281/zenodo.6579061</a>, 2022.</p> <p>Citation for the associated manuscript:<br> - Lipson, M. J., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: A Transformation in City-Descriptive Input Data for Urban Climate Models, Frontiers in Environmental Science, 10, <a href="https://doi.org/10.3389/fenvs.2022.866398">https://doi.org/10.3389/fenvs.2022.866398</a>, 2022.</p> <p>Location of associated processing code:<br> - <a href="https://github.com/matlipson/geoscape_processing_public.git">https://github.com/matlipson/geoscape_processing_public.git</a></p> <p><strong>Acknowledgments</strong></p> <p>We gratefully acknowledge the Australian Urban Research Infrastructure Network (AURIN) and Geoscape Australia for <br> providing the datasets necessary for this study, drawing on Geoscape Buildings, Surface Cover and Trees datasets, <br> © Geoscape Australia, 2020: https://geoscape.com.au/legal/data-copyright-and-disclaimer/. <br> This research was supported by the Australian Research Council (ARC) Centre of Excellence for Climate System Science <br> (grant CE110001028), the ARC Centre of Excellence for Climate Extremes (grant CE170100023). </p> <p> </p>
Arctic vegetation cover fractions derived from Landsat time series (1984-2020) for the greater Mackenzie Delta Region (Western Canadian Arctic)
<p>Data to the publication by Nill et al. (2022) "<em>Arctic shrub expansion revealed by Landsat-derived multitemporal<br> vegetation cover fractions in the Western Canadian Arctic"</em></p> <p>The dataset features Landsat-derived fractional cover estimates of Arctic plant functional types (shrub, evergreen trees, herbaceous, lichen) and other land cover (barren, water) in the greater Mackenzie Delta Region, Canada.<br> We utilized regression-based unmixing based on synthetic training data in order to build multitemporal Kernel Ridge Regression (KRR) models for estimating fractional cover and validated our predictions based on independent very-high-resolution imagery (please be referred to publication for details).<br> <br> <strong>Dataset information</strong><br> The fraction cover predictions ("krr-avg") are provided separately for each epoch (1984-1990, 1991-1996, ..., 2015-2020) and class/cover type. The decadal change images ("dec-cng") between 1984 and 2020 are provided separately for each class/cover type. The naming convention of the files is as follows:</p> <p>XXXX-XXXX_YYY-YYY_int16-10e3_class-Z-Z</p> <ul> <li>XXXX-XXXX = epoch, e.g. 2015-2020</li> <li>YYY-YYY = dataset ("krr-avg" = fraction cover, "dec-cng" = decadal fraction cover change)</li> <li>Z-Z = class ID and associated class name (sh = shrub, cf = coniferous, hb = herbaceous, lc = lichen, wt = water, br = barren)</li> </ul> <p>The fraction cover values are % scaled by 10,000. For instance, a value of 1234 refers to 12.34%. Further image metadata:</p> <ul> <li><strong>Datatype:</strong> Signed 16-bit integer (Int16) </li> <li><strong>Data format: </strong>GeoTiff (.tif)</li> <li><strong>No data value:</strong> -9999</li> <li><strong>Projection:</strong> EPSG:3573 with custom central meridian; WKT string: 'PROJCS["WGS 84 / North Pole LAEA Canada",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Lambert_Azimuthal_Equal_Area"],PARAMETER["latitude_of_center",90],PARAMETER["longitude_of_center",-135],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1],AXIS["Easting",EAST],AXIS["Northing",NORTH]]'</li> </ul> <p><strong>Publication</strong><br> Nill, L., Grünberg, I., Ullmann, T., Gessner, M., Boike, J. & Hostert, P. (2022): Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic. Remote Sensing of Environment, 2022, 281. https://doi.org/10.1016/j.rse.2022.113228</p> <p><strong>Further information</strong><br> For further information, please see the publication or contact Leon Nill (leon.nill@geo.hu-berlin.de).<br> A web-visualization of this dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/arctic-shrub/">here</a>.</p>
500-meter grid of Derived Soil Profiles (DSP) for Italy - SuoliCella500
<p>National database of Italian Soil Typological Units (STU) and corresponding Derived Soil Profiles (DSP) obtained on a 500 meters grid (1,109,672 points) by neural network. The most probable WRB Reference Soil Group (RSG), WRB Qualifiers, and USDA textural soil types were mapped on the 500 meters grid, by neural network. 18,707 Observed soil profiles and the respective 33,014 Soil Horizons were grouped into 4,472 STUs based on the combinations of Soil Region, WRB Reference Soil Group (RSG), WRB Qualifiers, and USDA textural soil types obtained on the 500 meters grid. Statistics were calculated (Mean Value, Standard Deviation Value, and Numerosity) for soil rooting depth and for the most common analytical parameters of the soil horizons (Coarse fragment content fraction; pH in water; Carbon (C) - organic; Carbonate (CO3--) - Total; Clay, Sand, and Silt fraction; Granulometry; Textural soil types). The 500 meters grid adopts EPSG 23032 (ED50 UTM-32). A reference scale of 1:250.000 may be attributed to the 500-meters grid map, on the base of the numerosity of DSP produced for the whole italian territory.</p>
A Map of Land Use and Land Cover in Southern Malawi Derived from Sentinel-2 Data (2023)
<h3><strong>Overview</strong></h3> <p>The land use and land cover map comprises the Mulanje and Phalombe districts, in Southern Malawi. It includes five classes: forest, natural vegetation, cropland, wetland, and other lands. The map is derived from Sentinel-2 mosaics, resulting in a spatial resolution of 10 meters, for 2023. </p> <p> </p> <h3><strong>Map Accuracy</strong></h3> <p>The land use and land cover map achieves an overall accuracy of 89%. Details of user and producer accuracies are provided in Table 1.</p> <p>Table 1: Land use and land cover classification validation,including overall, producer (PA) and user (UA) accuracies values for each class.</p> <div> <table> <tbody> <tr> <td> <p><strong>Class </strong></p> </td> <td> <p><strong>Producer Accuracy</strong></p> </td> <td> <p><strong>User Accuracy</strong></p> </td> </tr> <tr> <td> <p>Cropland</p> </td> <td> <p> 93%</p> </td> <td> <p> 85%</p> </td> </tr> <tr> <td> <p>Wetland</p> </td> <td> <p> 100%</p> </td> <td> <p> 100%</p> </td> </tr> <tr> <td> <p>Other Lands</p> </td> <td> <p> 90%</p> </td> <td> <p> 95%</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p> 79%</p> </td> <td> <p> 90%</p> </td> </tr> <tr> <td> <p>Natural Vegetation</p> </td> <td> <p> 90%</p> </td> <td> <p> 90%</p> </td> </tr> <tr> <td> <p><strong>Overall Accuracy</strong></p> </td> <td><br> <p><strong> 89%</strong></p> </td> </tr> </tbody> </table> </div> <h3> </h3> <h3><strong>Files descripion</strong></h3> <ul> <li>MLW_Sentinel_LULC_2023.tif / .qml: land use and land cover map and QGIS style file</li> <li>training_samples.gpkg: training samples with class labels</li> <li>validation_samples.gpkg: validation samples with class labels</li> </ul>
Mass spectrometry raw data for "Proteomics reveals substantial differences between in vitro matured abattoir-derived and in vivo matured oocytes in cattle"
<p><em><span>In vitro</span></em><span> production (IVP) of bovine embryos still has its limitations such as low blastocyst rate and lower embryo quality, resulting in lower pregnancy rates following the transfer of IVP embryos compared to <em>in vivo</em> produced embryos. </span><span>Given these differences in developmental competence, RNA sequencing and microarray technology have been applied to describe the differences in transcriptional activity between <em>in vitro</em> and <em>in vivo</em> produced embryos. All but one of these studies solely utilized oocytes obtained from slaughterhouse material for the <em>in vitro</em> production of embryos, thereby introducing the possibility, that differences between IVP and <em>in vivo</em> embryos are in part attributable to differing sources of oocytes. The aim of the present study was therefore to compare the proteome of oocytes retrieved from slaughterhouse material, with and without a period of <em>in vitro</em> maturation and <em>in vivo</em> matured oocytes obtained from donor cattle following superovulation. <span>For each group the protein pattern of four biological replicates containing ten oocytes each were analyzed via SWATH<sup>TM</sup>-MS.</span></span></p>
Global DEM derivatives at 250 m, 1 km and 2 km based on the MERIT DEM
<p>Layers include: various DEM derivatives computed using SAGA GIS at 250 m and using MERIT DEM (Yamazaki et al., 2017) as input. Antartica is not included. MERIT DEM was first reprojected to 6 global tiles based on the Equi7 grid system (Bauer-Marschallinger et al. 2014) and then these were used to derive all DEM derivatives. To access original DEM tiles please refer to MERIT DEM <a href="http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/">download page</a>.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>dtm = theme: digital terrain models,</li> <li>twi = variable: SAGA GIS Topographic Wetness Index,</li> <li>merit.dem = determination method: MERIT DEM,</li> <li>m = mean value,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2017 = time reference: year 2017,</li> <li>v1.0 = version number: 1.0,</li> </ul>
RNA datasets to derive predictors for immune checkpoint inhibitor therapy of non-small cell lung cancer
<p>Nanostring nCounter datasets and corresponding clinical data of tumor samples of patients with advanced NSCLC who received anti-PD-1 immuntherapy. Prospectively divided into a discovery and a validation cohort.</p> <p>Please cite the corresponding publication in Annals of Oncology (10.1093/annonc/mdz049)</p>
Cup-marked stone, Zermatt-Hubelwäng, Switzerland - imagery and photogrammetrically derived 2.5D data, 3D data and orthophoto of stone slab no. 3920-01
<p>Imagery and derived 2.5D data, 3D data and orthophoto of cup-marked stone slab No. 3920-01 (http://www.ssdi.ch/), Zermatt-Hubelwäng, Switzerland.</p> <p>Supplemental data for: J. Reinhard, Was in den Rucksack passt… In: Chr. Rinne et al. (ed.), Vom Bodenfund zum Buch - Archäologie durch die Zeiten. Festschrift für Andreas Heege. Historische Archäologie Sonderband 1 (Bonn 2017), 503-520. URL: <a href="http://www.histarch.uni-kiel.de/sonderband01.htm">http://www.histarch.uni-kiel.de/sonderband01.htm</a>, DOI:<a href="https://doi.org/10.18440/ha.2017.101"> https://doi.org/10.18440/ha.2017.101</a> (original paper and additional poster contained in the upload). See <a href="http://skfb.ly/6sxJT">https://skfb.ly/6sxJT</a> for an online visualization of the data on Sketchfab.</p> <p> </p> <p>Contents:</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_503.pdf?versionId=2c4ebd68-da57-42da-b49a-b95d10a9f4f8">HASB2017_130_503.pdf</a>: PDF of Reinhard 2017 (cited above).</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_sup1.zip?versionId=87498b06-3e60-4dc1-a182-0157df73ad80">HASB2017_130_sup1.zip</a>: dense point cloud (full resolution, .ply)</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_sup2.zip?versionId=951d3131-b09b-4efb-b768-adbd29e55e91">HASB2017_130_sup2.zip</a>: orthophoto (5 mm resolution, GeoTIFF)</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_sup3.zip?versionId=8f5ac851-e596-4a31-b724-5f056a4940eb">HASB2017_130_sup3.zip</a>: DEM (1 mm resolution, GeoTIFF)</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_sup4.zip?versionId=29043ecd-1bea-4fbd-8264-38e99c583691">HASB2017_130_sup4.zip</a>: orthophoto (1 mm resolution, GeoTIFF)</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_sup5.zip?versionId=ae05c230-c25a-4a29-801f-202823648ade">HASB2017_130_sup5.zip</a>: 3D model (full resolution, .obj/.mtl/.jpg)</p> <p><a href="https://zenodo.org/record/3373713/files/Image-based_modeling_report.pdf?download=1">Image-based_modeling_report.pdf</a>: Image-based modeling report generated by Agisoft PhotoScan</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/In_Rock_We_Trust_Poster_EAA_Bern_2019-09-07.pdf?versionId=7c003048-b261-45a6-803f-6affc3c48721">In_Rock_We_Trust_Poster_EAA_Bern_2019-09-07.pdf</a>: poster presented at the EAA annual conference 2019 in Bern</p> <p><a href="https://zenodo.org/record/3373713/files/Notes_on_image-based_modeling.pdf?download=1">Notes_on_image-based_modeling.pdf</a>: Notes on the image-based modeling process including scaling information</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/Photos.zip?versionId=833aaf74-8c0e-48a5-8459-28d47af02d2e">Photos.zip</a>: complete set of images used in this project, taken in april 2016</p> <p> </p>
ERA5-derived daily temperature summary 1980-2018
<p>Hourly air temperature at surface data from the ERA5 reanalysis at 0.5˚grid resolution was summarised to produce daily mean, minimum, and maximum temperatures.</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.