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2,222 results for “coordinates”
Data for the paper "Construction of an invertible mapping to boundary conforming coordinates for arbitrarily shaped toroidal domains."
<p>Data and scripts for the paper "Construction of an invertible mapping to boundary conforming coordinates for arbitrarily shaped toroidal domains."</p> <p>Presented at the "JOINT VARENNA - LAUSANNE INTERNATIONAL WORKSHOP: THEORY OF FUSION PLASMAS, 2024" and published in PPCF.</p>
Data related to: Hippocampal ripples and their coordinated dialogue with the default mode network during recent and remote recollection, Norman et al. (2021)
<p>This dataset contains intra-cranial EEG recordings and analysis code related to the paper: "Hippocampal ripples and their coordinated dialogue with the default mode network during recent and remote recollection" by Norman et al. (https://doi.org/10.1016/j.neuron.2021.06.020)<br> The study investigates the role of hippocampal ripples in the human brain during retrieval of recent and remote autobiographical memories and semantic facts. The intracranial recordings underwent standard preprocessing as described in the paper and were stored in EEGLAB datasets. The analysis code that accompanies the dataset implements the main analyses described in the paper.</p> <p>The dataset includes the following zip files:</p> <ul> <li>iEEG data and main analysis code: <ul> <li>“Norman_et_al_2021_iEEG_data_and_code_1.zip" </li> <li>“Norman_et_al_2021_iEEG_data_and_code_2.zip" </li> </ul> </li> <li>Patients' anatomical data: <ul> <li>“Norman_et_al_2021_Freesurfer.zip”</li> </ul> </li> <li>Additional toolboxes (developed by others): <ul> <li>“MATLAB_toolboxes.zip”</li> </ul> </li> </ul> <p>The code is written primarily in Matlab (version R2018b) and runs on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM. Matlab's Signal Processing Toolbox is required, as well as EEGLAB, Unfold toolbox, and several other open-source toolboxes.</p>
1117 Russian cities with city name, region, geographic coordinates and 2020 population estimate
<p>1117 Russian cities with city name, region, geographic coordinates and 2020 population estimate.</p> <p> </p> <p>How to use</p> <pre>from pathlib import Path import requests import pandas as pd url = ("https://raw.githubusercontent.com/" "epogrebnyak/ru-cities/main/assets/towns.csv") # save file locally p = Path("towns.csv") if not p.exists(): content = requests.get(url).text p.write_text(content, encoding="utf-8") # read as dataframe df = pd.read_csv("towns.csv") print(df.sample(5))</pre> <p> </p> <p>Files:</p> <ul> <li><a href="https://github.com/epogrebnyak/ru-cities/blob/main/assets/towns.csv">towns.csv</a> - city information</li> <li><a href="https://github.com/epogrebnyak/ru-cities/blob/main/assets/regions.csv">regions.csv</a> - list of Russian Federation regions</li> <li><a href="https://github.com/epogrebnyak/ru-cities/blob/main/assets/alt_city_names.json">alt_city_names.json</a> - alternative city names</li> </ul> <p> </p> <p>Сolumns (towns.csv):</p> <p>Basic info:</p> <ul> <li><code>city</code> - city name (several cities have alternative names marked in <code>alt_city_names.json</code>)</li> <li><code>population</code> - city population, thousand people, Rosstat estimate as of 1.1.2020</li> <li><code>lat,lon</code> - city geographic coordinates</li> </ul> <p>Region:</p> <ul> <li><code>region_name</code> - subnational region (oblast, republic, krai or AO)</li> <li><code>region_iso_code</code> - <a href="https://en.wikipedia.org/wiki/ISO_3166-2:RU">ISO 3166 code</a>, eg <code>RU-VLD</code></li> <li><code>federal_district</code>, eg <code>Центральный</code></li> </ul> <p>City codes:</p> <ul> <li><code>okato</code></li> <li><code>oktmo</code></li> <li><code>fias_id</code></li> <li><code>kladr_id</code></li> </ul> <p> </p> <p>Data sources</p> <ul> <li>City list and city population collected from Rosstat publication <a href="https://rosstat.gov.ru/folder/210/document/13206">Регионы России. Основные социально-экономические показатели городов</a> and parsed from publication Microsoft Word files.</li> <li>City list corresponds to <a href="https://ru.wikipedia.org/wiki/%D0%A1%D0%BF%D0%B8%D1%81%D0%BE%D0%BA_%D0%B3%D0%BE%D1%80%D0%BE%D0%B4%D0%BE%D0%B2_%D0%A0%D0%BE%D1%81%D1%81%D0%B8%D0%B8">this Wikipedia article</a>.</li> <li>Alternative dataset is <a href="https://github.com/hflabs/city">wiki-based Dadata city dataset</a> (no population data).</li> </ul> <p> </p> <p>Comments</p> <p> </p> <p>City groups</p> <ul> <li> <p><code>Ханты-Мансийский</code> and <code>Ямало-Ненецкий</code> autonomous regions excluded to avoid duplication as parts of <code>Тюменская область</code>.</p> </li> <li> <p>Several notable towns are classified as administrative part of larger cities (<code>Сестрорецк</code> is a municpality at Saint-Petersburg, <code>Щербинка</code> part of Moscow). They are not and not reported in this dataset.</p> </li> </ul> <p> </p> <p>By individual city</p> <ul> <li><code>Белоозерский</code> not found in Rosstat publication, but <a href="https://github.com/epogrebnyak/ru-cities/issues/5#issuecomment-886179980">should be considered a city as of 1.1.2020</a></li> </ul> <p> </p> <p>Alternative city names</p> <ul> <li> <p>We suppressed letter "ё" <code>city</code> columns in towns.csv - we have <code>Орел</code>, but not <code>Орёл</code>. This affected:</p> <ul> <li><code>Белоозёрский</code></li> <li><code>Королёв</code></li> <li><code>Ликино-Дулёво</code></li> <li><code>Озёры</code></li> <li><code>Щёлково</code></li> <li><code>Орёл</code></li> </ul> </li> <li> <p><code>Дмитриев</code> and <code>Дмитриев-Льговский</code> are the same city.</p> </li> </ul> <p><code>assets/alt_city_names.json</code> contains these names.</p> <p> </p> <p>Tests</p> <pre><code>poetry install poetry run python -m pytest </code></pre> <p> </p> <p>How to replicate dataset</p> <p> </p> <p>1. Base dataset</p> <p>Run:</p> <ul> <li>download data stro rar/get.sh</li> <li>convert <code>Саратовская область.doc</code> to docx</li> <li>run make.py</li> </ul> <p>Creates:</p> <ul> <li><code>_towns.csv</code></li> <li><code>assets/regions.csv</code></li> </ul> <p> </p> <p>2. API calls</p> <p>Note: do not attempt if you do not have to - this runs a while and loads third-party API access.</p> <p>You have the resulting files in repo, so probably does not need to these scripts.</p> <p>Run:</p> <ul> <li><code>cd geocoding</code></li> <li>run coord_dadata.py (needs token)</li> <li>run coord_osm.py</li> </ul> <p>Creates:</p> <ul> <li>coord_dadata.csv</li> <li>coord_osm.csv</li> </ul> <p> </p> <p>3. Merge data</p> <p>Run:</p> <ul> <li>run merge.py</li> </ul> <p>Creates:</p> <ul> <li>assets/towns.csv</li> </ul> <p> </p>
Grood and Suntay joint coordinate system for knee kinematics
<p>Grood and Suntay joint coordinate system for description of knee kinematics</p> <p>This software implements the Grood and Suntay joint coordinate system for the clinical description of the three-dimensional motion of the knee. The Grood and Suntay joint coordinate system is described in the research paper :</p> <p>Grood E.S., Suntay W.J., “A joint coordinate system for the clinical description of three-dimensional motions: application to the knee”, J Biomech Eng., 1983 May; 105(2), 136-44, 1983.</p> <p>The software was used in producing the numerical results shown in various research papers such as :</p> <p>Arsene, CT, Gabrys, B., “Probabilistic finite element predictions of the human lower limb model in the total knee replacement”, Med Eng Phys, 2013 Aug; 35(8): 1116-32, 2013.</p> <p>Description of the Matlab files:</p> <p>Uncerpasl.m – starts the process of calculating the knee joint coordinate system;</p> <p>Anglesn.m – calculates the angles and the displacements of interest for the tibio-femoral component</p> <p>Anglespf.m – calculates the angles and the displacements of interest for the patello-femoral component</p> <p>Readinput.m – reads the text files containing the raw data obtained from the Finie Element simulations such as :</p> <p>a) femur_COG_I_X - means the X coordinate of the I point which defines a coordinate system together with points J and K with respect to the center of gravity (COG) of the femoral component.</p> <p>b) femur_COG_X - means the X coordinate of the center of gravity (COG) of the femoral component which together with points I, J , K forms a sort of local coordinate system attached to the femoral component.</p> <p>All the text files such as femur_COG_I_X_1.txt, femur_COG_I_Y_1.txt, etc were produced with the finite element software PAM-CRASH/PAMOPT from the company ESI, Paris, France (<a href="https://www.esi-group.com/">https://www.esi-group.com</a>). The text files contain a single result consisting of 0.12 seconds of the passive flexion cycle and therefore are of no real use but only to verify that the Grood and Suntay Matlab software code works.</p> <p>Please acknowledge the project financed by the European Union Framework Programme 6 (FP6) entitled Decision Support Software for Orthopaedic Surgery and Mr Corneliu T.C. Arsene if you are going to use this software anywhere in your work. The license for this software is a Creative Commons Attribution 4.0 International License (CCL). This project has also a DOI : 10.5281/zenodo.2136822</p> <p>It is provided here with no warranty. Direct all questions and requests to <a href="mailto:galenpalimpsestproject@gmail.com">galenpalimpsestproject@gmail.com</a>.</p>
Survey for coordinators of agroecology research projects
<p>This dataset (Project_coordinators_survey.csv) contains data related to the survey launched within the Task 1.3 of AE4EU project for the coordinators of agroecology research projects funded by European, transnational, and national programmes identified in the mapping activities. Together with the dataset, the structure of the questionnaire related to this survey is also provided (Project_coordinators_questionnaire_structure.pdf).</p> <p>Answers from respondents were anonymised before the publication. Informed consent was obtained from participants to the survey to use their answers and quotations for research and publication</p>
3D Charged Particles Dataset - Roto-translated Local Coordinate Frames for Interacting Dynamical Systems
<p>This repository contains the "<strong>3D charged particles</strong>" dataset from the paper</p> <blockquote> <p><strong>Roto-translated Local Coordinate Frames for Interacting Dynamical Systems</strong><br> <a href="https://mkofinas.github.io/">Miltiadis Kofinas</a>, <a href="https://menaveenshankar.github.io/">Naveen Shankar Nagaraja</a>, <a href="https://egavves.com/">Efstratios Gavves</a><br> NeurIPS 2021<br> <a href="https://arxiv.org/abs/2110.14961">https://arxiv.org/abs/2110.14961</a><br> <a href="https://github.com/mkofinas/locs">https://github.com/mkofinas/locs</a></p> </blockquote> <p>It contains simulations of trajectories of 5 charged particles in 3 dimensions, interacting via Coulomb forces.</p> <p>There are 30,000 simulations for training, 5,000 for validation, and 5,000 for testing.</p> <p>Train and validation simulations last for 99 timesteps, while test simulations last for 99 timesteps.</p> <p>The features comprise positions and velocities of particles, while edges describe the product of pairwise charges.</p>
Supplemental data for "Computational screening of chemically active metal center in coordinated dipyridyl tetrazine network"
<p>Atomic coordinates of structures used in N. Ud Din, D. Le, T. S. Rahman "Computational screening of chemically active metal center in coordinated dipyridyl tetrazine network", J. Phys.: Condens. Matter .(2023). DOI: 10.1088/1361-648X/acb8f3</p>
Penta-coordinated or -valent: the nature of the chemical bond of some Ti-C-Al compounds
<p>These folders contain the raw date which were used to prepare the above publication.<br> The zipfiles entitles compound-1.zip, compound-2.zip, and compound-3.zip contain the data for the computed compounds of the main publication. Please refer to that for further details. The zip-file xray.zip contains the original cif-file for convenience.<br> </p>
Direct synthesis of a stable radical doped electrically conductive coordination polymer
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements.</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>,<strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_20220202_ULEI_01_ONDICathedral_RT </strong>folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>ONDICatechol– </strong>ONDI with Catechol, <strong>ONDIDMF– </strong>ONDI with DMF, <strong>ONDI_piperidine– </strong>ONDI with piperidine,<strong> ONDICdCl– </strong>ONDI with CdCl, <strong>ONDIDMSO– </strong>ONDI with DMSO.</li> <li>_RT– measured at 300 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> </ul> </li> </ul>
Data_Schönauer et al. (2023)_Root and branch hydraulic functioning and trait coordination across organs in drought-deciduous and evergreen tree species of a subtropical highland forest
<p>Data used in</p> <p>Schönauer, M., Hietz, P., Schuldt, B., and Rewald, B. (2023). Root and branch hydraulic functioning and trait coordination across organs in drought-deciduous and evergreen tree species of a subtropical highland forest. Frontiers in plant science 14, 1127292. doi: 10.3389/fpls.2023.1127292</p>
Tanana River Floodplain Dissolved Organic Nitrogen (DON) Budget, GPS coordinates for Tanana River Floodplain study plots
Ammonium, Nitrate, and Amino Acid concentrations in .5M K2SO4 extracted T0 Tanana Floodplain soils Ammonium, Nitrate, and Amino Acid concentrations in .5M K2SO4 extracted T30 Tanana Floodplain soils Sodium Bicarbonate Extracted Tanana River Floodplain Soil Protein Concentrations, 2001 Plot locations for the Tanana River Floodplain DON buget study. GPS coordinates for the Tanana River Floodplain Dissolved Organic Nitrogen (DON) Budget Study plots GPS coordinates for the Tanana River Floodplain Dissolved Organic Nitrogen (DON) Budget Study plots. Readings were taken from the centre of each 30 X 30 meter plot (1DW3 is 45 X 20). The GPS unit (Garmin etrex Summit) indicated an accuracy of about +/- 5 meters.
Carbon Dynamics Along a Permafrost Gradient at Caribou-Poker Creeks Research Watershed (CPCRW) in Interior Alaska: GPS coordinates for a 75x75m spatial domain along a permafrost and vegetation gradient.
This dataset includes GPS coordinates and elevation data across a 75x75m spatial domain in the Caribou-Poker Creeks Research Watershed. Project summary: Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which forest composition changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. This is an exploratory study to begin understanding SLA variation and controls thereof in a non-contiguous permafrost system.
Fernandez Plots KML shapefile and coordinates
The coordinates and shapefile (kml) of the Fernandez plots near El Verde Field Station Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
MRCE Plots KML shapefile and coordinates
The coordinates and shapefile (kml) of the MRCE Plots Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
EVFS incoming power lines KML shapefile and coordinates
The coordinates and shapefile (kml) of the incoming power lines near El Verde Field Station
Luquillo Forest Dynamics Plot (LFDP) KML shapefile and coordinates
The LFDP shapefile (.kml) and coordinates Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Gap Fire Perimeter (Santa Barbara County, CA), July 9, 2008 - From Geospatial Multi-Agency Coordination Group (GeoMAC)
The Gap Fire burned from 2008-07-01 to 2008-07-28, Lizard's Mouth area of Los Padres National Forest, Santa Barbara County. Approximately 9544 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2008-07-09, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.
Parramore Island of the Virginia Coast Reserve Permanent Plot Baseline Data : Plot Coordinates 1992-1993
This contains coordinates (UTM zone 18N, NAD27) for permanent vegetation plots on Parramore Island. Plots were established using a uniform random number generator to generate random coordinates. One hundred of the random points that fell on the upland portion of Parramore Island (based on the McCaffrey vegetation maps) were selected as plot centers.
Parramore Island of the Virginia Coast Reserve Permanent Plot Coordinates: UTM (NAD83/GRS80) zone 18 2002
Coordinates of Parramore Island Permanent Vegetation Monitoring Plots. Coordinates are for plot center. Projection = UTM zone 18. Units = meters. Datum/Spheroid = NAD83/GRS80. These coordinates should be equivalent to those received by GPS using the WGS84 spheroid, and are in the form most useful for navigating in the field using handheld GPS. GPS locations are only approximate. See notes from the 1996 and 1992-93 surveys for additional descriptiions of the plot coordinates, including triangulation details from local landmarks.
Data Files and Documentation - National Coordination of Data Steward Education in Denmark
<p>This site contains the Data Files and Documentation Files from the National Coordination of Data Steward Education in Denmark Project (2019-2020). The main deliverables from the project are:</p> <p>The Main Report:</p> <p><a href="https://doi.org/10.5282/zenodo.3609516">National Coordination of Data Steward Education in Denmark</a> (<strong>Main Report</strong>)</p> <p>and</p> <p>Wildgaard, Lorna</p> <p><a href="https://doi.org/10.5281/zenodo.3628375">Reframing Data Stewardship educations in Denmark and abroad</a> (<strong>Wildgaard</strong>)</p> <p> </p> <p>List of available Data and Documentation Files below:</p> <p><strong>Wildgaard </strong>and<strong> Main Report (section 2 Review of Data Steward Education)</strong></p> <p><em>1. DS_education_1_review_thematic_analysis.nvp (NVIVO-file) </em></p> <p><em>2. DS_education_2_review_coding_scheme (Excel) </em></p> <p><em>(Created by Wildgaard, Lorna)</em></p> <p><strong>Main Report (section 3 LinkedIn analysis)</strong></p> <p>No data available due to GDPR</p> <p><strong>Main Report (section 4 Job vacancies analysis)</strong></p> <p><em>3. DS_education_3_vacancies_and_method.zip (zip-file) </em></p> <p><em>4. DS_education_4_vacancies_top10_word_frequencies_geographical_location (Excel) </em></p> <p><em>(Created by Vlachos, Evgenios)</em></p> <p>The zip-file contains a text file describing the method used in the analysis (Method) and 119 pdf files with Data Steward vacancies.</p> <p><strong>Main Report (section 5 Questionnaire)</strong></p> <p><em>5. DS_education_5_questionnaire_questions </em></p> <p><em>6. DS_education_6_questionnaire_survey_report </em></p> <p><em>(Created by Vlachos, Evgenios & Knudsen, Christian B.)</em></p> <p><strong>Main Report (section 6 Interviews)</strong></p> <p>7. <em>DS_education_7_interviews_summaries_interview_1-4</em> </p> <p><em>(Created by Hüser, Falco)</em></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.