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13,117 results for “Maps”
Other supporting data for our manuscript "Mapping the Single Cell Transcriptomic Response of Murine Diabetic Kidney Disease to Therapies"
<p>Other supplementary data for our paper "Mapping the Single Cell Transcriptomic Response of Murine Diabetic Kidney Disease to Therapies"</p>
Raw Data - Part 2 : Spatial multi-omic map of human myocardial infarction
<p>We provide here the raw data of snATAC-seq and snRNA-seq for the manuscript: Kuppe, Ramirez Flores, Li et al. "Spatial multi-omic map of human myocardial infarction", 2022</p>
Fine-mapping gene-based associations via knockoff analysis of biobank-scale data with applications to UK Biobank
<p>The results of BIGKnock analyses of manuscript ''Fine-mapping gene-based associations via knockoff analysis of biobank-scale data with applications to UK Biobank''</p>
Land use/land cover maps
<p>LULC data were downloaded from the Resource and Environment Data Cloud Platform of China at http://www.resdc.cn/Datalist1.aspx?FieldTyepID=1,3</p>
Dehradun Ward Maps until 2016
<p>This data set is geofiles for Dehradun wards which was developed in 2015-16 when the city comprised of 60 wards. The listed file was used and can be used for academic purposes. The author claim no responsibility for the accuracy of the shapefile and the user is required to do cross-verification and ground truthing for their own purposes acknowledging the contributors. This distribution is CC by 4.0.</p>
Datasets and code for "Mapping the plague through natural language processing"
<p>This project investigates the performance of various NLP libraries and geocoding services for the semi-automated generation of quantitative datasets from narrative texts. We provide the original files, several intermediate data products as well as the final plague datasets. Please note that some of the steps in this process were done manually, thus some of the scripts cannot be run completely. </p> <p>This work is based on two plague treatises:</p> <p>- Sticker, G. 1908 <em>Abhandlungen aus der Seuchengeschichte und Seuchenlehre. Band 1: Die Pest</em>. Giessen, A. Töpelmann.</p> <p>- Biraben, J.-N. 1975 <em>Les hommes et la peste en France et dans les pays européens et méditerranéens</em>. Paris, Mouton.</p> <p>The final geocoded, plague datasets are:</p> <p><strong>- plague_sticker_v1.csv</strong></p> <p><strong>- plague_biraben_v1.csv</strong></p> <p>A data dictionary is available as</p> <p><strong>plague_datadict.xlsx</strong></p> <p>Other files:</p> <table> <tbody> <tr> <td>file name</td> <td>content</td> </tr> <tr> <td>sticker_OCR_orig.txt</td> <td>Original OCR text</td> </tr> <tr> <td>sticker_OCR.txt</td> <td>Original OCR text without parenthesis (author names)</td> </tr> <tr> <td>sticker_textprep.rds</td> <td>Original OCR text with further preparations</td> </tr> <tr> <td>sticker_goldstandard_annotated_1.tsv</td> <td>manual annotations file 1</td> </tr> <tr> <td>sticker_goldstandard_annotated_2.tsv</td> <td>manual annotations file 2</td> </tr> <tr> <td>sticker_goldstandard_annotated_consensus.tsv</td> <td>consenus annotation file</td> </tr> <tr> <td>sticker_standard_toponyms.csv</td> <td>Gold standard for toponym recognition. Contains the tokenization, the start/end character respective to the OCR text (orig and without parenthesis) and whether a token is a location or other</td> </tr> <tr> <td>sticker_comparison_NER.rds</td> <td>Comparison of NER performance</td> </tr> <tr> <td>sticker_comparison_geocoding.rds</td> <td>Comparison of Geocoding performance</td> </tr> </tbody> </table> <p> </p>
Intrinsic vulnerabilty maps for the Telesina Valley (IT), Marchfeld (AT) and Zala County (HU)
<p>The dataset contains the maps of the soil intrinsic vulnerability for three case studies, the Telesina Valley in Italy, the Marchfeld region in Austria and the Zala County in Hungary, simulated using the TFM-ext model (Bancheri et al.,2021). The maps show the intrinsic vulnerability, categorized in 6 classes, from very low to extremely high, according to the estimated mean travel times. The model considered the spatially varying pedoclimatic conditions (i.e., georeferenced hydraulic properties and climate) to evaluate the mean travel times of a generic non-reactive solute till the bottom of the soil profiles. Then, the normalized values were computed and categorized. </p> <p> </p>
Binary (presence/absence) maps of phototrophic biofilms - Otemma floodplain, June 2020 - November 2020
<p>Maps of presence/absence of phototrophic biofilms - Otemma floodplain, June 2020 - November 2020</p> <p>Presence/Absence is binary, where 0 is "not biofilm" and 1 is "biofilm". <strong>Binary maps generated from probability maps using date-by-date logistic models of Roncoroni et al. (2022)</strong>.</p> <p>For further information see Roncoroni et al.(2022), <a href="http://dx.doi.org/10.1080/01431161.2022.2079963">http://dx.doi.org/10.1080/01431161.2022.2079963</a></p> <p>Details:</p> <ul> <li>Format: .ascii </li> <li>Name format: mmdd_Binary (where mm is the month, and dd the day)</li> <li>Coordinate system: CH1903+ LV95 (EPSG:2056)</li> <li>Spatial resolution: 0.05 m</li> </ul>
Backward Population Synthesis: Mapping the Evolutionary History of Gravitational-Wave Progenitors dataset
<p>Dataset release accompanying Backward Population Synthesis: Mapping the Evolutionary History of Gravitational-Wave Progenitors.</p> <p>Note that A22_02_rerun.hdf, A22_5_rerun.hdf, A22_rerun.hdf, KW_rerun.h5 are not directly used in generating the plots in the paper.</p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Benchmark data sets of CDPred as described in</p> <p><strong>Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks</strong></p> <p>Zhiye Guo<sup>1</sup>, Jian Liu<sup>1</sup>, Jeffrey Skolnick<sup>2</sup>, Jianlin Cheng<sup>1*</sup></p> <p><sup>1 </sup>Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211</p> <p><sup>2 </sup>School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332-2000</p> <p>*Corresponding author (chengji@missouri.edu)</p> <p>There is four test dataset in this package, each test dataset contains four different folders and one list file. The <strong>afpred_pdb</strong> includes all the corresponding monomer structures predicted by alphafold. The <strong>cdpred_output </strong>includes the prediction results of our tool CDPred for each dataset. The <strong>pre_gen_a3m </strong>includes the multiple sequence alignments file used by CDPred to generate prediction results. And the <strong>true_pdb </strong>includes the fasta file for the test dataset and its heavy atom distance map (h_dist) and carbon alpha distance map (real_dist) that extract from the native structure.</p> <p>HomoTest1: The homodimer test dataset contains 28 targets collect from CASP_CAPRI 10-13</p> <p>HomoTest2: The homodimer test dataset contains 23 targets collect from CASP_CAPRI 13-14</p> <p>HeteroTest1: The heterodimer test dataset contains 9 targets collect from CASP_CAPRI13-14</p> <p>HeteroTest2: The heterodimer test dataset contains 55 targets collect from PDB bank 09-2021 to 11-2021</p>
Replication package: 40 Years of Designing Code Comprehension Experiments: A Systematic Mapping Study
<p>Replication package | 40 Years of Designing Code Comprehension Experiments: A Systematic Mapping Study</p>
Raw Data - Part 4 : Spatial multi-omic map of human myocardial infarction ---- Raw image
<p>We provide here the raw image for the visium data for the manuscript: Kuppe, Ramirez Flores, Li et al. "Spatial multi-omic map of human myocardial infarction", 2022</p>
Supplementary Material for Use of Journey Maps and Personas in Software Requirements Elicitation
<p>This repository contains supplementary material for the "Use of Journey Maps and Personas in Software Requirements Elicitation" article.</p> <p>Context: Requirements elicitation is a fundamental step in a software development process since it is at this stage that the software begins to be designed. In some situations, the problems related to the failure of the software development project are due to an incomplete requirements elicitation, resulting in solutions that do not understand all the necessary functionalities or do not incorporate innovation. Despite the various techniques offered by Requirements Engineering, situations such as the growing application market and the need for innovation further increase the importance of understanding the user's different needs. Objective: In this paper, we investigated how the journey map and personas techniques are being used in requirements elicitation in both the literature and the industry, along with the advantages, disadvantages, and challenges of using these techniques. Method: We conducted systematic literature review to identify the personas and journey map techniques used in requirements elicitation in the literature and industry. In addition, we conducted a survey with 27 practitioners (software developers, users, and managers) to investigate their perceptions of the use of journeys map and personas techniques in the requirements elicitation phase. Results: Twenty-three primary studies were identified that address journey map and personas techniques in software requirements elicitation. In addition, most respondents stated that using these techniques facilitates understanding the requirements, providing better integration, collaboration, and leveling of knowledge among the members of the software development teams. Conclusions: Our findings allow us to conclude that most of the software developers, users, and managers that participated in the survey consider that the journey map and personas techniques are effective in helping understand the software requirements to be developed by the development teams.</p>
Dataset for image segmentation of tree trunks from depth maps captured with a an Android app using Google ARCore
<p>This dataset consists of pairs of depth maps created with a custom-built Android app using Google's ARCore for depth estimates, and tree trunk segments obtained from depth maps captured by Huawei''s AREngine and processed with a tree diameter estimation algorithm. This dataset was used for a machine learning segmentation task that aimed to improve the inputs from ARCore tree trunk depths to the tree diameter estimation algorithm. For each pair of depth maps and segments an RGB image of the scene where samples were captured is also included.</p>
Liu_et_al_DMG_In Situ Sequencing_expression_maps
<p>This repository includes the In Situ Sequencing datasets (HybISS) generated for Liu et al. 2022. Both gene expression maps (reads.zip) as well as cell type maps (this is, after segmenting individual cells) are included. Malignant cell types were assigned based on probabilistic cell typing by ISS (Qian et al. 2020) and can be found at celltyping_malignant.zip. Healthy cells, in addition to the described malignant ones, were included in celltyping_malignant_and_healthy.zip. In addition annotated data objects (anndata) were generated for all 16 samples (anndata_objects)</p>
Accompanying dataset to ACS Appl. Nano Mater. 2022, 5, 4, 5508–5515 : Nanoscale Mapping of Light Emission in Nanospade-Based InGaAs Quantum Wells Integrated on Si(100): Implications for Dual Light-Emitting Devices
<p>Accompanying dataset containing the raw data and analysis contained in the main publication. This comprises the fitted CL-SEM dataset, the CL-STEM dataset, the CL-SEM presented in the SI and the STEM-EDX linescans.</p>
Datasets for "Versatile Domain Mapping of Scanning Electron Nanobeam Diffraction datasets utilising Variational Auto Encoders and decoder-assisted latent clustering"
<p>20210925_152115_data.hdf5 is the P2 sample raw data.</p> <p>FinalMap-weights.hdf5 is the weights for the P2 model used for clustering</p> <p>SimulatedDSA-data.hdf5 is the simulated data set raw data.</p> <p>FullyTrainedModel.hdf5 is the weights for the Simulated Dataset model used for clustering</p> <p><br> </p>
LiDAR-IMU Mapping Test Dataset
<p>This dataset contains two rosbag files recorded in an outdoor environment for testing LiDAR-IMU mapping systems.</p>
Disaggregated soil map of the Designation of Origin Campo de Borja
<p>Disaggregated soil map of the Designation of Origin Campo de Borja in GeoPackage format. It contains a single-polygon vector layer and a table of the map legend.</p>
Three-dimensional mapping of carbon, nitrogen, and phosphorus in soil microbial biomass and their stoichiometry at the global scale
<p>R code, raw datasets, and predicted global maps of soil microbial biomass C, N, and P and their stoichiometric ratios at 0-30 cm depth.</p> <p>When using any of these layers, please cite: Gao et al., Three-dimensional mapping of carbon, nitrogen, and phosphorus in soil microbial biomass and their stoichiometry at the global scale (2022). Global Change Biology. DOI: 10.1111/gcb.16374</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.