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Japan-Educated Officials in China's Wartime Central Administration (1944)
<p>This dataset is made up of two files:</p> <ol> <li>"CNKI-20231014003254589" is the raw extraction of bibliographical references from CNKI on the Chinese students who stuided in Japan before 1945. It consists in the main academic outputs on the topic, including journal article,s M.A. thesis, and doctoral dissertations.</li> <li>"CNKIjp2" is the pre-processed and cleaned file that was used for statistical analysis and topic modeling.</li> </ol> <p>The markdown script presents the core of the methodological framework for my study of Chinese historiography on the Japan-educated students in the late imperial and republican period. I developed this script as part of the paper titled "Japan-Educated Officials in China’s Wartime Central Administration (1944)". In this paper, I present a review of the literature on the Chinese students who went to Japan to study between 1896 and 1945. While the literature in English and Japanese is small and allwo for close reading, the literature in Chinese is massive. To explore this literature and identify research trends, I chose to apply topic modeling to the dataet of references extracted from CNKI (知網). The documentary basis consists in the abstracts of the academic outputs.</p>
Shapefile of administrative boundaries in Glasgow, UK, around 1920
<p>This dataset consists of a shapefile of administrative boundaries (municipal wards) in Glasgow around 1920, based on 'Map of the City of Glasgow shewing Parliamentary Divisions as fixed in 1918, and Municipal Wards as fixedin 1920'. The map is held in the Glasgow City Archives, reference DTC/13/98.</p> <p>Shapefile construction was undertaken as described in the related article:</p> <p>Angelopoulos, K., Stewart, G. and Mancy, R. <em>Local infectious disease experience influences vaccine refusal rates: a natural experiment. Proceedings of the Royal Society B: Biological Sciences. DOI: 10.1098/rspb.2022.1986.</em></p> <p>The attributes which are included in the shapefile are Ward_Num (municipal ward number) and Ward_Name (municipal ward name). Full details of the wards numbers and ward names are given in the Report of the Medical Officer of Health for the City of Glasgow for 1921, which can be accessed at<em> https://wellcomecollection.org/works/jxgvafxr/items. </em></p>
Shapefiles of administrative boundaries, Subway and main rivers in Glasgow, UK, around 1910
<p>This collection consists of ESRI shapefiles for Glasgow around 1910:</p> <ul> <li>sanitary district boundaries in 1903 (Sanitary_Districts.shp, etc.)</li> <li>municipal ward boundaries in 1912 (Wards_1912.shp, etc.)</li> <li>registration district boundaries within the area of the City of Glasgow in 1913 (Registration_Districts.shp, etc.)</li> <li>routes of main rivers (River Clyde and River Kelvin) around 1915 (Rivers.shp, etc.)</li> <li>route of the Glasgow Subway around 1915 (Subway.shp, etc.)</li> </ul> <p>For details of shapefile construction, please see the descriptions in the following article:</p> <p>Angelopoulos, K., Stewart, G. and Mancy, R. <em>Local infectious disease experience influences vaccine refusal rates: a natural experiment. Proceedings of the Royal Society B: Biological Sciences. DOI: 10.1098/rspb.2022.1986.</em></p> <p>Details of construction and references to original map sources are provided in the second paragraph of the section "Geographic conversion" in the online supplementary materials of the above reference. Further information about the boundaries is provided in the caption of Figure S1 of the supplementary materials. Additional contextual information is provided in both the main text and supplementary materials.</p>
State of Wildfires 2024-25: Regional Summaries of Burned Area, Fire Emissions, and Individual Fire Characteristics for National, Administrative and Biogeographical Regions
<p>This dataset supports the State of Wildfires 2024-25 report under review at <em>Earth System Science Data</em> (Kelley et al., <em>under review)</em>. It is an update of the State of Wildfires 2023-24 report (Jones et al. 2024). The dataset provides annual data and final-year anomalies in burned area (BA), fire carbon (C) emissions, and fire properties (e.g. distributional statistics for fire count, size, rate of growth). Annual data relate to the global fire season defined as March-February (e.g., March 2024-February 2025), aligning with an annuall lull in the global fire calendar (see Jones et al., 2024). The complete methodology is described by Kelley et al. (<em>under review</em>).</p> <h3>Citation</h3> <p>Work utilising our regional summaries should <strong>cite both Kelley et al. (under review) AND the primary reference for the variable(s) of interest</strong> as follows:</p> <ul> <li>Giglio et al. (2018) for MODIS MCD64A1 BA.</li> <li>van der Werf et al. (2017) for GFED4.1s fire C emissions.</li> <li>Kaiser er al. (2012) for GFAS fire C emissions.</li> <li>van der Werf et al. (2017) AND Kaiser er al. (2012) for the average of GFED4.1s and GFAS fire C emissions.</li> <li>Andela et al. (2019) for the Global Fire Atlas.</li> <li>Giglio et al. (2016) for the Fire Radiative Power (FRP) observations.</li> <li>Chuvieco et al. (2024) for FireCCIS311 BA.</li> <li>Giglio et al. (2024) for VIIRS VNP64A1 BA.</li> </ul> <h3>Input Data</h3> <p><strong>Burned Area (BA)</strong></p> <ul> <li>BA data from NASA’s MODIS BA product (MCD64A1) are extended from Giglio et al. (2018) and are available from <a href="https://lpdaac.usgs.gov/products/mcd64a1v061/">Giglio et al. (2021)</a>. <ul> <li>Period: 2002-February 2025</li> <li>Resolution: 500m, daily</li> </ul> </li> <li>BA data from ESA's Climate Change Initiative BA product (FireCCIS311) are extended from Lizundia-Loiola et al. (2022) and are available from <a href="Chuvieco,%20E.;%20Pettinari,%20M.L.;%20Lizundia-Loiola,%20J.;%20Khairoun,%20A.;%20Danne,%20O.;%20Boettcher,%20M.;%20Storm,%20T.%20(2024):%20ESA%20Fire%20Climate%20Change%20Initiative%20(Fire_cci):%20Sentinel-3%20SYN%20Burned%20Area%20Grid%20product,%20version%201.1.%20NERC%20EDS%20Centre%20for%20Environmental%20Data%20Analysis,%2029%20February%202024.%20https://catalogue.ceda.ac.uk/uuid/da8e669a74334c82a56e0b470bc4ef04">Chuvieco et al. (2024)</a>. <ul> <li>Period: 2019-February 2025</li> <li>Resolution: 300m, daily</li> </ul> </li> <li>BA data from NASA’s VIIRS BA product (VNP64A1) are available from <a href="https://lpdaac.usgs.gov/products/vnp64a1v002/">Giglio et al. (2024)</a>. <ul> <li>Period: 2012-February 2025 (only the data after 2019 are used for consistency in the comparisons between MCD64A1, FireCCIS311, and VNP64A1).</li> <li>Resolution: 500m, daily</li> </ul> </li> </ul> <p><strong>Fire Carbon (C) Emissions</strong></p> <ul> <li>GFED4.1s fire C emissions data are extended from van der Werf and are available at <a href="https://globalfiredata.org/">https://globalfiredata.org/</a>. <ul> <li>Period: 2003-February 2025</li> <li>Resolution: 0.25 degree, daily</li> </ul> </li> </ul> <ul> <li>GFAS fire C emissions data are extended from Kaiser et al. (2012) and are available from the <a href="https://confluence.ecmwf.int/display/CKB/CAMS+global+biomass+burning+emissions+based+on+fire+radiative+power+%28GFAS%29%3A+data+documentation">ECMWF Confluence Server</a>. <ul> <li>Period: 2003-February 2025</li> <li>Resolution: 0.1 degree, daily</li> </ul> </li> </ul> <p><strong>Global Fire Atlas (Individual Fire Properties)</strong></p> <ul> <li>Global Fire Atlas data are extended from Andela et al. (2019) and are available from the repository maintained by <a href="https://doi.org/10.5281/zenodo.11400062">Andela and Jones (2025)</a>. <br> <ul> <li>Period: 2002-February 2025</li> <li>Driven by 500m MODIS BA data (collection 6.1)</li> </ul> </li> </ul> <p><strong>Fire Intensities</strong></p> <ul> <li>FRP data are extended from MOD14A1 and MYD14A1 (Giglio et al., 2016) and are available at <a href="https://lpdaac.usgs.gov/products/mod14a1v061/">Giglio and Justice (2021)</a>.<br> <ul> <li>Period: 2002-February 2025</li> <li>Resolution: 1km, daily</li> </ul> </li> </ul> <h3>Regional Analysis</h3> <p>We performed "cookie-cutting" (spatial and temporal masking) of the above input data sets to features in each of the following regional layers (e.g. per country in the "Countries" layer). </p> <p>The statistics derived from cookie-cutting are listed below. Full details in Kelley et al. (2025).</p> <div> <table> <tbody> <tr> <td> <p>Layer</p> </td> <td> <p>Short Form </p> </td> <td> <p>Source</p> </td> </tr> <tr> <td> <p>Biomes</p> </td> <td> <p>NA</p> </td> <td> <p>Olson et al. (2001)</p> </td> </tr> <tr> <td> <p>Ecoregions</p> </td> <td> <p>NA</p> </td> <td> <p>Olson et al. (2001)</p> </td> </tr> <tr> <td> <p>Continents</p> </td> <td> <p>NA</p> </td> <td> <p>ArcGIS Hub (2024)</p> </td> </tr> <tr> <td> <p>Continental Biomes</p> </td> <td> <p>NA</p> </td> <td> <p>See above</p> </td> </tr> <tr> <td> <p>Countries</p> </td> <td> <p>NA</p> </td> <td> <p>EU Eurostat (2020)</p> </td> </tr> <tr> <td> <p>UC Davis Global Administrative Areas (GADM) Level 1</p> </td> <td> <p>GADM-L1</p> </td> <td> <p>UC Davis (2022)</p> <br><br></td> </tr> <tr> <td> <p>Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) Working Group I (WGI) Reference Regions </p> </td> <td> <p>IPCC AR6 WGI Regions</p> </td> <td> <p>Iturbide et al. (2020)</p> </td> </tr> <tr> <td> <p>Global C Project Regional C Cycle Assessment and Processes (RECCAP2) Reference Regions</p> </td> <td> <p>RECCAP2 Regions</p> </td> <td> <p>Ciais et al. (2022)</p> </td> </tr> <tr> <td> <p>Global Fire Emissions Database (GFED) Basis Regions</p> </td> <td> <p>GFED4.1s Regions</p> </td> <td> <p>van der Werf et al. (2006)</p> </td> </tr> </tbody> </table> </div> <h3> </h3> <h3>Regional Statistics and Anomalies</h3> <ul> <li><strong>Burned Area (BA)</strong> <ul> <li>Calculated regional totals for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranking amongst all recorded fire seasons.</li> <li>Onset, peak, and cessation based on monthly deviations from climatological means.</li> </ul> </li> </ul> <ul> <li><strong>Carbon Emissions</strong> <ul> <li>Calculated regional totals for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2003).</li> <li>Ranking amongst all recorded fire seasons.</li> <li>Onset, peak, and cessation based on monthly deviations from climatological means.</li> <li>Statistics available for GFAS, GFED, and their mean.</li> </ul> </li> </ul> <ul> <li><strong>Individual Fire Properties</strong> <ul> <li>Based on values of individual fire size and rate of growth ignition from the ignition point vectors of the Global Fire Atlas.</li> <li>Calculated regional count.</li> <li>Calculated regional maxima and 95th percentiles of fire size and rate of growth for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranked anomalies among all recorded fire seasons.</li> </ul> </li> </ul> <ul> <li><strong>Fire Intensity</strong> <ul> <li>Based on active fire observations of FRP, which are pooled within each fire of the Global Fire Atlas.</li> <li>For each fire, the 95th percentile value of all FRP observations is the assigned intensity value (i.e. a "peak fire intensity" omitting any spurious high-end values).</li> <li>Regionally, the peak fire intensity values are averaged across individual fires.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranked anomalies among all recorded fire seasons.</li> </ul> </li> </ul>
Job applications for positions in the Napoleonic administration (1800-1815)
<p>Dataset of application letters for positions in the Napoleonic administration (prefect, sub-prefect, secretary general, and counselor of the prefecture) in the French Republic and later the French Empire, drafted by a sample of 330 French and Italian candidates. The 800 applications are sourced from the French National Archives, collection F1dII. The table contains metadata extracted by the researcher. Column headers include a legend explaining the content where necessary. For columns where this is not specified, the content should be interpreted as follows. For instance, in the column concerning the candidates' previous experience as prefects or sub-prefects: "SPsi" = yes, the candidate has served as a sub-prefect; "SPno" = no experience as a sub-prefect. Other columns follow a similar structure. The final column contains full transcriptions of the texts, without lemmatization, preserving the original spelling. The concluding salutations in the letters have not been transcribed.</p>
The optimal period for oocyte retrieval after the administration of recombinant human chorionic gonadotropin in in vitro fertilization
<p>This is the dataset of the study called "The optimal period for oocyte retrieval after the administration of recombinant human chorionic gonadotropin in in vitro fertilization".</p> <p><strong>Abstract</strong></p> <p>Background</p> <p>Our objective was to investigate the existence of an optimal period for oocyte retrieval in regards to the clinical pregnancy occurrence after the administration of recombinant human chorionic gonadotropin (rhCG) (Ovitrelle®).</p> <p>Methods</p> <p>We studied the digital records of 3362 middle eastern couples who underwent in vitro fertilization (IVF) treatment between 2019 and 2021.</p> <p>Results</p> <p>Through statistical testing, we found that there is a significant positive correlation between the oocyte retrieval period and the clinical pregnancy occurrence up to the 37th hour, where retrieval at the 37th hour was found to provide the most optimal outcome, especially in the case of gonadotropin-releasing hormone agonist (GnRHa) long protocol.</p> <p>Conclusions</p> <p>This cohort study recommends retrieval at hour 37 after ovulation triggering under the described conditions.</p>
Pan-EU Landmask: 10m Resolution Geospatial Land Coverage with Administrative Boundary details on country and regional level
<p><strong>Pan-EU Land Mask Summary</strong></p> <p>Considering the land mask for pan-EU, we will closely match the data coverage of <a href="https://land.copernicus.eu/pan-european">https://land.copernicus.eu/pan-european</a> i.e. the official selection of countries listed here: <a href="https://land.copernicus.eu/portal_vocabularies/geotags/eea39">https://lanEEA39d.copernicus.eu/portal_vocabularies/geotags/eea39</a>.</p> <p>There are a total of three landmask files available, each of which is aligned with the standard spatial/temporal resolution and sizes of <a href="https://ai4soilheath.eu">AI4SoilHealth</a> Data Cube specifications, which is: Xmin = 900,000, Ymin = 899,000, Xmax = 7,401,000, Ymax = 5,501,000, with Coordinate reference system of epsg:3035. Additionally, these files include a corresponding look-up table that provides explanations for the values present in the raster data. The scripts used to generate these masks can be found <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/tree/main/paneu_landmask">here</a>.</p> <p>The masks are:</p> <ol> <li> <p>Landmask</p> </li> <li> <p>ISO-code country mask</p> </li> <li> <p>NUTS3 mask</p> </li> </ol> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, the files here are named according to the standard OpenLandMap file-naming convention. The OpenLandMap file-naming convention works with 10 fields that basically define the most important properties of the data, this way users can search files, prepare data analysis etc, without even needing to access or open files. The 10 fields include:</p> <ol> <li> <p>Generic variable name: country.code</p> </li> <li> <p>Variable procedure combination i.e. method standard (standard abbreviation): iso.3166</p> </li> <li> <p>Position in the probability distribution / variable type: c</p> </li> <li> <p>Spatial support (usually horizontal block) in m or km: 30m</p> </li> <li> <p>Depth reference or depth interval e.g. below ("b"), above ("a") ground or at surface ("s"): s</p> </li> <li> <p>Time reference begin time (YYYYMMDD): 20210101</p> </li> <li> <p>Time reference end time: 20211231</p> </li> <li> <p>Bounding box (2 letters max): eu </p> </li> <li> <p>EPSG code: epsg.3035</p> </li> <li> <p>Version code i.e. creation date: v20230722</p> </li> </ol> <p>An example of a file-name based on the description above:</p> <p><em>country.code_iso.3166_c_100m_s_20210101_20211231_eu_epsg.3035_v20230722</em></p> <p><strong>Landmask</strong></p> <p>The basic principle to create the land mask is to include as much as land as possible, to avoid missing any land pixels and ensure precise differentiation between land, ocean and inland water bodies.</p> <p>Two reference datasets are used, </p> <ol> <li> <p><a href="https://esa-worldcover.org/en">WorldCover</a>, 10 m resolution.</p> </li> <li> <p><a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a>, with shapefiles of administrative boundaries, inland water bodies, ocean and landmask.</p> </li> </ol> <p>When generating the land mask, the two reference datasets in a way that:</p> <ul> <li> <p>If either of the two reference datasets identifies a pixel as land, it is considered a land pixel in our mask. </p> </li> <li> <p>Regarding ocean and inland water bodies, a pixel is classified as a water pixel only when both reference datasets confirm its identification as water.</p> </li> </ul> <p>The landmask consists of 4 values:</p> <ul> <li> <p>10: not in the pan-EU area, i.e. out of mapping scope</p> </li> <li> <p>1: land</p> </li> <li> <p>2: inland water</p> </li> <li> <p>3: ocean</p> </li> </ul> <p>This landmask is available in 10m, 30m, 100m, 250m, and 1km resolution formats respectively. The coarse resolution landmasks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “min” in GDAL. This “min” method allows taking the minimum values from the contributing pixels, to keep as much land as possible.</p> <p><strong>ISO-3166 country code mask</strong></p> <p>This ISO-3166 country code mask is created from <a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a> country shapefile. This mask is available in 10m, 30m and 100m resolution. In this raster file, each country is assigned a unique value, which allows for the interpretation and analysis of data associated with a specific country.</p> <p>The values are assigned to each country according to iso-3166 country code, which can be found in the corresponding look-up table. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.</p> <p><strong>NUTS-3 mask</strong></p> <p>The nuts-3 code mask is created from the European NUTS3 shapefile. In this raster file, each unique NUT3 level area is assigned a unique value, which allows for the interpretation and analysis of data associated with specific NUTS3 regions.</p> <p>The values of pixels and its associated meanings can be found in the corresponding look-up table. This nut-3 code mask is available in 10m, 30m and 100m resolution formats. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.</p> <p>It should be noted that the ISO-code country mask covers a more extensive area compared to the NUTS3 mask. This broader coverage includes countries like Ukraine and others beyond the NUTS3 mask, while NUTS mask shows more details about regional administrative boundaries.</p>
German ZIP codes, Kreisschlüssel (Administration Unit), Kreis, Inhabitant per ZIP, City Names, responsible Arbeitsagentur (Social Agency
<p>This dataset from 2019 contains all German ZIP codes, city names associated with it, Kreisschlüssel (Administration Unit ID) Kreis, (Administration Unit), Bundesland (State), Inhabitants, responsible Arbeitsagentur (Social Agency). Note that especially the PLZ ZIP Codes and the responsible Arbeitsagentur change from time to time due to administrative reasons.</p>
Data from: Chronic Rapamycin administration via drinking water mitigates the pathological phenotype in a Krabbe disease mouse model through autophagy activation.
<p>ABSTRACT </p><p>Krabbe disease (KD) is a rare disorder caused by a deficiency of the lysosomal enzyme galactosylceramidase (GALC), resulting in the accumulation of the cytotoxic metabolite psychosine (PSY) in the nervous system. This accumulation triggers demyelination and neurodegeneration. Despite ongoing research, the underlying pathogenic mechanisms remain incompletely understood, and there is currently no cure available.</p><p>Previous studies from our lab revealed the presence of autophagy dysfunctions in KD pathogenesis, as evidenced by the presence of p62-tagged protein aggregates in the brains of KD mice and increased p62 levels in the KD sciatic nerve. We also demonstrated that the autophagy inducer Rapamycin (RAPA) can partially restore the wild-type (WT) phenotype in KD primary cells by reducing the number of p62 aggregates.</p><p>In this study, we tested RAPA in the Twitcher (TWI) mouse, a spontaneous KD mouse model. We administered the drug ad libitum via drinking water (15 mg/L) starting from post-natal day (PND) 21-23. We longitudinally monitored the motor performance of the mice through grip strength and rotarod tests, along with various biochemical parameters related to KD pathogenesis (i.e. autophagy markers expression, myelination, astrogliosis, and PSY accumulation).</p><p>Our findings demonstrate that RAPA significantly enhances motor functions at specific treatment time points and reduces astrogliosis in TWI brain, spinal cord, and sciatic nerves. Using western blot and immunohistochemistry, we observed a decrease in p62 aggregates in TWI nervous tissues, which corroborates our earlier in-vitro results. Furthermore, RAPA treatment partially reduces PSY levels in the spinal cord.</p><p>In conclusion, our results support the consideration of RAPA as a supportive therapy for KD. Importantly, as RAPA is already available in pharmaceutical formulations for clinical use, its potential for KD treatment can be promptly evaluated in clinical trials.</p>
Dose and administration time of indocyanine green in near-infrared fluorescence cholangiography during laparoscopic cholecystectomy (DOTIG) Dataset.
<p><strong><span>Introduction </span></strong></p> <p><span>Different techniques have been described to reduce the incidence of the intraoperative bile duct injury during laparoscopic cholecystectomy (LC), and Near-Infrared Fluorescence Cholangiography (NIFC) with Indocyanine Green (ICG) is one of the latest additions. Currently, there are great disparities in the usage or administration protocols of ICG.</span></p> <p><strong><span>Methods</span></strong></p> <p><span>The aim of this randomised multicenter clinical trial (RCT) is to analyse whether there are differences between the dose and administration ICG intervals to obtain good-quality NIFC during LC. In addition, different factors were analysed that may have an influence on the results of this technique. </span><span>This trial was approved by the local institutional Ethics Committee</span><span>.</span></p> <p><strong><span>Results </span></strong></p> <p><span>From June 2022 to June 2023, 200 patients were randomised in the four arms (G1: </span><span>2.5 mg ICG >3 hours prior to surgery, G2: 2.5 mg ICG 15-30 minutes prior to surgery, G3: 0.05 mg/kg ICG >3 hours prior to surgery and G4: 0.05 mg/kg ICG 15-30 minutes prior to surgery)</span><span>. We found differences in the DISTURBED score between the groups (<em>p</em><0.001), suggesting that ICG administration 15-30 minutes before surgery was worse than administration >3 hours after LC (<em>p</em>=0.02). We also observed that body mass index, gender, ASA Classification System, previous liver and biliary disease and the type of surgery had influence on NIFC. Finally, the NIFC had impact in intraoperative and postoperative complications, operative time and hospital length of stay. </span></p> <p><strong><span>Conclusion </span></strong></p> <p><span>The time of ICG administration was related to NIFC results, as well as different preoperative predictors. NIFC may also influence in surgical outcomes of LC.</span></p> <p><strong><span>Documentation in ZENODO</span></strong></p> <p>Files stored in this repository correspond to the data extracted from the CRDe RedCAP used in this study. The file 'DOTIG_DATA_NoAA_LABELS_2023-10-31' corresponds to the data collected during the study, and the file 'DOTIG_DATA_AA_LABELS_2023-10-31' corresponds to the adverse events recorded during the study. Additionally, there is one more file which is the recoding of adverse events to "MEDDRA Terms (PT) vs 25.1" and "SOC".</p> <p>IBSAL uses the REDCap system for database creation. A detailed description of the information to be captured in the database is documented in the "DOTID_PGD_Data Dictionary Codebook" document. The structure of this data in the CRDe is described in the "DOTIG_PGD_Annotated CRDe document". The Annotated CRDe details the names of input objects in the CRDe with the name, format, and type of variables that will be used during the data entry process. The database must capture all the elements included in the "DOTIG_PGD_Variable List" document.</p> <p>The data management throughout the study is documented in the document DOTIG_PGD_Data Management Report.</p>
UK Administrative Shapefiles clipped to buildings (simplified at 100m)
<p>This dataset includes a series of modified UK administrative boundary shapefiles based on the 2011 census which are intended for use in more accurate visualisation of UK geospatial data analysis. There are two key features of these shapefiles: (1) administrative shapes have been clipped to the Ordnance Survey buildings shapefile, so that in choropleth visualisations relating to demographic data filled spaces represent populated areas of the UK rather than large undifferentiated blocks. (2) Shapefiles have been simplified to reduce loading and processing time, in the case of this repository at 100m. After testing, we have settled on a procedure to render buildings layer visually comprehensible at high zoom levels, by adding a small buffer, dissolving (so that individual overlapping shapes combine into a single more easily visualised shape) and then simplifying at 150m. It is important to emphasise that because of the use of simplification (using a Ramer–Douglas–Peucker algorithm), these shapefiles are not suitable for analysis as boundaries may not be suitably precise or accurate. For users interested in the process used to generate these files you can consult the codebase deposited on <a href="https://github.com/kidwellj/uk_census_shapes_clipped">github</a>.</p> <p>Many thanks to colleagues including Alasdair Rae for recommendations on technique used here. Computations were performed using the University of Birmingham's BEAR Cloud service, which provides flexible resource for intensive computational work to the University's research community. See <a href="http://www.birmingham.ac.uk/bear">http://www.birmingham.ac.uk/bear</a> for more details. Given the massive size of datasets involved (including the district buildings vector shapefile which is 1.4gb and consists of hundreds of thousands of individual shapes), this work would have been impossible without this invaluable resource. I hope that these files will be of use to colleagues who may not have access to similar large computational arrays and make the process of visualising UK boundary and census data more accurate and efficient.</p> <p>Original files are under OGLv3 licenses. Derived data files, where possible are licensed for use under CC BY 4.0.</p> <p>Files include the following:</p> <p><em>Original unmodified data:</em></p> <ul> <li>infuse_ctry_2011.zip - original country level shapes, based on 2011 census, downloaded from https://borders.ukdataservice.ac.uk/ukborders/easy_download</li> <li>infuse_dist_lyr_2011.zip - original local authority shapes, based on 2011 census, downloaded from https://borders.ukdataservice.ac.uk/ukborders/easy_download</li> <li>TermsAndConditions.html - UK Data Service license details (OGLv3), applies to all the above</li> <li> GB_Postcodes.zip - UK postcode district shapes, prepared by Addy Pope, https://datashare.ed.ac.uk/handle/10283/2597</li> </ul> <p>Derived data files:</p> <ul> <li>OS_Open_Zoomstack_district_buildings.zip - buildings layer extracted from <a href="https://www.ordnancesurvey.co.uk/business-government/products/open-zoomstack">Ordnance Survey Zoomstack package</a>, licensed under <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">OGLv3</a> and exported to gpkg format.</li> <li>*_simplified_100m.gpkg - Administrative shapes from above, simplified in R at a resolution of 100 metres.</li> <li>*_simplified_100m_buildings_overlay_simplified.gpkg - Administrative shapes from above, simplified in R at a resolution of 100 metres, and then clipped to the buildings layer.</li> <li>*_simplified_100m_buildings_overlay_simplified.gpkg - Administrative shapes from above, simplified in R at a resolution of 100 metres, and then run against the buildings layer as a difference layer. Suitable for using as an overlay as the shapes are inverse.</li> </ul> <p>Users who wish to use these shapefiles in a reproducible research context may want to download individual files directly from this repository. To do so, you could use the following R code:</p> <pre><code># load packages require(sf) # load simplefeature data class, supercedes sp() and used for st_read # given the size and complexity even of simplified files here, ragg is highly recommended # for users on macos given inefficiencies in default R graphics device require(ragg) # create paths as needed if (dir.exists("data") == FALSE) { dir.create("data") } # download data files only if they aren't already present if (file.exists("data/infuse_dist_lyr_2011.shp") == FALSE) { download.file("https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/infuse_dist_lyr_2011.zip", destfile = "data/infuse_dist_lyr_2011.zip") unzip("infuse_dist_lyr_2011.zip", exdir = "data")} local_authorities <- st_read("data/infuse_dist_lyr_2011.shp")</code></pre> <p> </p>
OpenStreetMap+ Land Use / Land Cover classes and administrative regions of Europe
<p>This dataset contains 23 30m resolution raster data of continental Europe land use / land cover classes extracted from <a href="https://www.openstreetmap.org/">OpenStreetMap</a>, as well as administrative areas, and a harmonized building dataset based on OpenStreetMap and <a href="https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness#:~:text=The%20imperviousness%20products%20capture%20the,over%20long%20periods%20of%20time.">Copernicus HRL Imperviousness</a> data.</p> <p>The land use / land cover classes are:</p> <ol> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:building%3Dcommercial">buildings.commercial</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:building%3Dindustrial">buildings.industrial</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:building%3Dresidential">buildings.residential</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dcemetery">cemetery</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dconstruction">construction.site</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dlandfill">dump.site (landfill)</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dfarmland">farmland</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dfarmyard">farmyard</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dforest">forest</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dgrass">grass</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:building%3Dgreenhouse">greenhouse</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dharbour">harbour</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dmeadow">meadow</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dmilitary">military</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dorchard">orchard</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dquarry">quarry</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:railway%3Drail">railway</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dreservoir">reservoir</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:highway%3Droad">road</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dsalt_pond">salt</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dvineyard">vineyard</a></li> </ol> <p>The land use / land cover data was generated by extracting OSM vector layers from https://download.geofabrik.de/). These were then transformed into a 30 m density raster for each feature type. This was done by first creating a 10 m raster where each pixel intersecting a vector feature was assigned the value 100. These pixels were then aggregated to 10 m resolution by calculating the average of every 9 adjacent pixels. This resulted in a 0—100 density layer for the three feature types. Although the digitized building data from OSM offers the highest level of detail, its coverage across Europe is inconsistent. To supplement the building density raster in regions where crowd-sourced OSM building data was unavailable, we combined it with Copernicus High Resolution Layers (HRL) (obtained from https://land.copernicus.eu/pan-european/ high-resolution-layers), filling the non-mapped areas in OSM with the Impervious Built-up 2018 pixel values, which was averaged to 30 m. The probability values produced by the averaged aggregation were integrated in such a way that values between 0—100 refer to OSM (lowest and highest probabilities equal to 0 and 100 respectively), and the values between 101—200 refer to Copernicus HRL (lowest and highest probability equal to 200 and 101 respectively). This resulted in a raster layer where values closer to 100 are more likely to be buildings than values closer to 0 and 200. Structuring the data in this way allows us to select the higher probability building pixels in both products by the single boolean expression: Pixel > 50 AND pixel <150.</p> <p>This dataset is part of the OpenStreetMap+ was used to pre-process the LUCAS/CORINE land use / land cover samples (https://doi.org/10.5281/zenodo.4740691) used to train machine learning models in Witjes et al., 2022 (https://doi.org/10.21203/rs.3.rs-561383/v4)</p> <p>Each layer can be viewed interactively on the Open Data Science Europe data viewer at <a href="https://maps.opendatascience.eu/?base=OpenStreetMap%20(grayscale)&layer=Copernicus-OSM%20buildings&zoom=4&eye=5000000&center=53.7139,17.0066&opacity=45">maps.opendatascience.eu</a>.</p>
Data: multimodal cell tracking from systemic administration to tumour growth by combining gold nanorods and reporter genes
<p>This data set includes multispectral optoacoustic tomography images supporting an article on cell tracking (preprint: bioRxiv 199836; https://doi.org/10.1101/199836). The corresponding bioluminescence results are included too, as well as the spectra used for the multispectral processing. </p>
Effect of the Increased Nursing Attrition Rate on Nursing Administration Process during the Covid-19 Pandemic in a Selected Tertiary Care Hospital
<p><span>During<span> </span>the<span> </span>COVID-19<span> </span>outbreak,<span> </span>healthcare<span> </span>professionals,<span> </span>particularly<span> </span>nurses,<span> </span>were<span> </span>more<span> </span>prone to<span> </span>diseases.<span> </span>Globally<span> </span>attrition<span> </span>rate<span> </span>was<span> </span>high<span> </span>among<span> </span>nurses<span> </span>and<span> </span>during<span> </span>the<span> </span>pandemic,<span> </span>it<span> </span>increased because<span> </span>of<span> </span>various<span> </span>reasons<span> </span>such<span> </span>as<span> </span>the<span> </span>risk<span> </span>of<span> </span>infection,<span> </span>occupational<span> </span>and<span> </span>psychological<span> </span>stress, causing risk to their loved ones. This led to a chaotic situation where nurse managers were forced to implement specific strategic plans to deal with increased nurse attrition. This study aims<span> </span>to<span> </span>describe<span> </span>the<span> </span>impact<span> </span>of<span> </span>nurse<span> </span>attrition<span> </span>rate<span> </span>on<span> </span>nursing<span> </span>administration<span> </span>during<span> </span>COVID-19 at a selected tertiary care hospital. The research approach adopted in this study is descriptive cross-sectional. A total sample of 66 nurses involved in nursing administration. The data is collected through a structured questionnaire and the nurse attrition data during the COVID-19 pandemic period was collected from the interview method during the survey. Statistical tests used were frequency, percentage, mean, Standard Deviation (S.D). The study showed that there is a moderate impact of increased nurse attrition on nursing administration during the COVID-19 pandemic. The study led to the identification of gaps that need to be addressed in a similar crisis.</span></p>
The administrative topography of Rome. Mapping administrative space and the spatial dynamics of Roman Republicanism
<p>This dataset contains the following figures:</p> <p><em>Table 1, The Radar Chart</em></p> <p><em>Map 1, ROME, 2nd CENTURY BCE</em></p> <p><em>Map 2, ROME, 1st CENTURY BCE</em></p> <p><em>Map 3, ROME, 1st CENTURY ACE</em></p> <p><em>Map 4, ROME, 2nd CENTURY ACE</em></p> <p><em>Map 5, ROME, 3rd CENTURY ACE</em></p>
BG07 - Administrative - Berkovitsa (Bulgaria)
<blockquote><p>Data files for building: BG07 - Administrative - Berkovitsa (Bulgaria)</p></blockquote><p>Languages: Bulgarian, English</p><p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p><p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p><p>The files are organised in the following folders (note that not all files are always provided):</p><ol><li>Main data and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol><p>The files have been redacted to exclude confidential information. </p>
BG06 - Administrative - Etropole (Bulgaria)
<p>Data files for building: BG06 - Administrative - Etropole (Bulgaria)</p><p>Languages: Bulgarian, English</p><p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p><p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p><p>The files are organised in the following folders (note that not all files are always provided):</p><ol><li>Main data and Results, with:<ol><li>Neutral data inventory.</li><li>Neutral results report.</li><li>Original EPC certificate.</li></ol></li><li>Energy Consumption Data, with:<ol><li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li></ol></li><li>Drawings<ol><li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li></ol></li><li>Other Data<ol><li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li></ol></li><li>Dynamic Model<ol><li>Data to run a dynamic model of the building, if available.</li></ol></li></ol><p>The files have been redacted to exclude confidential information. </p>
Experiment data in support of "Segmentation analysis and the recovery of queuing parameters via the Wasserstein distance: a study of administrative data for patients with chronic obstructive pulmonary disease"
<p>This archive contains a ZIP archive, `data.zip`, that itself contains the data used in the final sections of the paper. The remainder of the paper's supporting files are available at <a href="https://github.com/daffidwilde/copd-paper/">github.com/daffidwilde/copd-paper/</a></p> <p>The ZIP archive is structured as follows:</p> <ul> <li>There is a directory, `wasserstein`, for the parameter sweep described in the model construction section of the paper. Its contents are: (i) a file, `main.csv`, describing each parameter and their maximal Wasserstein distance to the observed data, and (ii) three directories, `best`, `median` and `worst`, each containing the simulated queuing results (in `main.csv`) from that sweep with the best, median and worst found parameter sets, respectively (in `params.txt`).</li> <li>The remaining three directories correspond to the experiments conducted in the final section of the paper. Each directory contains two files: (i) `system_times.csv` which holds trial parameters and system time records for every patient to pass through the model in that experiment, and (ii) `utilisations.csv` which holds trial parameters and utilisations for each server in the model for that experiment.</li> </ul>
Designing a Training Journey for Privacy and Information Security Practitioners in the Federal Public Administration
<p> Context : The Ministry of Management and Innovation in Public Services (MGI) leads the formulation and coordination of the Digital Government Strategy (EGD). DEPSI, under the Secretariat of Digital Government (SGD), is responsible for the Privacy and Information Security Program (PPSI), which aims at data privacy, compliance, and institutional resilience. Problem: The culture of privacy and information security in the Federal Public Administration faces development challenges. Despite the PPSI, there is a lack of awareness initiatives, training, a clear strategy, best practices, and performance indicators. Proposed Solution: The proposal aims to develop a training journey for Practitioners working in roles related to privacy and information security, with the goal of identifying and promoting the best practices, skills, and competencies required for these roles. IS Theory: This study aligns with Organizational Information Processing Theory, providing mechanisms to help organizations adapt to regulatory uncertainties in privacy and security. Method: We employed a mixed approach, combining document analysis, a literature review, and a survey. Guidelines and standards were analyzed to map competencies and responsibilities, while the survey gathered practitioners' perceptions of the proposed training journey. Summary of Results: We identified the key profiles and their corresponding responsibilities, and proposed a personalized training journey. Survey results indicated that the journey meets Practitioners’ expectations, being well-evaluated in terms of criteria and assigned weights. Contributions and Impact in IS: This work contributes by presenting a proposal for a Training Journey to assess the knowledge of Federal Public Administration employees and guide them on the best paths for professional development. </p>
Administrative OpenStack Traffic
<p>This is a simple dataset which groups the administrative network traffic volume from OpenStack clouds by Virtual Machine (VM) operations. This dataset considers ten different images of OS for the VMs.</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)
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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.