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13 results for “digital sources”
Swiss public's acceptance and sustainability perceptions of food produced with chemical, digital and mechanical weed control measures and the influence of information source on technology perception in agriculture
<p><span>This data was obtained from an online survey conducted with the Swiss public from the two biggest language regions (German and French) in Switzerland. The survey was conducted in February 2023. Participants were recruited through a professional panel provider and quotas were used for age, gender and language region. The final sample contained </span><span>542 respondents. </span><span>In the first part of the survey, respondents provided basic sociodemographic information. In the second part, their sustainability perceptions regarding four different weed management practices (full-surface spraying, hoeing machine, spot spraying and precise spraying) were investigated. Respondents were then assigned to one of five information source groups, in which information on a hoeing and a milking robot was presented, using 5 different information sources (male/female farmer, male/female scientist, no source). Technology perception was assessed using several questions and aspects. Finally, respondents answered several questions assessing their attitudes towards the perception of farmers, food technology neophobia, chemophobia and the importance of naturalness. The survey can be used and adapted to different contents, aiming to investigate public perception of smart farming technologies and the influence of information sources on technology perception. </span></p>
The Collection Management System Collection - Crowd-sourcing a list of digital repository options
<p><strong>The Collection Management System Collection - Crowd-sourcing a list of digital repository options</strong></p> <p>This dataset contains a list of digital repository options for collection management systems. It has been started and complited by Ashley Blewer.<br> The data set contains:</p> <ul> <li>a PDF capture of the blog describing motivation and background, columns of the spreadsheet and further resources; originally published at https://bits.ashleyblewer.com/blog/2017/08/09/collection-management-system-collection/</li> <li>The dataset / spreadsheet of The Collection Management System Collection, originally published at https://docs.google.com/spreadsheets/d/1cXOug3qM0pNNeD_wssiVEv9c0W1Y5I1VDTnSPTk7fb4/<br> The data was exported from the google spreadsheet on November 14th 2020 into the following formats: <ul> <li>PDF</li> <li>XLSX</li> <li>CSV</li> <li>TSV</li> </ul> </li> </ul> <p>The list contains basic information, administration considerations, interface considerations, technical considerations and social considerations for 70 different repository systems.</p>
Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant
<p>The programming exercises were automatically generated by the Digital Teaching Assistant (DTA) system that automates a massive Python programming course at MIREA – Russian Technological University (RTU MIREA). Source codes of the small programs grouped by the type of the solved task can be used for benchmarking source code classification and clustering algorithms. Moreover, the data can be used for training intelligent program synthesizers, or benchmarking mutation testing frameworks, and more applications are yet to be discovered. This dataset is a supplementary material for a paper entitled <a href="https://doi.org/10.3390/data8060109"><strong>Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant</strong></a> submitted to the <strong>MDPI Data</strong> journal.</p>
Digitized geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy) collected from public sources.
<p>The database is a supplementary material of:</p><p>Livani, M., Petracchini, L., Benetatos, C., Marzano, F., Billi, A., Carminati, E., Doglioni, C., Petricca, P., Maffucci, R., Codegone, G., Rocca, V., Antoncecchi, I. (2023). Subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy). Earth System Science Data Discussions, 15, n. 9, 4261-4293, doi: 10.5194/essd-15-4261-2023</p><p> </p><p>This database comprises subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy). We realized the database by collecting, revising, and digitizing data, originally in raster format, from public sources. These data have been then used to reconstruct the overall subsurface 3D architecture and to extract the physical properties of the subsurface geological units.</p><p>The data have a common geographical system: WGS 84/UTM zone 32N; EPSG: 32632.</p><p>The database contains borehole information from 160 deep wells (i.e., wellhead coordinates, rotary table elevation, measured depth, true depth, total depth and deviation survey) and digitized Spontaneous Potential, Gamma Ray, and Sonic logs. Five horizons were digitized from 61 geological cross-sections and from 10 isobath maps that roughly correspond to the boundaries of units showing different lithological properties and with different mechanical properties. The horizons are, from the oldest to the youngest: the top of the magnetic basement, the top of the carbonate succession, the base of the Pliocene, the base of the Calabrian and the base of recent continental deposits. In addition, the gridded surfaces of the 3D geological model are available.</p><p>We organized the database into two groups: "primitive data" and "derived data".</p><p>Primitive data are the result of the digitization of public data. The database of the primitive data is formed by the main horizons reported in the geological cross-sections, the isobaths of the main geological surfaces, the well locations, comprised their trajectory along depth, lithological and stratigraphical information, and geophysical logs from composite well logs. Well data include specific sets of well logs aimed to geological/mechanical characterization of the geological units (e.g., Spontaneous Potential log, Resistivity log, Gamma Ray log, and Sonic log).</p><p>Derived data consist of two datasets: i) the primitive isobaths maps and geological cross-sections data that have been filtered and verified after a data accuracy analysis performed to unravel discrepancies in the interpretation of the subsurface geological horizons; ii) a set of regional surfaces of the main geological units of the Po Plain subsurface. These surfaces were generated by interpolating the filtered primitive data and without considering the fault occurrence/displacements.</p><p>Primitive and derived data are provided in delimited text file format organized according to the data type (i.e., well, geological cross-section, map and gridded surface). The format and the organization of data are explained in the related "readme" file presents within each data folder.</p><p>Our database represents a collection of the main published works regarding the Po Plain. Detailed studies related to specific sectors of the Po Plain might not be present in our database.</p><p>Further details about the data processing and organization are given in the related manuscript.</p><p> </p>
Normalisation of Early Modern Science: Digitized Corpus of 17th- and 18th-Century Sources
<p>This dataset contains a digitized corpus of early modern natural philosophy works that underlie the European Research Commission-funded Starting Grant “The Normalisation of Natural Philosophy: How Teaching Practices Shaped the Evolution of Early Modern Science,” (grant agreement No. 801653 NaturalPhilosophy), led by Dr. Andrea Sangiacomo at the Faculty of Philosophy at the University of Groningen.</p> <p>The methodology we used for the digitization of the present dataset is described in the paper:</p> <ul> <li>A. Sangiacomo, H. Hogenbirk, R. Tanasescu, A. Karaisl, N White. 2022. “Reading in the Mist: High-Quality Optical Character Recognition Based on Early Modern Digitized Books.” <em>Digital Scholarship in the Humanities</em>. https://bit.ly/3vwvwKI</li> </ul> <p>The inventory of the present dataset is available at <a href="https://zenodo.org/record/5566681">DOI: 10.5281/zenodo.5566681</a></p> <p>The methodology behind the retrieval, cleaning, and annotation of the above inventory is described in the paper:</p> <ul> <li>Sangiacomo, Andrea; Tanasescu, Raluca; Donker, Silvia; Hogenbirk, Hugo. 2021. “Mapping the Evolution of Early Modern Natural Philosophy: Corpus Collection and Authority Acknowledgement,” published in the <em>Annals of Science</em> (DOI: 10.1080/00033790.2021.1992502; permanent link: <a href="https://doi.org/10.1080/00033790.2021.1992502">https://doi.org/10.1080/00033790.2021.1992502</a></li> </ul> <p>The dictionaries from which we selected the data in worksheets 2-5 in the inventory are the following:</p> <ul> <li>Wiep van Bunge, Henri Krop, Bart Leeuwenburgh, Paul Schuurman, Han van Ruler and Michiel Wielema, <em>Dictionary of Seventeenth- and Eighteenth-Century Dutch Philosophers</em> (London: Bloomsbury, 2003);</li> <li>John Yolton, Valdimir Price and John Stephens. <em>Dictionary of Eighteenth-Century British Philosophers</em> (London: Bloomsbury, 1999);</li> <li>Andrew Pyle. <em>Dictionary of Seventeenth-Century British Philosophers</em> (London: Bloomsbury, 2000);</li> <li>Luc Foisneau. <em>Dictionary of Seventeenth-Century French Philosophers</em> (London: Bloomsbury, 2008);</li> <li>Heiner F. Klemme and Manfred Kuehn. <em>Dictionary of Eighteenth-Century Philosophers</em> (London: Bloomsbury, 2011).</li> </ul> <p>University of Groningen Team:</p> <ul> <li>Andrea Sangiacomo (principal investigator)</li> <li>Raluca Tanasescu (postdoctoral researcher)</li> <li>Silvia Donker and Hugo Hogenbirk (PhD students)</li> <li>Cristian A. Marocico (scientific programmer, Center for Information Technology)</li> <li>Wim Breakman (bibliographer, University of Groningen Library)</li> </ul>
Digitized Particulate Matter Size Distribution Profiles from Literature Sources for Improved Size Representation of PM Emissions in Atmospheric Chemical Transport Models
<p>Processing particulate matter (PM) emissions for use in a chemistry transport model (CTM) such as GEM-MACH (Global Environmental Multiscale Modelling Air-Quality and Chemistry) requires detailed information about particle size distribution and chemical speciation for different PM emissions source types. The current PM size distribution and speciation profile library used at Environment and Climate Change Canada (ECCC) for preparing model-ready emission files for GEM-MACH contains very detailed chemical speciation profiles for PM emissions from 91 source types but only has three generic PM size disaggregation profiles, one each for mobile, point, and area sources. These generic profiles are used to disaggregate bulk PM emissions to a 12-bin sectional size representation, where PM<sub>2.5</sub> emissions are split into size bins 1-8 and PM<sub>10‑2.5</sub> emissions are split into bins 9 and 10. Since there is wide variability in the particle size distribution depending on the source type, the inclusion of source-type-specific PM size disaggregation profiles should lead to better representation of PM particle size for emissions from different source types in the model.</p> <p>A presentation entitled “Expansion of a Size Distribution Profile Library for Particulate Matter (PM) Emissions Processing from Three to 32 Source Categories” was given recently at the Community Modeling and Analysis System (CMAS) conference in Chapel Hill, North Carolina in October 2019 (<a href="https://www.cmascenter.org/conference/2019/slides/1300_zhang_expansion_size_2019.pptx">https://www.cmascenter.org/conference//2019/slides/1300_zhang_expansion_size_2019.pptx</a>) . This presentation described work carried out at ECCC to improve the PM size disaggregation profile library used to generate model-ready emissions. In particular, the number of PM size disaggregation profiles in the library was increased from three generic profiles to 32 source-type-specific profiles. After the conference, four more profiles were added to the library for a total of 36 PM size disaggregation profiles. In order to carry out this study, over 100 PM size distribution profiles from various PM emissions sources were gathered from literature publications, analyzed, and transformed into size disaggregation profiles that correspond to the GEM-MACH 12-bin sectional configuration. The 36 PM size disaggregation profiles that were obtained were then combined with detailed PM chemical speciation data to compile a new PM size disaggregation and chemical speciation library for emissions processing using the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system.</p> <p>This Excel workbook provides the digitized particle size distribution data for PM emissions from 36 different source types that were used as input to calculate the PM size disaggregation profiles for the GEM-MACH 12-bin sectional configuration. The digitized particle size distribution profiles were obtained by digitizing images of size distribution plots obtained from the literature publications using graph digitizing software such as Engauge Digitizer (<a href="http://markummitchell.github.io/engauge-digitizer/">http://markummitchell.github.io/engauge-digitizer/</a>) and WebPlot Digitizer (<a href="https://directory.fsf.org/wiki/WebPlotDigitizer">https://directory.fsf.org/wiki/WebPlotDigitizer</a>). By manually defining the axes and selecting points along the curve by computer mouse, a comma-separated-values file was generated for each size distribution profile image. From there, a series of transformations were carried out as required, including particle diameter conversions from aerodynamic diameter to Stokes diameter, and conversion of number-weighted size distributions to volume-weighted size distributions, in order to obtain a harmonized set of profiles. This Excel workbook contains the raw digitized data for all literature size distributions included in the compilation of the new library, as well as the diameter and size distribution weighting conversions. There are 39 worksheets: the first is an introductory worksheet entitled “Spreadsheet_Info” while the next 36 worksheets are ordered alphabetically and correspond to each of the 36 emissions source types for which a PM size disaggregation profile was generated. The final two worksheets contain digitized particle penetration data for common PM control devices.</p> <p>These digitized profiles may be used and adapted for use with other emissions processing systems and other CTMs with a size-resolved representation for PM. More details are provided in the following publication:</p> <p>Elisa I. Boutzis, Junhua Zhang & Michael D. Moran (2020) Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles, <em>Journal of the Air & Waste Management Association</em>, 70:11, 1067-1100, DOI: <a href="https://doi.org/10.1080/10962247.2020.1743794">10.1080/10962247.2020.1743794</a></p>
A Systematic Mapping of the Classification of Open Educational Resources for Computer Science Education in Digital Sources (Data)
<p>Data from a Systematic Mapping of the classification of Open Educational Resources for Computer Science Education.</p> <p>Content:</p> <ul> <li>Studies selected</li> <li>Digital sources used to classify Open Educational Resources for Computer Science Education</li> <li>Computer Science domains explored by Open Educational Resources</li> <li>Approaches for the classification of Open Educational Resources for Computer Science Education</li> </ul>
B-slim, a Multi-source Digital Super Coach for Sustainable Weight Loss
ClinicalTrials.gov study NCT02595671. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Sources and types of errors in digital archaeological data
<p>Sources and types of errors in data collection and compilation, data processing and data usage that result in final global error, adapted and modified from Hunter and Beard 1992</p>
The Sloan Digital Sky Survey Reverberation Mapping Project: the XMM-Newton X-ray source catalog and multi-band counterparts
<p>The XMM-RM project was designed to provide X-ray coverage of the Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) field. 41 XMM-Newton exposures, placed surrounding the Chandra AEGIS field, were taken, covering an area of 6.13 deg2 and reaching a nominal exposure depth of ∼ 15 ks. We present an X-ray catalog of 3553 sources detected in these data, using a PSF-fitting algorithm and a sample selection threshold that produces a ∼ 5% fraction of spurious sources. The Bayesian method “NWAY” was employed to identify counterparts of the X-ray sources from the optical Legacy and the IR unWISE catalogs, using a 2-dimensional unWISE magnitude-color prior created from optical/IR counterparts of Chandra X-ray sources. 932 of the XMM-RM sources are covered by SDSS spectroscopic observations. 89% of them are classified as AGN, and 71% of these AGN are in the SDSS-RM quasar catalog. Among the SDSS-RM quasars, 80% are detectable at the depth of the XMM observations. Forced X-ray photometry is performed at the positions of the undetected SDSS-RM quasars.</p>
Heat Capacity Mapping Mission Digital Source
The HCMM Digital Source dataset includes approximately 2400 scenes of recovered digital data with a resolution of 100 dpi. The original scenes are 715 km wide and vary in length from 715 to 3,000 km. The file size is 3-13 MB depending on the length of the scene and is stored in a TIFF format. The source data was transmitted to seven ground stations and stored on binary magnetic tape. The source data on tape is no longer readable and the only remaining set of HCMM data is on black and white film.
Sloan Digital Sky Survey Stripe 82 Chandra Source Match Catalog
This table contains some of the data from the latest release of the Stripe 82 X-ray (82X) survey point-source catalog, which currently covers 31.3 deg<sup>2</sup> of the Sloan Digital Sky Survey (SDSS) Stripe 82 Legacy field. In total, 6,181 unique X-ray sources are significantly detected with XMM-Newton (> 5 sigma) and Chandra (> 4.5 sigma). This 31 deg<sup>2</sup> catalog release includes data from XMM-Newton cycle AO 13, which approximately doubled the Stripe 82X survey area. The flux limits of the Stripe 82X survey are 8.7 x 10<sup>-16</sup> erg s<sup>-1</sup> cm<sup>-2</sup>, 4.7 x 10<sup>-15</sup> erg s<sup>-1</sup> cm<sup>-2</sup>, and 2.1 x 10<sup>-15</sup> erg s<sup>-1</sup> cm^=2^ in the soft (0.5 - 2.0 keV), hard (2 - 10 keV), and full (0.5 - 10 keV) bands, respectively, with approximate half-area survey flux limits of 5.4 x 10<sup>-15</sup> erg s<sup>-1</sup> cm<sup>-2</sup>, 2.9 x 10<sup>-14</sup> erg s<sup>-1</sup> cm<sup>-2</sup>, and 1.7 x 10<sup>-14</sup> erg s<sup>-1</sup> cm<sup>-2</sup>, respectively. The authors matched the X-ray source lists to available multi-wavelength catalogs, including updated matches to the previous release of the Stripe 82X survey; 88% of the sample is matched to a multi-wavelength counterpart. Due to the wide area of Stripe 82X and rich ancillary multi-wavelength data, including coadded SDSS photometry, mid-infrared WISE coverage, near-infrared coverage from UKIDSS and VISTA Hemisphere Survey (VHS), ultraviolet coverage from GALEX, radio coverage from FIRST, and far-infrared coverage from Herschel, as well as existing ~30% optical spectroscopic completeness, this study is beginning to uncover rare objects, such as obscured high-luminosity active galactic nuclei at high redshift. The Stripe 82X point source catalog is a valuable data set for constraining how this population grows and evolves, as well as for studying how they interact with the galaxies in which they live. The authors derive the XMM-Newton number counts distribution and compare it with their previously reported Chandra log N - log S relations and other X-ray surveys. Throughout this study, the authors adopt a cosmology of H<sub>0</sub> = 70 km s<sup>-1</sup> Mpc<sup>-1</sup>, Omega<sub>M</sub> = 0.27, and Lambda = 0.73. The XMM-Newton and Chandra X-ray sources were matched with sources in the SDSS, WISE, UKIDSS, VHS, GALEX, FIRST and Herschel databases using the maximum likelihood estimator (MLE) method, as discussed in detail in Section 4 of the reference paper. This table contains the list of 1,146 Chandra sources detected in the SDSS Stripe 82. A related table SDSSS82XMM contains the list of 5,220 XMM-Newton sources detected in the SDSS Stripe 82. This table was initially created by the HEASARC in April 2014 based on the machine-readable version of the table ('Properties of SDSS Quasars Detected by Chandra') described in Appendix B1 of the reference paper (LaMassa et al. 2013, MNRAS, 436, 3581) which was obtained from the CDS (their catalog J/MNRAS/436/3581/ file chands82.dat). The present version was created by the HEASARC in January 2017 based on <a href="https://cdsarc.cds.unistra.fr/ftp/cats/J/ApJ/817/172">CDS Catalog J/ApJ/817/172</a> file chandra.dat. This is a service provided by NASA HEASARC .
Sloan Digital Sky Survey Stripe 82 XMM-Newton Source Match Catalog
This table contains some of the data from the latest release of the Stripe 82 X-ray (82X) survey point-source catalog, which currently covers 31.3 deg<sup>2</sup> of the Sloan Digital Sky Survey (SDSS) Stripe 82 Legacy field. In total, 6,181 unique X-ray sources are significantly detected with XMM-Newton (> 5 sigma) and Chandra (> 4.5 sigma). This 31 deg<sup>2</sup> catalog release includes data from XMM-Newton cycle AO 13, which approximately doubled the Stripe 82X survey area. The flux limits of the Stripe 82X survey are 8.7 x 10<sup>-16</sup> erg s<sup>-1</sup> cm<sup>-2</sup>, 4.7 x 10<sup>-15</sup> erg s<sup>-1</sup> cm<sup>-2</sup>, and 2.1 x 10<sup>-15</sup> erg s<sup>-1</sup> cm^=2^ in the soft (0.5 - 2.0 keV), hard (2 - 10 keV), and full (0.5 - 10 keV) bands, respectively, with approximate half-area survey flux limits of 5.4 x 10<sup>-15</sup> erg s<sup>-1</sup> cm<sup>-2</sup>, 2.9 x 10<sup>-14</sup> erg s<sup>-1</sup> cm<sup>-2</sup>, and 1.7 x 10<sup>-14</sup> erg s<sup>-1</sup> cm<sup>-2</sup>, respectively. The authors matched the X-ray source lists to available multi-wavelength catalogs, including updated matches to the previous release of the Stripe 82X survey; 88% of the sample is matched to a multi-wavelength counterpart. Due to the wide area of Stripe 82X and rich ancillary multi-wavelength data, including coadded SDSS photometry, mid-infrared WISE coverage, near-infrared coverage from UKIDSS and VISTA Hemisphere Survey (VHS), ultraviolet coverage from GALEX, radio coverage from FIRST, and far-infrared coverage from Herschel, as well as existing ~30% optical spectroscopic completeness, this study is beginning to uncover rare objects, such as obscured high-luminosity active galactic nuclei at high redshift. The Stripe 82X point source catalog is a valuable data set for constraining how this population grows and evolves, as well as for studying how they interact with the galaxies in which they live. The authors derive the XMM-Newton number counts distribution and compare it with their previously reported Chandra log N - log S relations and other X-ray surveys. Throughout this study, the authors adopt a cosmology of H<sub>0</sub> = 70 km s<sup>-1</sup> Mpc<sup>-1</sup>, Omega<sub>M</sub> = 0.27, and Lambda = 0.73. The XMM-Newton and Chandra X-ray sources were matched with sources in the SDSS, WISE, UKIDSS, VHS, GALEX, FIRST and Herschel databases using the maximum likelihood estimator (MLE) method, as discussed in detail in Section 4 of the reference paper. This table contains the list of 5,220 sources detected in the SDSS Stripe 82 in archival, AO10 and AO13 XMM-Newton observations. A related table SDSSS82CXO contains the list of 1,146 Chandra sources detected in the SDSS Stripe 82. Compared to the initial version of this catalog based on the 2013 paper, in the current version of the catalog the MLE matching between the XMM-Newton archival and AO10 source lists and ancillary catalogs was updated to include a 1 arcsecond systematic error added in quadrature to the emldetect reported positional error. This table was initially created by the HEASARC in April 2014 based on the machine-readable version of the table ('Properties of SDSS Quasars Detected by XMM-Newton') described in Appendix B2 of the reference paper (LaMassa et al. 2013, MNRAS, 436, 3581) which was obtained from the CDS (catalog J/MNRAS/436/3581/, file xmms82.dat). The present version was created by the HEASARC in January 2017 based on <a href="https://cdsarc.cds.unistra.fr/ftp/cats/J/ApJ/817/172">CDS catalog J/ApJ/817/172</a>, files xmmao10.dat and xmmao13.dat. This is a service provided by NASA HEASARC .
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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.