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3,172 results for “Publications”
CLAMATO2017: IGM Lyman-alpha Forest Tomography Survey Public Data Release of Spectra and Maps
<p><strong>CLAMATO 2017 Data Release 1 </strong></p> <p>Public release: 2017 October 9</p> <p>Uploaded to Zenodo on 2018 June 19th after acceptance for publication in ApJS</p> <p>By Khee-Gan Lee (kglee@lbl.gov) and collaborators</p> <p>Supporting paper: https://arxiv.org/abs/1710.02894</p> <p>These are data products associated with the first data release of the COSMOS Lyman-Alpha Mapping And Tomography Observations (CLAMATO) survey with the Keck-I telescope, which mapped 3D Lyman-alpha forest absorption at 2.05<z<2.55 within the COSMOS field.</p> <p>The following is the summary of the main products:<br> - Source catalog (CL2017_VALUEADDED_RELEASE_20171009.TXT)<br> - Reduced spectra, in /spec_v0/ (blue) and /spec_v0_red (red) sub-directories<br> - Continuum-fitted 2.05<z<2.55 Lyman-alpha forest pixel data (pixel_data.bin)<br> - Wiener-reconstructed 3D absorption map (map_2017_v3.bin)</p> <p>Versions:<br> v0 (not public): Initial rough extraction for 2.15<z<2.55 <br> v1 (not public): Extended redshift range to 2.05<z<2.55 <br> v2 (not public): Caught bug that caused wrong [RA,Dec] for ~4-5 objects<br> v3 (released 2017 Oct 9): Fixed bug that caused wrong aspect ratio in output map<br> v4 (released 2018 Mar 29): Fixed bug that caused negative continua in some spectra</p> <p><br> <strong>Redshift Catalog and Spectra</strong> </p> <p>We provide our redshift catalog and reduced spectra obtained with Keck-I/LRIS</p> <p>The source catalog is provided in the ASCII file CL2017_VALUEADDED_RELEASE_20171009.TXT, with the following columns:</p> <p>- BLUE_SPEC: Blue spectrum filename (in /spec_v0/ sub-directory)<br> - TOMO_ID: CLAMATO ID number<br> - GMAG: g-magnitude (AB) per Capak et al 2007 photometric catalog<br> - CONF: Redshift confidence grade: see https://arxiv.org/abs/1710.02894<br> - ZSPEC: Spectroscopic redshift as determined from CLAMATO spectrum<br> - QSO: QSO flag (1 if QSO, 0 if non-QSO)<br> - RA: R.A. in degrees (J2000)<br> - DEC: Dec in degrees (J2000)<br> - S/N_1: Estimated Lya-forest S/N at 2.05<z<2.15, -9.0 denotes no estimate<br> - S/N_2: Estimated Lya-forest S/N at 2.15<z<2.35, -9.0 denotes no estimate<br> - S/N_2: Estimated Lya-forest S/N at 2.35<z<2.55, -9.0 denotes no estimate<br> - S/N_RED: Estimated S/N over restframe 1250 ang < lambda < 1350 ang, -9.0 denotes no estimate<br> - TOMOFLAG: Flag on whether sightline was used in tomographic map (0 for no, 1 for yes)<br> - EXPTIME: Exposure time on the spectrum, in seconds (aggregate)<br> - RED_SPEC: Red spectrum filename (in /spec_v0_red/ sub-directory), 'NA' if doesn't exist</p> <p>The tarballs spec_v0.tar.gz and spec_v0_red.tar.gz include all the reduced spectra from LRIS-Blue and LRIS-Red, respectively.</p> <p>The individual LRIS spectra are provided in FITS format, with the following HDU Extensions:<br> - HDU0: Object spectral flux density, in units of 10^{-17} ergs/s/cm^2/angstrom<br> - HDU1: Noise standard deviation<br> - HDU2: Pixel Wavelengths in angstroms</p> <p><strong>Pixel Data </strong></p> <p>The binary file PIXEL_DATA_v4.BIN stores the concatenated Lyman-alpha forest pixels at 2.05<z<2.55 that have been extracted from the 1D spectra and continuum-fitted. </p> <p>The first value in the binary is a 32-bit integer specifying the number of pixels (64332), followed by 5 double-precision floating point (64-bit) vectors storing the x, y, z, sigma_f, and delta_f of the pixels.</p> <p>An example python script to read pixel_data is as follows:<br> import numpy as np<br> with open('CLAMATO2017_public/pixel_data_v4.bin','r') as f:<br> npix = np.fromfile(f, dtype=np.int32, count=1)<br> f.seek(4)<br> pixel_data = np.fromfile(f,dtype=np.float64).reshape((npix,5))</p> <p>LIST_TOMO_INPUT_2017.TXT is a summary file of corresponding to PIXEL_DATA.BIN, listing the [x,y,z] position of the sightlines that contributed to the file as well as, in the final two columns, the index range that can be used to grab the relevant pixels from the concatenated pixel list.</p> <p><strong>Tomographic Map</strong></p> <p>The Wiener-reconstructed map of the 2.05<z<2.55 IGM within the CLAMATO field is the result of applying the dachshund algorithm (http://github.com/caseywstark/dachshund) to PIXEL_DATA.BIN, with the configuration file INPUT.CFG . (Caveat: the version of PIXEL_DATA.BIN here is not actually the right version to directly input into the dachshund code: the first integer in this file should not be present for input to dachshund). </p> <p>The reconstructed map is MAP_2017_V4.BIN, which is a 60x48x876 = 2552880 pixel double-precision binary with. The dimension that changes fastest is the z-dimension (876 pixels per dimension), followed by the y-dimension (48 pixels per dimension) and x-dimension (60 per dimension).</p> <p>Each map pixel represents a 0.5Mpc/h comoving voxel of the Ly-alpha forest absorption. See the Appendix of https://arxiv.org/abs/1710.02894 for the conversion factors to assume to switch between pixel/voxel and [RA, Dec, redshift].</p> <p>The file MAP_2017_V4_SM2.0.BIN is the same map, but smoothed with a R=2Mpc/h Gaussian kernel.</p>
R-code for publication: Ensembles of Ensembles: Combining the Predictions from Multiple Machine Learning Methods
<p>This is the R-code as well as the underlying data needed to reproduce the results of the springer book chapter: "Ensembles of Ensembles: Combining the Predictions from Multiple Machine Learning Methods"</p> <p>For more information contact: dlieske@mta.ca</p> <p> </p>
Data associated with the publication "The origin of the world's smallest flightless bird, the Inaccessible Island Rail Atlantisia rogersi (Aves: Rallidae)"
<p><strong>DESCRIPTION OF FILES</strong><br> These are files including data and additional results, that support the paper "The origin of the world's smallest flightless bird, the Inaccessible Island Rail Atlantisia rogersi (Aves: Rallidae)", by Stervander et al. 2018, published in Molecular Phylogenetics and Evolution (doi: 10.1016/j.ympev.2018.10.007).</p> <p>The phylogenetic analyses focus on rails (Aves: Rallidae) and outgroups based on (1) a dataset, 'MtProt' comprising the coding sequences (cds) from full mitochondrial genome assemblyes, and (2) a mixed-marker dataset, '2Nc3Mt', comprising the mitochondrial markers cytochrome <em>b</em> (cyt<em>b</em>), cytochrome oxidase subunit I (COI), and 16S ribosomal RNA (16S), and the nuclear markers β-fibrinogen intron 7 (bFib7) and recombination activating gene 1 (RAG1). The latter dataset i largely based on data from Garcia-R et al. (2014), with additions of the Inaccessible Island Rail <em>Atlantisia rogersi</em> and some further sequences (see our paper).</p> <p>Trees mentioned in our paper as "results not shown" can be found below.</p> <p><br> <strong>This deposition contains five groups of data:</strong><br> 1. Beast input xml files for phylogenetic analyses<br> 2. Beast output: log files<br> 3. Beast output: raw tree files<br> 4. Beast output: Maximum Clade Credibility trees<br> 5. Tree figures (pdf format)</p> <p><strong>The above are available for the following analyses:</strong><br> A. Mixed-marker dataset ‘2Nc3Mt’, one tree <br> B. Mixed-marker dataset ‘2Nc3Mt’, one tree; Micropygia schomburgkii excluded<br> C. Mixed-marker dataset ‘2Nc3Mt’, separate mitochondrial (‘3Mt’) and nuclear marker trees (RAG1 and bFib7)<br> D. Protein coding dataset ‘MtProt’ from entire mitochondrial genomes</p> <p>The files are thus the following, sorted according to dataset:<br> A1 Beast_input_2Nc3Mt_1tree.xml<br> A2 Beast_output_2Nc3Mt_1tree.log<br> A3 Beast_output_2Nc3Mt_1tree.raw.trees<br> A4 Beast_output_2Nc3Mt_1tree.max_clade_cred_burnin10M.trees<br> A5 Tree_2Nc3Mt_1tree.max_clade_cred_burnin10M.pdf<br> B1 Beast_input_2Nc3Mt_exclMicropygia_1tree.xml<br> B2 Beast_output_2Nc3Mt_exclMicropygia_1tree.log<br> B3 Beast_output_2Nc3Mt_exclMicropygia_1tree.raw.trees<br> B4 Beast_output_2Nc3Mt_exclMicropygia_1tree.max_clade_cred_burnin10M.trees<br> B5 Tree_2Nc3Mt_exclMicropygia_1tree.max_clade_cred_burnin10M.pdf<br> C1 Beast_input_2Nc3Mt_separate_trees.xml<br> C2 Beast_output_2Nc3Mt_separate_trees.log<br> C3 Beast_output_2Nc3Mt_RAG1.raw.trees<br> C3 Beast_output_2Nc3Mt_bFib7.raw.trees<br> C3 Beast_output_2Nc3Mt_mt.raw.trees<br> C4 Beast_output_2Nc3Mt_RAG1.max_clade_cred_burnin10M.trees<br> C4 Beast_output_2Nc3Mt_bFib7.max_clade_cred_burnin10M.trees<br> C4 Beast_output_2Nc3Mt_mt.max_clade_cred_burnin10M.trees<br> C5 Tree_2Nc3Mt_RAG1.max_clade_cred_burnin10M.trees.pdf<br> C5 Tree_2Nc3Mt_bFib7.max_clade_cred_burnin10M.trees.pdf<br> C5 Tree_2Nc3Mt_mt.max_clade_cred_burnin10M.trees.pdf<br> D1 Beast_input_MtProt_1tree.xml<br> D2 Beast_output_MtProt_1tree.log<br> D3 Beast_output_MtProt_1tree.raw.trees<br> D4 Beast_output_MtProt_1tree.max_clade_cred_burnin1M.trees<br> D5 Tree_MtProt_1tree.max_clade_cred_burnin1M.pdf</p> <p>Or, sorted according to file type:<br> 1A Beast_input_2Nc3Mt_1tree.xml<br> 1B Beast_input_2Nc3Mt_exclMicropygia_1tree.xml<br> 1C Beast_input_2Nc3Mt_separate_trees.xml<br> 1D Beast_input_MtProt_1tree.xml<br> 2A Beast_output_2Nc3Mt_1tree.log<br> 2B Beast_output_2Nc3Mt_exclMicropygia_1tree.log<br> 2C Beast_output_2Nc3Mt_separate_trees.log<br> 2D Beast_output_MtProt_1tree.log<br> 3A Beast_output_2Nc3Mt_1tree.raw.trees<br> 3B Beast_output_2Nc3Mt_exclMicropygia_1tree.raw.trees<br> 3C Beast_output_2Nc3Mt_RAG1.raw.trees<br> 3C Beast_output_2Nc3Mt_bFib7.raw.trees<br> 3C Beast_output_2Nc3Mt_mt.raw.trees<br> 3D Beast_output_MtProt_1tree.raw.trees<br> 4A Beast_output_2Nc3Mt_1tree.max_clade_cred_burnin10M.trees<br> 4B Beast_output_2Nc3Mt_exclMicropygia_1tree.max_clade_cred_burnin10M.trees<br> 4C Beast_output_2Nc3Mt_RAG1.max_clade_cred_burnin10M.trees<br> 4C Beast_output_2Nc3Mt_bFib7.max_clade_cred_burnin10M.trees<br> 4C Beast_output_2Nc3Mt_mt.max_clade_cred_burnin10M.trees<br> 4D Beast_output_MtProt_1tree.max_clade_cred_burnin1M.trees<br> 5A Tree_2Nc3Mt_1tree.max_clade_cred_burnin10M.pdf<br> 5B Tree_2Nc3Mt_exclMicropygia_1tree.max_clade_cred_burnin10M.pdf<br> 5C Tree_2Nc3Mt_RAG1.max_clade_cred_burnin10M.trees.pdf<br> 5C Tree_2Nc3Mt_bFib7.max_clade_cred_burnin10M.trees.pdf<br> 5C Tree_2Nc3Mt_mt.max_clade_cred_burnin10M.trees.pdf<br> 5D Tree_MtProt_1tree.max_clade_cred_burnin1M.pdf</p> <p><strong>Note about the tree figures (pdf format): </strong>Nodes marked with a black circle are supported by a posterior probability (PP) of 1.0, for lower PP the number is given at the node. Blue bars represent the 95% highest posterior density intervals of the node age. MYA = Million years ago.</p> <p>/Martin Stervander (martin@stervander.com)</p>
Dataset for publication: Compound parabolic collector solar disinfection system for the treatment of harvested rainwater, Strauss et al. (2018). DOI:10.1039/c8ew00152a.
<p>Datasets used for the publication: Strauss A, Reyneke B, Waso M and Khan W (2018) Compound parabolic collector solar disinfection system for the treatment of harvested rainwater. Environ Sci: Water Res. Technol. DOI: 10.1039/c8ew00152a. Please cite the article when using the datasets.</p> <p>Available datasets:</p> <ul> <li>WATERSPOUTT_688928_US_Environmental Conditions_01_1.0.0: Dataset describing the environmental conditions on sampling days while assessing a SODIS-CPC reactor for the treatment of roof-harvested rainwater.</li> <li>WATERSPOUTT_688928_US_SODIS-CPC Schematics_01_1.0.0: Schematic diagrams showing the design of the SODIS-CPC reactor.</li> <li>WATERSPOUTT_688928_US_SODIS-CPC-Microbiology_01_1.0.0: Dataset describing the results obtained while monitoring the microbiological quality of the roof-harvested rainwater before and after treatment with the SODIS-CPC reactor.</li> <li>WATERSPOUTT_688928_US_SODIS-CPC-Physicochemical_01_1.0.0: Dataset describing the physicochemical quality of the roof-harvested rainwater before and after treatment with the SODIS-CPC reactor.</li> <li>WATERSPOUTT_688928_US_UV-transmittance_01_1.0.0: Dataset describing the UV transmittance of polymethyl methacrylate and borosilicate glass.</li> </ul>
Data of publication Controlled size reduction of rare earth doped nanoparticles for optical quantum technologies
<p>Data corresponding to the figures of the publication " Controlled size reduction of rare earth doped nanoparticles for optical quantum technologies" by S. Liu et al. (https://pubs.rsc.org/en/content/articlelanding/2018/ra/c8ra07246a#!divAbstract). A text file describes data in each compressed folder, please refer to the publication for more details. </p>
Group size effects and critical mass in public goods games
<pre>This dataset accompanies the paper "Group size effects and critical mass in public goods games", https://doi.org/10.1038/s41598-019-41988-3 It records participant decisions in a set of binary one-shot Public Goods Games with curvilinear payoff function (see details in the paper). The explanation of the data fields is present also in the metadata of the file: # Cooperation: 0=defection, 1=cooperation, 99=the participant did not make the decision # Group: interacting group size (N) # Treatment: Critical Mass Nc # dropout: 1=the participant did not make the decision, 0=the participant made the decision # incompleted: 1=participant did not make all the decisions of the experiment; 0=participant did made all the decisions of the experiment # female: 1=female, 0=male </pre> <p> </p>
Large scale and information effects on cooperation in public good games
<pre>This dataset accompanies the paper "Large scale and information effects on cooperation in public good games", https://doi.org/10.1038/s41598-019-50964-w It records participant decisions in a set of Public Goods Games (see details in the paper). Explanation of the data fields: #participant_id: identification number for each participant in each treatment #player_alive: (for each round) 1=participant is still playing; 0=participant has been banned for forgetting three decisions or did not show up the first day. #group_avg_contribution: Average contribution #player_contribution: participant individual contribution to the PGG #round_number: round number #gender #age #treatment: see the publication for details regarding the different treatments # IBSEN metadata # 2017A2ECOCOOPANSA01ONL0000ESPMAD </pre>
Title, Author, Publisher, Place of Publication, and Language-related Network Graphs of the Berlin State Library Main Catalog
<p>The dataset contains graphs in GML, GraphML, and a simple JSON format.</p> <p>For each of the following languages:</p> <ol> <li>cze</li> <li>dan</li> <li>dut</li> <li>eng</li> <li>fre</li> <li>fry</li> <li>ger</li> <li>gre</li> <li>ice</li> <li>ita</li> <li>lat</li> <li>nor</li> <li>pol</li> <li>por</li> <li>rum</li> <li>rus</li> <li>slo</li> <li>spa</li> <li>swe</li> </ol> <p>two graphs are made available linking</p> <ul> <li>author, publisher, and place of publication</li> <li>author, publisher, place of publication, and title</li> </ul> <p>Additionaly, a third graph links authors and publishers to the language of publication (incl. year of the publication).</p> <p>The core statistics of each graph are outlined in <em>social_analysis_statistics.csv</em>. The smallest graph (fry, author_publisher_location) has 298 nodes and 264 edges, while the largest (ger, author_publisher_location_title) has 2,499,943 nodes and 3,950,900 edges.</p> <p>The language graphs spans all languages and has 1,706,273 nodes and 1,827,759 edges.</p> <p>All graphs have been created by a Python script available <a href="https://github.com/elektrobohemian/CulturalAnalytics/blob/master/SocialAnalysisStabikat.ipynb">here.</a></p>
Dataset: Publication cultures and Dutch research output: a quantitative assessment
<p>Dataset belonging to the report: <a href="https://doi.org/10.5281/zenodo.2643360">Publication cultures and Dutch research output: a quantitative assessment</a></p> <p> </p> <p>On the report:</p> <p>Research into publication cultures commissioned by VSNU and carried out by Utrecht University Library has detailed university output beyond just journal articles, as well as the possibilities to assess open access levels of these other output types. For all four main fields reported on, the use of publication types other than journal articles is indeed substantial. For Social Sciences and Arts & Humanities in particular (with over 40% and over 60% of output respectively not being regular journal articles) looking at journal articles only ignores a significant share of their contribution to research and society. This is not only about books and book chapters, either: book reviews, conference papers, reports, case notes (in law) and all kinds of web publications are also significant parts of university output.</p> <p>Analyzing all these publication forms and especially determining to what extent they are open access is currently not easy. Even combining some the largest citation databases (Web of Science, Scopus and Dimensions) leaves out a lot of non-article content and in some fields even journal articles are only partly covered. Lacking metadata like affiliations and DOIs (either in the original documents or in the scholarly search engines) makes it even harder to analyze open access levels by institution and field. Using repository-harvesting databases like BASE and NARCIS in addition to the main citation databases improves understanding of open access of non-article output, but these routes also have limitations. The report has recommendations for stakeholders, mostly to improve metadata and coverage and apply persistent identifiers.</p>
Data for the publication "The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity"
<p>This repository contains the data for the paper:</p> <p>"Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Stier, P. Partridge, D. G., Tegen, I., Bey, I., Stanelle, T., Kokkola, H., and Lohmann, U.: The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity, Geosci. Mod. Dev., https://doi.org/10.5194/gmd-2018-307, 2019."</p> <p>Each tar-file contains the data (or instructions how to obtain the data) to reproduce a figure or table in our paper.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.2553891)</p> <p> </p>
3PFL: Database of Patents and Publications with a Public-Funding Linkage
<p>The 3PFL database links information on patented inventions and scientific publications related to a public procurement contract or a research grant awarded by the U.S. Federal Government to detailed contract-level/grant-level information (e.g., awarding agency, recipient organization, award size). We have combined data from multiple sources, including (but not limited to) the United States Patent and Trademark Office bulk database, the Federal Procurement Database System, the Award Submission Portal (ASP), and the European Patent Office's PATSTAT database. We also provide a link to the scientific publications associated with these patents. The 3PFL database provides rich and original information that opens the door to novel empirical research in the economics of innovation and science. </p>
Data for the publication "Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation"
<p>Michibata et al. (2019), currently under peer-review for publication in <em>Geoscientific Model Development</em>, incorporated a diagnostic tool for warm rain microphysics into the CFMIP Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011; Swales et al., 2018), designed to evaluate model representations of aerosol–cloud–precipitation interactions at a fundamental process-level. The tool automatically generates two diagnostics related to warm rain microphysics during COSP execution in a host model. One is the contoured frequency by optical depth diagram (CFODD), which visualizes a cloud-to-rain microphysical vertical structure (Suzuki et al., 2015). The other diagnostic is a global map of warm rain fraction classified as non-precipitating clouds (< –15 dBZ<sub>e</sub>), drizzling clouds (–15 < dBZ<sub>e</sub>< 0), and precipitating clouds (0 < dBZ<sub>e</sub>).</p> <p>This repository contains the MIROC6/COSP2 input data and A-Train satellite statistics used in Michibata et al. (2019). A sample of the post-processing scripts for visualization using the GrADS software is also included in this repository.</p>
Dataset about Diada de Sant Jordi from public TV3 audiovisual archive - FIAT/IFTA Media Studies Grant
<p>[eng] This a dataset related to the Diada de Sant Jordi through TV3 public audiovisual archive. This dataset contains for a total of 572 videos with title, link, length in time and the program that have published information about this day. Most of these videos belong to the news program. This dataset is published on the occasion FIAT-IFTA Media Studies Grant 2019</p> <p>Additionally, three more files are included. These files include the final sample, the queries on Tv3 archive and recommended books that appear on the footage. The final sample has 337 videos and queries are useful to look by year </p> <p>[cat] Aquest és un dataset vinculat a la Diada de Sant Jordi dels arxius audiovisuals públics de Tv3. Aquest dataset conté un total de 572 videos amb el títol, dreçera, longitud en temps i el programa que ha publicat informació relativa aquest dia. La major part d'aquests videos corresponent corresponen als programes de notícies. Aquestes dades es publiquen amb motiu de FIAT-IFTA Media Studies Grant 2019</p> <p>A més a més s'inclouen 3 fitxers. Aquests fitxes inclouen la mostra final, les cadenes de cerca a l'arxiu de Tv3 i els llibres recomanats que apareixen en els videos. La mostra final conté 339 videos i les cadenes són útils per fer cerques per any.</p>
Dataset to the publication "A unique signal sequence of the chemokine receptor CCR7 promotes package into COPII vesicles for efficient receptor trafficking"
<p>This repository accompanies the paper:</p> <p>"A unique signal sequence of the chemokine receptor CCR7 promotes package into COPII vesicles for efficient receptor trafficking".</p> <p>Organized into 14 folders it provides the data allowing the replication of all analyses and the manuscript's figures. For a more convenient download files were zipped.</p> <p>Upon publication of results using this dataset, please cite the following paper:</p> <p>Uetz-von Allmen E, Rippl AV, Farhan H, Legler DF. 2018. A unique signal sequence of the chemokine receptor CCR7 promotes package into COPII vesicles for efficient receptor trafficking. J Leukoc Biol 104(2):375-389.</p>
Data for the publication "Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer"
<p>This dataset encompasses all data needed to reproduce the analyses presented in <a href="https://www.nature.com/articles/s41591-019-0406-6">Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer</a></p> <p>You can also check the <a href="https://github.com/zellerlab/crc_meta">GitHub repository</a></p>
Data of publication Coherent optical and spin spectroscopy of nanoscale Pr3+ : Y2O3
<p>Data corresponding to the figures of the publication " Coherent optical and spin spectroscopy of nanoscale Pr3+: Y2O3" by D. Serrano et al. (file:///C:/Users/diana.serrano/Zotero/storage/86IZ7H73/PhysRevB.100.html). A text file describes data in each compressed folder, please refer to the publication for more details. </p>
Geo-referenced Harmonized Financial Data on Soil Defense Public Works in Italy
<p>The dataset collects financial data about public works in Italy, specifically, it focuses on soil defense investments. The data is sourced from three distinct platforms: the OpenCoesione website, the OpenBDAP database, the Ministry of Economy and Finance's open data platform, and the ReNDiS database, provided by ISPRA, that exclusively gathers information about interventions in soil defense. The data obtained is interconnected using unique project codes (CUP) to prevent duplication.</p> <p>Georeferencing involves integrating geographic references into the three datasets. It enhances the accuracy of spatial analyses of spatial defense investments and provides valuable context for understanding the geographical distribution of available financial data. By incorporating geographic references such as regions, provinces, and municipalities analysts can gain insights into the spatial patterns and relationships within the datasets. This step is crucial for effective decision-making and policy formulation in the field of soil defense investments.</p> <p>Geographical references for each project were integrated using codes and names of regions, provinces, and municipalities from the ISPRA database. This database retrieves information directly from ISTAT websites, ensuring constant updates to names and codes, thus enhancing the accuracy of spatial analyses.</p> <p>Furthermore, geographical codes facilitated the association of centroids coordinates and polygon shapes for each financial observation, enhancing spatial visualization and analysis of soil defense investments, empowering decision-makers with a deeper understanding of the geographic distribution and impact of these initiatives. This comprehensive approach allows for a deeper exploration of the geographical factors influencing soil defense investments, including identifying hotspots of activity, assessing spatial trends, and understanding the localized impact of interventions on environmental sustainability and community resilience.</p> <p>The zip folder comprises four subfolders and two files. Among the files, one is a text file containing metadata, while the other is a CSV file consolidating merged data at the national level from three repositories. The subfolders contain data categorized by region and data categorized by region sourced from the three distinct repositories.</p> <p> Datasets present 28 variables: </p> <ul> <li>Columns 1-2: descriptive variables;</li> <li>Column 3: total amount financed for each intervention;</li> <li>Columns 4-9: geo-reference variables;</li> <li>Columnn 10:25: key dates of the public works process;</li> <li>Column 26: source of the data;</li> <li>Columns 27-28: geo-referencing (centroids and areal shape).</li> </ul> <p>An additional dataset has been added comprising all Italian municipalities, including thos that lack information on soil defense investments. In such a way, there are geographical information regarding all the peninsula. </p>
Data supporting publication: Revealing Mode Formation in Quasi-Bound States in the Continuum Metasurfaces via Near-Field Optical Microscopy
<p>This repository includes the data corresponding to the figures shown in the journal article entitledRevealing Mode Formation in Quasi-Bound States in the Continuum Metasurfaces via Near-Field Optical Microscopy, published in Advanced Materials on 02.08.2024</p>
Public database of geological-paleontological mapping in the surroundings of Vălioara
<p>The database contains the coordinates of the geological-paleontological mapping sites and measurements in the area of V<span>ă</span>lioara (Romania) from 2019 onwards. The Excel format file data tables contain in separate worksheets the localities, the measurements and the explanation of the mapping units. The coordinates are given in UTM34 coordinate system and also with latitude-longitude data (WGS84 datum).</p>
Additional data for publication: Simple protocol for combined extraction of exocrine secretions and RNA in small arthropods.
<p>Additional data and results are given in this repository. It contains the trimmed reads (fastp; raw reads also on SRA accession numbers SRR29851544-SRR29851549, Bioproject PRJNA1136254), full busco reports for individual transcriptomes, assembly of all six RNAseqs together (transcriptome as base for differential expression analysis) and results of salmon.</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.