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7,378 results for “case”
A Google Earth Engine code to analyze e visualize land surface temperature and thermal hot-spot patterns: a Rome (Italy) case study
<p>Link to the <strong>Google Earth Engine </strong>(GEE) code: <strong>https://code.earthengine.google.com/cc3ea6593574e321acd7b68c975a9608</strong></p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, 30 m horizontal resolution, from Landsat-8 remote sensing data, years 2017-2022)</li> <li><strong>The surface thermal hot-spot pattern </strong>(raster data,30 m horizontal resolution) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS tool. </li> </ol> <p>Here attached the .txt file from the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
A Google Earth Engine code to analyze residential buildings' real estate values, summer surface thermal anomaly patterns and urban features: a Florence (Italy) case study
<ol> </ol> <p>The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (<strong>https://doi.org/10.3390/su14148412</strong>).</p> <p>Link to the <strong>Google Earth Engine (GEE) code</strong> <strong>(link: <a href="https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002">https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002</a></strong>)</p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)</li> <li><strong>Surface thermal hot-spot </strong>(raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.</li> <li><strong>Surface albedo</strong> (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)</li> <li><strong>Impervious area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Tree cover</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2018)</li> <li><strong>Grassland area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Water bodies</strong> (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)</li> <li><strong>Sky View Factor</strong> (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)</li> <li><strong>Buildings' units</strong> of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%]. </li> </ol> <p>Here attached the .txt file of the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
An experimental data set for analysis of the thermophysical behavior of a single-story mechanically ventilated double-skin façade (DSF) in fixed boundary conditions corresponding to winter/mid-season and summer cases
<p>Double-skin facades (DSFs) are dynamic and flexible building envelopes that employ a ventilated cavity to either prevent or reduce the solar-induced cooling load or exploit solar energy for passive solar heating. The mechanical ventilation of the cavity offers higher flexibility and control than natural ventilation, as the latter largely depends on stochastic and unpredictable external conditions. Furthermore, when mechanical ventilation rates are combined with the operation of a shading device, the possibilities for controlling the accumulated heat in the cavity of the DSF increase further. Therefore, this experimental campaign systematically investigates how these two important features interact in controlling the cavity's thermal load and airflow conditions. The measurement collected during the experiments constitutes a dataset that contains the results of a series of experimental runs where the different configurations of DSF, in terms of mechanical ventilation rate and venetian blinds, have been subjected to two representative boundary conditions through a climate simulator facility equipped with a solar simulator device. The full-scale DSF mock-up, which includes venetian blinds installed in a 200 mm ventilated cavity, is operated in this experiment in two modes: outdoor air curtain (OAC) and supply air (SA) mode. Tests were carried out under a steady-state regime with different boundary conditions. For the analysis of the utilization of the excess heat accumulated in the cavity and prevention of DSF overheating, boundary conditions corresponding to g-value calculations were selected. For the analysis of air preheating in the DSF cavity, the boundary conditions corresponding to late winter/mid-season weather (cold outdoor air and low-to-moderate solar irradiance) were chosen. The entire set of experimental data collected during the tests is made publicly available to enable the scientific community to access experimental data to further analyze this problem or for model validation purposes. The data set supplements the open-access paper entitled "<strong>Control of heat transfer in single-story mechanically ventilated facades</strong>" (<a href="https://doi.org/10.1016/j.enbuild.2022.112304">https://doi.org/10.1016/j.enbuild.2022.112304</a>), where additional information about the aims of the experiments, the detailed methods, and other data processing procedures can be found. The database is supported by a guide ("Guide.pdf"), where further explanations about how to read data and schematic drawings of the sensor layout are provided. The collection of experimental tests is divided into two files, according to two considered cases:</p> <ul> <li><strong>DSF operating in outdoor air curtain mode </strong>(24 steady-state measurements). The following factors were changed: mechanical ventilation rate (0, 10, 15, 20, 30, 40, 50, and 100 % of maximum fan power) and venetian blind configuration (closed θ=0 º, semi-open θ=45 º, and raised blinds). The outdoor and indoor temperatures, 30 ℃ and 25 ℃, and solar irradiance of 500 Wm<sup>-2</sup> were replicated. [file name: "Summer.csv"],</li> <li><strong>DSF operating in supply air mode</strong> (27 steady-state measurements). The following factors were changed: mechanical ventilation rate (0, 10, 15, 20, 30, 40, 50, 75, and 100 % of maximum fan power) and venetian blind configuration (closed θ=0 º, semi-open θ=45 º, and raised blinds). The outdoor and indoor temperatures, 10 ℃ and 25 ℃, and solar irradiance of 300 Wm<sup>-2</sup> were replicated. [file name: " Winter_MidSeason.csv"]</li> </ul> <p>Any inquiries about the experimental data can be sent to: <a href="mailto:aleksandar.jankovic@ntnu.no">aleksandar.jankovic@ntnu.no</a></p> <p>The activities presented in this paper were carried out within the research project "REsponsive, INtegrated, VENTilated - REINVENT – windows," supported by the Research Council of Norway through the research grant 262198, and the partners SINTEF, Hydro Extruded Solutions, Politecnico di Torino and Aalto University.</p>
A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13: Climate Action
<p>This data set pertains to the following research article: Purnell, P.J. (2022) <em>A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13 – Climate Action</em>. arXiv:2201.02006</p>
Simulation results for ProSSE and datafile for case study
<p>This repository includes the data files for the simulation study and the case study in the publication.</p> <p>The simulation_result.Rdata is an R data file that stores simulated data and results. The data is used to generate Figures 2-7, S1, and S2.</p> <p>The casestudy_trees.Rdata is an R data file that stores the posterior samples of the species tree of the Australian rainbow skinks and the model fitting on these trees. The data file is used to generate Figure 8.</p>
Golden Agents - Occasional Poetry (case study)
<p>Scripts and data accompanying the Semantics2022 paper (TBA).</p>
Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and East Asian ancestries
<p><strong>Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions, and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Cross-tissue analyses indicated that over a third of effector genes most likely act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis, and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.</strong></p> <p><strong>The data submitted here are expression and methylation models with LD reference data for the transcriptome-wide (TWAS), methylome-wide (MWAS) and transcript isoform-wide association study (TIsWAS) as described in the manuscript "Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and East Asian ancestries". Details of the methods are presented in the method section and supplementary information file. </strong></p> <p><strong>TWAS analysis </strong></p> <p>Gene expression models for the six in-house expression datasets were generated using the PredictDB v7 pipeline for a total of 1,077 participants. Elastic net model building with 10-fold cross-validation was performed independently for each dataset. The elastic net models for GTEx v8 Colon Transverse were obtained from the PredictDB data repository (<a href="http://predictdb.org/">http://predictdb.org/</a>) and had been generated using the same pipeline. Models were computed using HapMap2 SNPs ±1Mb from each gene, together with covariate factors estimated using PEER32, clinical covariates when appropriate (age, sex and, where appropriate, case-control status, type of polyp and anatomic location in the colorectum), and three PCs from the individual dataset’s SNP genotype data.</p> <p>Transcript-based TWAS analyses (TIsWAS) were likewise performed by using transcript-level data from the SOCCS, BarcUVa-Seq and GTEx Colon Transverse datasets.</p> <p><strong>MWAS analysis </strong></p> <p>Methylation beta values were calculated based on the manufacturer’s standard, ranging from 0 to 1. Quality control and data normalization were performed in R using the ChAMP software pipeline for the EPIC and 450K arrays. Briefly, we filtered out failed probes with detection P > 0.02 in >5% of samples, probes with <3 reads in >5% of samples per probe and all non-CpG probes. Samples with failed probes >0.1 were also excluded from downstream analyses. We discarded all probes with SNPs within 10bp of the interrogated CpG (from 1,000 Genomes Project, CEU population)34, and probes that ambiguously mapped to multiple locations in the human genome with up to two mismatches33. We only considered probes mapping to autosomes and those overlapping between the EPIC and the 450K arrays. Normalization was achieved using the Beta MIxture Quantile (BMIQ) method. Per probe methylation models were created using the PredictDB pipeline on the normalized methylation matrix and the genotypes as per TWAS eQTL analysis. To optimize power, we restricted our analysis to 263,341-238,443 (for the 450K array) and 377,678 (for the EPIC array) probes annotated to Islands, Shores and Shelves, and discarded “Open Sea” regions. </p>
ClinSpEn-CC (Clinical Cases) Test + Background Set
<p>This repository contains the test and background data for the ClinSpEn-Clinical Cases sub-track. ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content.</p> <p>The data is made up of a TSV file with three columns: document number, line number and English line. The direction of this sub-track is EN>ES. The clinical cases themselves include COVID-19 case reports as well as diverse content extracted from PubMed.</p> <p> </p> <p>Related Links:</p> <p><strong>- Data website with more information: </strong><a href="https://temu.bsc.es/clinspen/">https://temu.bsc.es/clinspen/</a></p> <p><strong>- WMT website (includes schedule, registration, ...): </strong><a href="https://www.statmt.org/wmt22/">https://www.statmt.org/wmt22/</a></p> <p><strong>- CodaLab: </strong><a href="https://codalab.lisn.upsaclay.fr/competitions/6696">https://codalab.lisn.upsaclay.fr/competitions/6696</a></p> <p> </p> <p>ClinSpEn SAMPLE SETS:</p> <p><strong>- ClinSpEn-CC Sample Set (Clinical Cases):</strong> <a href="https://doi.org/10.5281/zenodo.6497350">https://doi.org/10.5281/zenodo.6497350</a></p> <p><strong>- ClinSpEn-CT Sample Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6497372">https://doi.org/10.5281/zenodo.6497372</a></p> <p><strong>- ClinSpEn-OC Sample Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6497388">https://doi.org/10.5281/zenodo.6497388</a></p> <p>ClinSpEn TEST SETS:</p> <p><strong>- ClinSpEn-CC Test Set (Clinical Cases): </strong><a href="https://doi.org/10.5281/zenodo.6948634">https://doi.org/10.5281/zenodo.6948634</a></p> <p><strong>- ClinSpEn-CT Test Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6948669">https://doi.org/10.5281/zenodo.6948669</a></p> <p><strong>- ClinSpEn-OC Test Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6948679">https://doi.org/10.5281/zenodo.6948679</a></p> <p> </p>
Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus
<p>We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. While graph neural networks can efficiently handle the variable structure of hierarchical documents using the permutation invariant message passing operations, we show that we can gain extra performance improvements using our proposed selective graph pooling operation that arises from the fact that some parts of the hierarchy are invariable across different documents. We applied our model to classify clinical trial (CT) protocols into completed and terminated categories. We use bag-of-words based as well as pre-trained transformer-based embeddings to featurize the graph nodes, achieving f1-scores $\simeq 0.85$ on a publicly available large scale CT registry of around 360K protocols. We further demonstrate how the selective pooling can add insights into the CT termination status prediction.</p>
Predicting Patch Correctness Based on the Similarity of Failing Test Cases
<p>Dataset for the paper "Predicting Patch Correctness Based on the Similarity of Failing Test Cases"</p> <p>Tool name: BATS</p>
LPSE data for ray-based CBET test cases
<p>This dataset contains field data from LPSE simulations for the purpose of validating ray-based CBET models. The input parameters required to replicate these results are given in the Physics of Plasmas paper "Validation of ray-based cross-beam energy transfer models." All of the data is stored in HDF5 files. Each file has three data sets: Ez, xAxis, and yAxis (the 1-D datset does not have yAxis).</p> <p>Here is an example of the Matlab code to open and plot one of the 2-D files:</p> <pre><code>filename = 'two_beam_at_caustic.h5'; hInfo = h5info(filename ); data = h5read(filename , '/Ez'); xAxis = h5read(filename , '/x_axis'); yAxis = h5read(filename , '/y_axis'); figure(1); clf; imagesc(yAxis, xAxis, data), colorbar, axis xy </code></pre> <p>Here is an example of the Python code to open and plot one of the 2-D files:</p> <pre><code class="language-python">import h5py import matplotlib.pyplot as plt filename = 'two_beam_at_caustic.h5' f = h5py.File(filename, 'r') Ez = list(f["Ez"]) x_axis = list(f["x_axis"]) y_axis = list(f["y_axis"]) plt.figure() plt.pcolormesh(x_axis,y_axis,Ez) plt.colorbar() plt.show() </code></pre> <p> </p>
INFOGRAPHICS ON COUNTRY CASE STUDIES - ROADMAP H2020 project
<p>We are happy to introduce you the ROADMAP infographics on country case studies!<br> <br> During our 3rd ROADMAP Annual Meeting, members and stakeholders discussed their goals and challenges on every country involved in our project and we used the technique of message houses in order to obtain and gather all the information.<br> <br> Right after, we translated this information into visuals in order to make it easier to convey and analyse. The infographics are ready to download!</p>
Data for: A spatial framework for prioritizing biochar application to arable land: a case study for Sweden
<p>The uploaded data is related to the publication: <em>A spatial framework for prioritizing biochar application to arable land: a case study for Sweden</em>, and contains the following:</p> <p>(I) Raster files for three different biochar prioritization narratives.</p> <p>(II) High-resolution biochar use indication maps (in JPEG) for different prioritization narratives. </p>
Statistical data collected in the case studies of the SPOT report - dataset
<p>Statistical data was collected for fifteen case studies in the context of the SPOT project, funded by the European Commission within the framework programme Horizon 2020.</p>
Results of SPOT surveys for tourists, residents and entrepreneurs in the case studies - dataset
<p>This is a dataset of three surveys conducted within the scope of the SPOT project. The purpose of this dataset is to provide the results of the surveys for tourists, residents and entrepreneurs of the fifteen participating case studies. </p>
Bigger telcos are not necessarily better for infrastructure: A case study of EU versus US markets
<p>This is data and slides for an anticipated forthcoming publication.</p> <p>Telecommunications companies (telcos) provide infrastructure essential to the delivery of digital content. Further, investment in next-generation communication technologies is also seen as critical to overall competitiveness of a market. This dataset results from an examination of the case to be made for European telco consolidation, through comparison with both telcos in the more-concentrated US market, and with other corporations involved in the information or ``eye-ball'' value chain. We find that both profits and growth for EU and US telcos are already comparable before investment in infrastructure, and that in line with standard theory, more value is returned to customers in the form of infrastructure investment in the less-concentrated, EU market. Profits are also in line with other companies in the value chain, with the notable exception of the extremely-concentrated digital ad exchanges segment. </p> <p>The data for the charts was collected from Bloomberg, so we therefore have protected the primary datasheet, available on specific request.</p> <p>No discrepancies with information available from other public sources was identified in respect of the data on revenue. However, companies do not report operating profit (EBIT) and EBITDA systematically in the same manner. We based our calculation on the Bloomberg adjusted EBIT and EBITDA. We thank Benedikt Ströbl for comparing the Bloomberg revenue, EBIT and EBITDA figures with other available sources for all companies in the sample. In particular, the data from Bloomberg was compared to data from Alphaquery and 10-K and annual reports.</p> <p>The below is a non-exhaustive list of the data points for which the Bloomberg adjusted data displayed a delta compared to the data that could be collected from the public sources used for verification, where only some years displayed a delta in the data the year is specified in brackets: (i) in respect of EBIT: Publicis (2018, 2019), NYT (2017) and Axel Springer (2017 and 2019); (ii) in respect of EBITDA (additionally to EBIT list): Verizon, Bertelsmann (2018), Interpublic (2019).</p> <p>Finally, to avoid any confusion in respect of the segment data for Alphabet, the data is presented as retrieved from Bloomberg in full on the tab “Alphabet”, data from the SEC reports used on top of the Bloomberg data to estimate the EBITDA is also reproduced on this tab.</p>
Congruence among multiple indices of habitat preference for species facing human-induced rapid environmental change: A case study using the Brewer's sparrow
<p>Accurate evaluations of habitat preference are key to understanding optimal conditions for wildlife survival and reproduction. Habitat selection, however, usually is evaluated using a single index of preference, and congruence among multiple, relevant indices of preference is examined rarely.</p> <p>We assessed the concordance between patterns of habitat preference using three different indices of breeding site preference in a migratory songbird. Specifically, we compared the chronology of territorial establishment, pair formation, and reproductive initiation of the Brewer's sparrow (<em>Spizella breweri</em>) along a gradient of surface disturbance associated with natural gas development in Wyoming, USA during 2019.</p> <p>We expected all three indices to demonstrate a preference for breeding sites with less surface disturbance, where reproductive success typically is higher. By contrast, all indices suggested suboptimal preference with respect to surface disturbance, with some discrepancy among them. The chronology of settlement and pairing did not vary across the disturbance gradient, whereas nest initiation tended to occur earlier at sites with more disturbance.</p> <p>If the pattern of suboptimal selection of breeding sites that we identified is generalizable across other populations of migratory birds affected by energy development, the resultant lower fitness in those areas may exacerbate population declines.</p> <p>Our results suggest that traditional, single-index approaches to the study of habitat selection, if chosen carefully, may provide adequate inference on habitat preferences. Different metrics, however, can lead to at least subtle differences in patterns of habitat selection. The simultaneous examination of multiple indices of preference across a diversity of systems would help clarify the contexts under which preference metrics can become decoupled.</p>
Unmet Clinical Needs and Case Studies in Blood Testing - Prof Bryant Lin and Dr. Kevin Chang (Stanford University)
<p>This video is the seventh talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Unmet Clinical Needs and Case Studies in Blood Testing - Prof Bryant Lin and Dr. Kevin Chang (Stanford University)</p> <p>Bio: Bryant Lin, MD, MEng is a primary care physician, educator and researcher. The cornerstone of Dr. Lin's work is keeping medicine focused on humans - patients, providers, families and trainees - and not lost in technology and algorithms. His research and educational interests span (1) Developing and testing novel medical technologies, (2) Improving the health of Asian populations with Precision and Population Health, and (3) Increasing expression and interconnections in the Health Community with the Humanities and Arts. After receiving his undergraduate and master's degrees in Electrical Engineering and Computer Science from MIT, he completed his MD and internal Medicine training at Tufts University School of Medicine and Tufts Medical Center. He came to Stanford to serve as a Research Fellow in Cardiac Electrophysiology and Biodesign Fellow where he learned to identify unmet human-centered needs. Since completing his post-graduate training, he stayed at Stanford as clinical faculty in Primary Care and Population Health in the Department of Medicine where he has invented and researched new medical technologies addressing unmet human-centered needs and started the Consultative Medicine Clinic evaluating patients with medical mysteries. He serves as the Training Director for the Joe and Linda Chlapaty DECIDE Center which has created a novel shared decision making tool for atrial fibrillation anticoagulation and is an investigator in several active clinical trials. Three years ago, he co-founded and currently co-directs, with Dr. Latha Palaniappan, the Center for Asian Health Research and Education (CARE) which aims to improve the health of Asians everywhere. Most recently, he has worked closely with the Medicine and the Muse leadership to help start the Stuck@Home concert series, the Stanford SoundWalk and the COVID Remembrance project. Dr. Lin has an active interest in storytelling and film-making. He co-directs an undergraduate seminar, MED 53Q “Storytelling in Medicine”, with Dr. Lauren Edwards and is working with a group of students on a documentary on end-of-life care at a JapaneseAmerican Senior Home in the Bay Area.</p> <p>Kevin Chang MD, MS, is a primary care physician. His focus in on patient care, population health and quality improvement, and medical education. He received his undergraduate degree and master's degree in biomedical engineering from Duke University and Stanford University respectively, and completed his MD at New York University, followed by his medical training at Stanford University. He has since stayed on at Stanford as clinical faculty in Primary Care and Population Health in the Department of Medicine, where he also serves as the co-director of the resident physician Internal Medicine clinic.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/ozk1iJYC1yk</p>
Surgical case mixes and distributions of perioperative surgical process durations for German hospitals
<p>The data set consists of parameters of distributions of perioperative surgical process durations in German hospitals subdivided by the level of care, the surgical specialty, patient type (in- or outpatient), and the procedure’s main OPS code. We consider the following processes: anesthesia induction time, anesthesia emergence time, surgical lead-in, incision-to-closure time, surgical lead-out, and closure-to-incision time (as described in the German Perioperative Procedural Time Glossary). In addition, we provide the number of cases (classified by the procedure’s main OPS code) treated in one year per level of care, surgical specialty, and patient type (in- or outpatient).</p> <p>The supplied data set is the result of processing the 2019 surgical process data set from the Operating Room benchmarking program of German-speaking countries provided by the company digmed GmbH. In total, we considered 2,035,126 recorded surgeries from 212 different hospitals. The data set was created and published to facilitate and promote research on Operating Room planning in the field of Operations Research. It can be used for generating specific problem instances or benchmark sets for Operating Room planning problems.</p> <p> </p> <p> </p>
Fig. 6 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria
Fig. 6. Abundance (mean number of burrows/100 × 5 m transect) of S. citellus in 4 colonies in the study area in summer (for the period 2017–2021) N = Luda Yana; –– l –– = Belotrup; ---- l ---- = Panagyurski kolonii; u = Beli Manastiri
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.