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1,773 results for “Supplementary data,”
Supplementary Data files of Genus Vigna
<h1><span>Unexpected Unity: Convergent NLR Evolution Across Vigna Species</span></h1> <p>We performed an evolutionary analysis on 13 species of the genus <em>Vigna </em>and discovered evidence of convergent evolution among them.</p> <p> </p>
Supplementary Data and Code for 'Comprehensive Evaluation of MACE on Aluminium Alloys'
<p>This contains a raw dump of the code and data used in the publication of Comprehensive Evaluation of MACE on Aluminium Alloys. As such it is not yet ready for production use. If you wish to reproduce the results it is highly recommended you contact the author who will be very happy to help get you set up. </p> <p> </p> <p>In the main code_data directory there are two subdirectories:</p> <p>data/ </p> <p>Which contains the OQMD-dump data used to supply information on basic bulk structures. See the OQMD.LICENSE file for information on the OQMD </p> <p>evalpot/ </p> <p>Which contains a snapshot of an internal git repository used to produce the results. The installation is not yet ready for a general public and some work may be needed to get it work for your machine. See evalpot/requirements_snapshot.txt for the modules installed via pip at time of code use. (Not all will be needed). </p> <p>You may install the code by running; $pip install . </p> <p>The main results are run by two jupyter notebooks: </p> <p>compute_AllTests.ipynb which runs all the benchmarks</p> <p>and </p> <p> plot_AllTests.ipynb which plots all the results. </p> <p>You will need to adjust some of the file paths in the first few cells to adapt to your machine. </p>
Supplementary Data and Scripts for the Paper "Synchronous Development in Open-Source Projects: A Higher-Level Perspective"
<p>Anonymized data and scripts used to produce the results of the paper "Synchronous Development in Open-Source Projects: A Higher-Level Perspective".</p>
Figure 7 from: Gess F (2012) A new species of Masarina Richards 1962 from southern Africa, description of the female of M. ceres Gess 1997 and supplementary data on three other species of the genus (Hymenoptera, Vespidae, Masarinae). Journal of Hymenoptera Research 25: 83-92. https://doi.org/10.3897/jhr.25.2442
Figure 7 - Masarina ceres, ♀, head, front view (actual width 2.0 mm)
Supplementary IGV data for Human Embryonic RNA Editome
<p>Supplementary IGV data for Human Embryonic RNA Editome</p>
Supplementary data for the paper 'Tool assisted task on touchscreen: a case study on drawing behaviour in chimpanzees (Pan troglodytes)'
<p>Supplementary data for the paper 'Tool assisted task on touchscreen: a case study on drawing behaviour in chimpanzees (Pan troglodytes)'</p> <p>Drawing data (final drawing, spatial and temporal coordinates)</p> <p> </p> <p><strong>Abstract</strong></p> <p>Observations of drawing behaviour in chimpanzees have often focused on the completed drawings. Here, we compared drawing behaviour using fingers or tools on a touch-sensitive monitor between five chimpanzees from the Kumamoto Sanctuary (KS) and two from the Primate Research Institute (PRI), both located at Kyoto University, Japan. Regarding drawing duration, both PRI females drew relatively longer than the other, with the exception of one KS female. However, a long drawing duration did not correspond with a decrease in the number of pauses, which can be interpreted as a lack of concentration or interest. Therefore, to better understand the engagement of individuals, we recorded the time spent looking at the touchscreen. Pan, one of the two PRI females, had the longest drawing periods and spent the most time looking at the screen. We compared her with Ai, the other PRI female, to better understand their individual marking techniques and behaviours. By adapting to each one’s specific behaviour and previous experience with tool-assisted drawing on paper, we offered the females appropriate tools for making marks on the touchscreen. Our results indicate that electronic devices are not limiting in the expression of drawing behaviour. The females did not have the same drawing technique and also showed different types of engagement as motivation, which could not have been detected by only studying the completed drawings. By focusing more on the process rather than on the drawings themselves, we try to show inter-individual differences in drawing behaviour of chimpanzees and the relevance to adapt to it as experimenters.</p>
Supplementary data
<p><strong>SUPPLEMENTARY TABLES</strong>, FIGURES and DATASETS OF ARTICLE, <strong>A comprehensive analysis of mRNA expression profiles of Esophageal Squamous Cell Carcinoma reveals downregulation of Desmoglein 1 and crucial genomic targets</strong></p>
Supplementary data for "Coastal retreat rates of high-Arctic rock cliffs on Brøgger peninsula, Svalbard, accelerate during the past decade"
<p>This upload contains three dGNSS surveys at Kjærsvika, Kongsfjordneset and Stuphallet, representing the coastline of Brøgger Peninsula, Svalbard, including the standard deviations. The measurements were conducted 31.08.2021.</p> <p>In this study, the coastline was manually ditigized based on orthoimages. To analyze the uncertainty of the manual digitization, the process was repeated three more times along selected transects of the coastline. This upload contains a file with the following information:<br>FID: vertices of repeated digitizations, starting from 0 for each separate coastline<br>NEAR_DIST: Distance [m] of each vertex to the digitized coastline that was used in the study<br>Location: Describes the three selected transects which were used for the uncertainty analysis (KV: Kjærsvika, KN: Kongsfjordneset, STU: Stuphallet)<br>ID_digitization: Number of repetition</p>
Supplementary Website, Data, and Scripts for the Paper "Automatic Core-Developer Identification on GitHub: A Validation Study"
<p>Supplementary website containing result plots, pseudonymized input data, resulting pseudonymized classification data, analysis scripts, and Dockerfile used to produce the results of the paper "Automatic Core-Developer Identification on GitHub: A Validation Study".</p> <p>The pseudonymized input data is also availble separately here: https://zenodo.org/record/7775078</p> <p>The resulting pseudonymized classification data is also available separately here: https://zenodo.org/record/7775385</p>
Supplementary Data for GCB-23-0339
<p>Supplementary tables S1 to S30 for <strong>"Integrative population genetics and metagenomics reveals urbanization increases pathogen loads and decreases connectivity in a wild bee" </strong>published in <em>Global Change Biology</em>.</p>
Supplementary Data Chapter 3
<p>Contains supplementary data of chapter 3 of Julian Andres Paganini PhD Thesis</p>
Supplementary data for Bosson et al. 2023 Nature
<p> Dataset and script corresponds to characteristics of glaciers and emerging deglaciated areas until 2100, associated to Bosson et al. (accepted to Nature). See Materials and Methods for more informations.</p> <p>Dataset contains files (.dat) for differents environnemental variables (area, elevation, slope, MAAT, ...) for 19 regions (RGI Code at beginning of the filename) and all glaciers (called RGI20) and for five Shared Socio-economic Pathways (number of ssp at the end of the filename). </p> <p>aggregated.zip contains files for each of the 13 differents Global Circulation Models (GCM, specified in the middle of the filename), while average.zip contains files corredponding to values of the median GCM and to standard deviation of the 13 GCM (files start by STD).</p> <p> </p> <p>The file ecosystems-caused-by-glacial-retreat-script.zip contains :</p> <p>01_aggregatedRGI20_script and 02_averagedRGI20_script and 03_R_overdeepenings_volume_RGI20 were used to calculate paramaters for all glaciers (called RGI20)</p> <p>04_R_Deglaciated Area by year was used to calculate dynamic of deglaciated area for each glacier (from RGI01 to RGI19) and all glaciers (called RGI20)</p> <p>05_realize_fig_png was used to realise every figures and summary tables (files are available in 06_Supplementary_files_final.zip)</p> <p> </p>
Supplementary Data Files - PhD thesis Carmela Federica FARANDA
<p>This dataset represent Supplementary data prepared as a part of PhD thesis. In following lines all files are listed, and brief description is added to each.</p> <p><strong>Chapter 3</strong><br> Supplementary data-<strong>Table S3.1</strong>: NanoSIMS data for Br-doped samples.</p> <p><strong>Chapter 4</strong><br> Supplementary data-<strong>Table S4.1:</strong> Electron microprobe (EPMA) of Br-doped experimental samples.<br> Supplementary data-<strong>Table S4.2:</strong> Mass balance calculation for Br-doped experimental samples.<br> Supplementary data-<strong>Table S4.3:</strong> Electron microprobe (EPMA) of I-doped experimental samples.<br> Supplementary data-<strong>Table S4.4:</strong> Mass balance calculation for I-doped experimental samples. </p> <p><strong>Chapter 6</strong><br> Supplementary data-<strong>Table S6.1:</strong> Electron microprobe (EPMA) of natural sample.<br> Supplementary data-<strong>Table S6.2: </strong>Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) "raw" 79Br data of natural samples before post-processing for interferences.</p>
Supplementary Data for 'Systematic differences in discovery of genetic effects on gene expression and complex traits'
<p>This repository contains data used for or generated by Mostafavi et al.: <a href="https://doi.org/10.1038/s41588-023-01529-1">https://doi.org/10.1038/s41588-023-01529-1</a></p>
Supplementary data for "Resource-efficient photonic quantum computation with high-dimensional cluster states"
<p>Supplementary data for the paper "Resource-efficient photonic quantum computation with high-dimensional cluster states" by Ohad Lib and Yaron Bromberg.</p>
Experimental and Supplementary Data for The Roles of Impact and Inertia in the Failure of a Shoelace Knot
Open the record for dataset details and reuse information.
SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) Supplementary Data
SONEX_Other_Data_1 is the supplementary datasets for the SONEx suborbital campaign. Included in this product are images from the National Lightning Data Network (NLDN) and Optical Transient Detector (OTD).The SASS (Subsonics Assessment) Ozone and NOx Experiment (SONEX) was an international, multi-organizational mission that took place in October-November 1997. NASA was the US sponsor of SONEX that partnered with POLINAT-2 (Pollution from Aircraft Emissions in the North Atlantic Flight Corridor) funded by the German DLR (Deutsches Zentrum für Luft- und Raumfahrt) or German Aerospace Agency. NASA flew the DC-8 aircraft out of NASA/Ames Research Center. DLR operated an instrumented Falcon 20 aircraft. The staging locations for NAFC sampling were primarily Bangor, Maine (US), and Shannon, Ireland. Subsonic aircraft emissions impact several aspects of atmospheric composition: nitrogen oxides (NOx), CO, and hydrocarbons from emissions can perturb upper tropospheric/lower stratospheric (UT/LS) ozone; water vapor, soot, and sulfur oxides (SOx) emitted by aircraft may perturb clouds and aerosols, changing UT/LS radiative forcing and global temperature.In SONEX and POLINAT, flights were conducted in the vicinity of the North Atlantic Flight Coordinator (NAFC) to observe the impact of aircraft emissions on NOx and ozone (O3). The DC-8 aircraft payload (Singh et al., 1999) primarily measured in-situ CO, CO2, hydrocarbons/halocarbons, O3, aerosols (Dibb et al., 2000), OH/HO2, water vapor, nitric acid (Talbot et al., 1999), photolysis rates, temperature, pressure, winds, NOx, and NOy.Three sampling approaches were implemented during SONEX. First, special meteorological (Fuelberg et al., 2000) were developed to allow targeted sampling for air parcels affected by aircraft emissions and various meteorological events, e.g., convection, lightning (Jeker et al., 2000), stratospheric intrusions (Cho et al., 2000). Second, because the NAFC had not been extensively sampled in the past, it was important for SONEX to characterize the climatology of trace species like CN (Wang et al., 2000), NOx and NOy (Koike et al., 2000). Third, tracers (Simpson et al., 2000; Thompson et al., 1999) and model sensitivity studies (Meijer et al., 2000) were employed for Air Mass Identification. This sampling strategy answered the following questions: Where and when are air masses found with the greatest aircraft influence? When and where was stratospheric air sampled? SONEX showed a substantial impact of aircraft emissions on UT/LS NOx and CN in the vicinity of fresh aircraft emissions. However, during October-November 1997 over the NAFC, UT/LS NOx was dominated by surface emissions redistributed by convection and augmented by lightning.
STRAT Supplementary Satellite Data Products
STRAT_Satellite_Data is the supplementary satellite data collected during the Stratospheric Tracers of Atmospheric Transport (STRAT) campaign. Satellite images from the GOES-7 and GOES-9 satellites are featured in this collection. Data collection for this product is complete.The STRAT campaign was a field campaign conducted by NASA from May 1995 to February 1996. The primary goal of STRAT was to collect measurements of the change of long-lived tracers and functions of altitude, latitude, and season. These measurements were taken to aid with determining rates for global-scale transport and future distributions of high-speed civil transport (HSCT) exhaust that was emitted into the lower atmosphere. STRAT had four main objectives: defining the rate of transport of trace gases from the stratosphere and troposphere (i.e., HSCT exhaust emissions), improving the understanding of dynamical coupling rates for transport of trace gases between tropical regions and higher latitudes and lower altitudes (between tropical regions, higher latitudes, and lower altitudes are where most ozone resides), improving understanding of chemistry in the upper troposphere and lower stratosphere, and finally, providing data sets for testing two-dimensional and three-dimensional models used in assessments of impacts from stratospheric aviation. To accomplish these objectives, the STRAT Science Team conducted various surface-based remote sensing and in-situ measurements. NASA flew the ER-2 aircraft along with balloons such as ozonesondes and radiosondes just below the tropopause in the Northern Hemisphere to collect data. Along with the ER-2 and balloons, NASA also utilized satellite imagery, theoretical models, and ground sites. The ER-2 collected data on HOx, NOy, CO2, ozone, water vapor, and temperature. The ER-2 also collected in-situ stratospheric measurements of N2O, CH4, CO, HCL, and NO using the Aircraft Laser Infrared Absorption Spectrometer (ALIAS). Ozonesondes and radiosondes were also deployed to collect data on CO2, NO/NOy, air temperature, pressure, and 3D wind. These balloons also took in-situ measurements of N2O, CFC-11, CH4, CO, HCL, and NO2 using the ALIAS. Ground stations were responsible for taking measurements of O3, ozone mixing ratio, pressure, and temperature. Satellites took infrared images of the atmosphere with the goal of aiding in completing STRAT objectives. Pressure and temperature models were created to help plan the mission.
POLARIS Supplementary Satellite Data Products
POLARIS_Satellite_Data is the supplementary satellite data for the Photochemistry of Ozone Loss in the Arctic Region in Summer (POLARIS) campaign. Data from the Advanced Very High Resolution Radiometer (AVHRR), Geostationary Operational Environmental Satellite 9 (GOES-9), and the Geostationary Meteorological Satellite 5 (GMS-5) are featured in this collection. Data collection for this product is complete.The POLARIS mission was a joint effort of NASA and NOAA that occurred in 1997 and was designed to expand on the photochemical and transport processes that cause the summer polar decreases in the stratospheric ozone. The POLARIS campaign had the overarching goal of better understanding the change of stratospheric ozone levels from very high concentrations in the spring to very low concentrations in the autumn. The NASA ER-2 high-altitude aircraft was the primary platform deployed along with balloons, satellites, and ground-sites. The POLARIS campaign was based in Fairbanks, Alaska with some flights being conducted from California and Hawaii. Flights were conducted between the summer solstice and fall equinox at mid- to high latitudes. The data collected included meteorological variables; long-lived tracers in reference to summertime transport questions; select species with reactive nitrogen (NOy), halogen (Cly), and hydrogen (HOx) reservoirs; and aerosols. More specifically, the ER-2 utilized various techniques/instruments including Laser Absorption, Gas Chromatography, Non-dispersive IR, UV Photometry, Catalysis, and IR Absorption. These techniques/instruments were used to collect data including N2O, CH4, CH3CCl3, CO2, O3, H2O, and NOy. Ground stations were responsible for collecting SO2 and O3, while balloons recorded pressure, temperature, wind speed, and wind directions. Satellites partnered with these platforms collected meteorological data and Lidar imagery. The observations were used to constrain stratospheric computer models to evaluate ozone changes due to chemistry and transport.
SOLVE II Supplementary Satellite Data Products
SOLVE2_Satellite_Data is the supplementary satellite data for the SAGE III Ozone Loss and Validation Experiment II (SOLVE II). Data were collected by the Polar Ozone and Aerosol Measurement III (POAM III) satellite. Data collection for this product is complete.The SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA’s Earth Science Enterprise (ESE). SOLVE’s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.SOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center’s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.
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.