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230 results for “Data Aggregation”
Aggregated 30-minute soil water and temperature data from 9 NPP shrub sites at Jornada Basin LTER, 2013 - 2024 (for Pinos et al 2025 manuscript)
This is an aggregated dataset of 30-minute soil temperature and moisture data from 9 shrub-dominated NPP study sites at the Jornada Basin LTER site in southern New Mexico, U.S.A. Collection of soil volumetric water content data at all of the 15 Jornada LTER NPP sites, New Mexico, supports the environmental monitoring objectives of the Jornada LTER monitoring program that look at plant-soil water dynamics. Volumetric water content and soil temperature are measured every 30 minutes at an automated meteorological station installed at all 15 of the Jornada LTER program’s NPP sites. This dataset aggregates only the stations located at the 9 sites with shrub-dominated vegetation cover (creosotebush, tarbush, and mesquite dune sites). Measurements are made every 30 minutes at 10 cm, 20 cm, and 30 cm soil depths. This dataset is in support of the Pinos et al. 2025 manuscript.
Aggregated 30-minute meteorology data from 9 NPP shrub sites at Jornada Basin LTER, 2013 - 2024 (for Pinos et al 2025 manuscript)
This is an aggregated dataset of 30-minute summary meteorology data from 9 shrub-dominated NPP study sites at the Jornada Basin LTER site in southern New Mexico, U.S.A. Average air temperature, relative humidity, total precipitation, wind speed, wind direction, and solar radiation are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at all 15 of the Jornada LTER program’s NPP sites. This dataset aggregates only the stations located at the 9 sites with shrub-dominated vegetation cover (creosote, tarbush, and mesquite sites). Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. Solar radiation is measured at 3m. This dataset is in support of the Pinos et al. 2025 manuscript.
Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Long-term data (2000-2022)
<p>This data is part of the <em>Monthly aggregated Water Vapor MODIS MCD19A2 (1 km)</em> dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.</p><p><strong>General Description</strong></p><p>The monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:</p><ul><li><strong>Monthly time-series:</strong><br>Derived from <em>MCD19A2 v061</em>, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li><strong>Yearly time-series:</strong><br>Derived from <em>monthly time-series</em>, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li><strong>Long-term data (2000-2022):</strong><br>Derived from <em>monthly time-series</em>, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul><p><strong>Data Details</strong></p><ul><li><strong>Time period:</strong> 2000–2022</li><li><strong>Type of data:</strong> Water vapor column above the ground (0.001cm)</li><li><strong>How the data was collected or derived:</strong> Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li><strong>Statistical methods used:</strong> Four statistics were derived: standard deviation, percentiles 25, 50, and 75.</li><li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li><li><strong>Coordinate reference system:</strong> EPSG:4326</li><li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.00081, 179.99994, 87.37000)</li><li><strong>Spatial resolution:</strong> 1/120 d.d. = 0.008333333 (1km)</li><li><strong>Image size:</strong> 43,200 x 17,924</li><li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li></ul><p><strong>Support</strong></p><p>If you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:</p><ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul><p><strong>Name convention</strong></p><p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p><ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20000101 = 2000-01-01</li><li>Time reference end time: 20221231 = 2022-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>
COVID-19 Mobility Data Aggregator
<p><strong>Description</strong></p> <p>This repository includes:<br> 1) Data scraper of Google, Apple and Waze Mobility data<br> 2) Preprocessed mobility reports in different formats<br> 3) Merged mobility reports in summary files</p> <p><strong>About data</strong></p> <p>About <a href="https://www.google.com/covid19/mobility/">Google COVID-19 Community Mobility Reports</a></p> <p>About <a href="https://www.apple.com/covid19/mobility">Apple COVID-19 Mobility Trends Reports</a></p> <p>About <a href="https://www.waze.com/covid19">Waze COVID-19 local driving trends</a></p> <p><strong>Description of data files</strong></p> <p><em><strong>Google reports (located in google_reports directory):</strong></em></p> <p>The raw report in ZIP format: Global_Mobility_Report.zip<br> Data for the worldwide (only 1st level of subregions): mobility_report_countries (CSV and Excel formats available)<br> Data for Brazil: mobility_report_brazil (CSV and Excel formats available)<br> Data for Europe: mobility_report_europe (CSV and Excel formats available)<br> Data for Asia + Africa: mobility_report_asia_africa (CSV and Excel formats available)<br> Data for North and South America + Oceania (Brazil and US excluded): mobility_report_america_oceania (CSV and Excel formats available)</p> <p><em><strong>Apple reports (located in apple_reports directory):</strong></em></p> <p>Raw report: applemobilitytrends.csv<br> Data for the worldwide: apple_mobility_report (Google Sheets, CSV and Excel formats available)<br> Data for the US: apple_mobility_report_US (CSV and Excel formats available)</p> <p><em><strong>Waze reports (located in waze_reports directory):</strong></em></p> <p>Raw CSV files: Waze_Country-Level_Data.csv, Waze_City-Level_Data.csv<br> Preprocessed report: waze_mobility (Google Sheets, CSV and Excel formats available)</p> <p><em><strong>Summary reports (located in summary_reports directory)</strong></em></p> <p>These are merged Apple and Google reports.</p> <p>Report by regions: summary_report_regions (CSV and Excel formats available)<br> Report by countries: summary_report_countries (Google Sheets, CSV and Excel formats available)<br> Report for the US: summary_report_US (CSV and Excel formats available)</p> <p><strong>License</strong></p> <p>See LICENSE.txt</p> <p><strong>Credits</strong></p> <p>If you use this dataset, please also cite the original data sources:</p> <p>1. Google LLC <em>"Google COVID-19 Community Mobility Reports"</em>. https://www.google.com/covid19/mobility/ Accessed: <date></p> <p>2. Apple Inc. "<em>Apple COVID-19 Mobility Trends Reports"</em>. https://www.apple.com/covid19/mobility Accessed: <date></p> <p>3. Waze Ltd "<em>Waze COVID-19 Impact Dashboard". </em>https://www.waze.com/covid19 Accessed: <date></p>
Supplementary material (aggregated data set): Egeler, G.-A. & Baur, P. (2020). Menüwahl in der Hochschulmensa: Fleisch oder Vegi? Ergebnisse eines 12-wöchigen Feldexperiments (NOVANIMAL Working Paper No. 5). ZHAW. https://doi.org/10.21256/zhaw-1405
<p><strong>Meal choice at two university canteens in a field experiment during 12 weeks: aggregated menu sales data</strong></p> <p>How do canteen visitors respond to a revised offer of meat-based and plant-based meals? Selected innovations were simultaneously implemented and tested in a trans­disciplinary field experiment in two university canteens over a 12-week period in the autumn semester 2017. Throughout this time, the meat dishes and ‘veg-meals’ (ovo-lacto-vegetarian and vegan meals) were randomly distributed among the three menu lines, the veg-meals were not marketed and advertised as such and the previous vegetarian menu line was abolished. Weeks where the usual number of meat dishes were on offer (the ‘base weeks’) alternated with weeks where the share of veg-meals was increased (the ‘intervention weeks’). <br> The field experiment did not have a negative impact on the number of meals sold or the turnover compared to the two previous years. Women choose meat dishes less often than men. This connection applies in the base weeks and intervention weeks, in all age groups, among both students and among staff. Remarkably, the share of (non-labelled) vegan dishes is comparable for women and men over all age groups, independent of university affiliation (student, staff). Authentic vegan dishes were particularly welcome. Veg-meals could also be sold on the more expensive menu line. There was a better correlation between meal choice, eating habits and attitudes (health, environment, animal welfare, social aspects) than expected. <br> One quarter of canteen visitors show ‘veg-oriented’ eating habits and three quarters thereof ‘meat-oriented’ eating habits. Only a minority of potential visitors eat regularly at the canteen, and those who do exhibit meat-oriented eating habits more often. We conclude, therefore, that the canteen’s usual menu offer is primarily aimed at visitors with meat-oriented eating habits at lunchtime. The most typical visitors to the canteen are male students who select meat dishes.<br> It has been shown, therefore, that the simultaneous changes in supply have worked. Veg-meals are preferred, particularly by women and those prone to flexitarian eating habits; however, also the canteen visitors with meat-oriented eating habits chose veg-meals during the intervention weeks. Catering in canteens has the great potential to expand the range of veg-meals at the expense of meat dishes, provided that the culinary quality is of a high enough standard and meals are not offered as vegetarian or vegan. The question arises as to whether canteens are not missing an economic opportunity if they only offer traditional meat dishes? Canteens are perfectly suited as real-world laboratories in which innovations for sustainable catering can be tried out. The field experiment in the two university canteens is a start; further experiments are needed.</p> <p><strong>The data set contains more than <em>26'000</em> aggregated menu sales. The analyses and results are summarized in the working paper No. 5 <a href="https://doi.org/10.21256/zhaw-1405">https://doi.org/10.21256/zhaw-1405</a></strong></p> <p>The corresponding scripts are: </p> <p>- <a href="http://doi.org/10.5281/zenodo.4034686">10.5281/zenodo.4034686</a></p> <p>- <a href="http://doi.org/10.5281/zenodo.4034698">10.5281/zenodo.4034698</a></p> <p>- <a href="http://doi.org/10.5281/zenodo.4244258">10.5281/zenodo.4244258</a> (newer Version)</p> <p>For more information visit the <a href="http://novanimal.ch">novanimal.ch</a> website.</p>
Open-data release of aggregated Australian school-level information. Edition 2016.1
<p>The file set is a freely downloadable aggregation of information about Australian schools. The individual files represent a series of tables which, when considered together, form a relational database. The records cover the years 2008-2014 and include information on approximately 9500 primary and secondary school main-campuses and around 500 subcampuses. The records all relate to school-level data; no data about individuals is included. All the information has previously been published and is publicly available but it has not previously been released as a documented, useful aggregation. The information includes:<br /> (a) the names of schools<br /> (b) staffing levels, including full-time and part-time teaching and non-teaching staff<br /> (c) student enrolments, including the number of boys and girls<br /> (d) school financial information, including Commonwealth government, state government, and private funding<br /> (e) test data, potentially for school years 3, 5, 7 and 9, relating to an Australian national testing programme know by the trademark 'NAPLAN'<br /> <br /> Documentation of this Edition 2016.1 is incomplete but the organization of the data should be readily understandable to most people. If you are a researcher, the simplest way to study the data is to make use of the SQLite3 database called 'school-data-2016-1.db'. If you are unsure how to use an SQLite database, ask a guru.<br /> <br /> The database was constructed directly from the other included files by running the following command at a command-line prompt:<br /> <em>sqlite3 school-data-2016-1.db < school-data-2016-1.sql</em><br /> Note that a few, non-consequential, errors will be reported if you run this command yourself. The reason for the errors is that the SQLite database is created by importing a series of '.csv' files. Each of the .csv files contains a header line with the names of the variable relevant to each column. The information is useful for many statistical packages but it is not what SQLite expects, so it complains about the header. Despite the complaint, the database will be created correctly.<br /> <br /> Briefly, the data are organized as follows.<br /> (a) The .csv files ('comma separated values') do not actually use a comma as the field delimiter. Instead, the vertical bar character '|' (ASCII Octal 174 Decimal 124 Hex 7C) is used. If you read the .csv files using Microsoft Excel, Open Office, or Libre Office, you will need to set the field-separator to be '|'. Check your software documentation to understand how to do this.<br /> (b) Each school-related record is indexed by an identifer called 'ageid'. The ageid uniquely identifies each school and consequently serves as the appropriate variable for JOIN-ing records in different data files. For example, the first school-related record after the header line in file 'students-headed-bar.csv' shows the ageid of the school as 40000. The relevant school name can be found by looking in the file 'ageidtoname-headed-bar.csv' to discover that the the ageid of 40000 corresponds to a school called 'Corpus Christi Catholic School'.<br /> (3) In addition to the variable 'ageid' each record is also identified by one or two 'year' variables. The most important purpose of a year identifier will be to indicate the year that is relevant to the record. For example, if one turn again to file 'students-headed-bar.csv', one sees that the first seven school-related records after the header line all relate to the school Corpus Christi Catholic School with ageid of 40000. The variable that identifies the important differences between these seven records is the variable 'studentyear'. 'studentyear' shows the year to which the student data refer. One can see, for example, that in 2008, there were a total of 410 students enrolled, of whom 185 were girls and 225 were boys (look at the variable names in the header line).<br /> (4) The variables relating to years are given different names in each of the different files ('studentsyear' in the file 'students-headed-bar.csv', 'financesummaryyear' in the file 'financesummary-headed-bar.csv'). Despite the different names, the year variables provide the second-level means for joining information acrosss files. For example, if you wanted to relate the enrolments at a school in each year to its financial state, you might wish to JOIN records using 'ageid' in the two files and, secondarily, matching 'studentsyear' with 'financialsummaryyear'.<br /> (5) The manipulation of the data is most readily done using the SQL language with the SQLite database but it can also be done in a variety of statistical packages.<br /> (6) It is our intention for Edition 2016-2 to create large 'flat' files suitable for use by non-researchers who want to view the data with spreadsheet software. The disadvantage of such 'flat' files is that they contain vast amounts of redundant information and might not display the data in the form that the user most wants it.<br /> (7) Geocoding of the schools is not available in this edition.<br /> (8) Some files, such as 'sector-headed-bar.csv' are not used in the creation of the database but are provided as a convenience for researchers who might wish to recode some of the data to remove redundancy.<br /> (9) A detailed example of a suitable SQLite query can be found in the file 'school-data-sqlite-example.sql'. The same query, used in the context of analyses done with the excellent, freely available R statistical package (http://www.r-project.org) can be seen in the file 'school-data-with-sqlite.R'.</p>
Data from: Fluid flow and amyloid transport and aggregation in the Brain Interstitial Space
<p>This data accompanies the paper entitled <strong>Fluid flow and amyloid transport and aggregation in the Brain Interstitial Space.</strong></p> <p> </p> <p>The zip archive contains the results of Lattice Boltzmann Molecular Dynamics simulations of the systems investigated and presented in the manuscript. Computational Fluid Dynamics data are in VTK format. Molecular Dynamics trajectories are in XYZ format.</p>
Raw and aggregated data for the study introduced in the paper "The way we cite: common metadata used across disciplines for defining bibliographic references"
<p>These data have been gathered in the context of a study aiming to investigate citation practices for referencing different types of entities and, in particular, for understanding the most used metadata in bibliographic references. The data are stored in two documents in XLSX format:</p> <ul> <li>file "links-intext-pointers-and-cited-entity-types.xlsx" - it contains information about whether the in-text reference pointers of the various PDF articles of the corpus have specified hypertextual links from the in-text reference pointers to the denoted bibliographic reference, plus information about the types of all the entities cited by each article in the corpus;</li> <li>file "metadata-bibliographic-references.xlsm" - it contains information about the metadata used to identify the various descriptive elements of all the bibliographic references defined in the article of the corpus.</li> </ul> <p>The methodology used to gather all these data is described in:</p> <blockquote> <p>Santos, E. A. d., Peroni, S., Mucheroni, M. L.: Workflow for retrieving all the data of the analysis introduced in the article "Citing and referencing habits in Medicine and Social Sciences journals in 2019". (2020), <a href="https://doi.org/10.17504/protocols.io.bbifikbn">https://doi.org/10.17504/protocols.io.bbifikbn</a></p> </blockquote>
Data to support the publication "Impact of agricultural management on soil aggregates and associated organic carbon fractions: Analysis of long-term experiments in Europe"
<p><strong>Raw data:</strong> Experimental plot ids and information, mass distribution of all aggregate fractions after wet sieving, Sand content of each fraction to conduct the sand correction, mass distribution of all fractions after isolating the micro-aggregates held within the macroaggregates, yields per treatment, carbon content per fraction (raw data)</p> <p><strong>All data per plot: </strong>SOC content, MAOM and POM content of each fraction presented in the fractionation scheme included in the manuscript, together with the mass of the relative fractions. </p> <p> </p>
Research data for: Preventing the coffee-ring effect and aggregate sedimentation by in situ gelation of monodisperse materials
<p>Raw data for the publication: Preventing the coffee-ring effect and aggregate sedimentation by in situ gelation of monodisperse materials</p>
Daily weather data averages for Germany aggregated over official weather stations
<p>Daily data averaged across Germany for the period of 2016-01-01 till 2021-06-27:</p> <p><strong>temperature_mean: </strong>mean daily temperature in degree Celsius averaged across all weather stations in Germany.</p> <p><strong>temperature_max:</strong> maximum daily temperature in degree Celsius averaged across all weather stations in Germany.</p> <p><strong>precipitation:</strong> daily precipitation sum in millimeter (equals liter per square meter) averaged across all weather stations in Germany.</p> <p><strong>sunshine: </strong>sunshine duration per day averaged across all weather stations in Germany.</p> <p> </p> <p> </p> <p>gemittelte Werte basierende auf Daten des Deutschen Wetterdiensts, Vermessungsverwaltungen der Länder und BKG (https://gdz.bkg.bund.de/)</p>
Aggregated data of abundance indices
<p>For the reconstruction of flight peaks, an abundance index was calculated by relating records to search efforts based on the evidence of field activities left in the database by proficient observers. For this search-effort correction, we selected observers with at least 50 records of at least 10 butterfly species each year. As a measure of their collective search effort, we calculated the sum for each day of all the 100 × 100 m grid cells from which an observer had reported records (of any species, including non-butterflies; also including absence records). This method of ‘proven day-grid-visits’ has become the standard proxy for search effort when analyzing incidental observations of the portal waarnemingen.be. Day-grid-visits do not cover search effort completely, because records are not submitted from every visited hectare grid cell, but strongly correlates with it.</p> <p>We provide the raw data containing day (2009-2020), the X and Y coordinate of the centroid of the 100x100 m grid cell (In Lambert 72, EPSG:<em>31370</em> Projected coordinate system for Belgium), the number of peacock butterflies reported, and the number of hectare day grid visits. </p>
Raw and aggregated data for the study introduced in the article "An analysis of citing and referencing habits across all scholarly disciplines: approaches and trends in bibliographic metadata errors"
<p>This dataset contains all the raw data and aggregated data subject of the study introduced in the article "An analysis of citing and referencing habits across all scholarly disciplines: approaches and trends in bibliographic metadata errors". The study is based on the bibliographic and citation data contained in 729 articles published in 147 journals in 27 subject areas. The articles contained a total amount of 34,140 bibliographic references and 55,100 mentions and quotations overall.</p> <p>The dataset is composed of a series of files:</p> <ul> <li>the files "subject_area_<discipline-name>.csv" contain the raw data of the articles published in the journals of all the disciplines considered in the study;</li> <li>the file "article_data_summary.csv" contains the aggregated data created considering the raw data in the previous files, which have been used to creating all the tables and figures in the article;</li> <li>the file "starred_metadata_set.csv" contains information about the most used subset of bibliographic metadata;</li> <li>the file "journals_selection.csv" contains information about all the journals selected for the study.</li> </ul>
Parramore Island of the Virginia Coast Reserve Permanent Plot Resurvey: Landcover Class Aggregation data 1996
First 3-5 year resurvey of permanent monitoring plots using essentially the same protocol as the intial survey of 1992-1993 except that: 1.) standing biomass of the herbaceous groundcover was added (including for new lower salt marsh plots) using clip plots at the subplot locations; and 2.) an estimate of landcover/habitat class aggregation was conducted surrounding each plot center out to 60m in the four cardinal directions. Extends baseline data useful for estimating landscape-scale vegetative productivity, mortality, and turnover; for establishing pre-disturbance conditions in the case of later stand- or island-wide disturbance; and for assisting in the ground-truthing of landcover and habitat classification using aerial or satellite remote-sensing imagry.
Aggregated Eco Province data
<p>Aggregated Eco Province (AEP) data for each AEP between complexity 1 to 115.</p> <p>To accompany Sonnewald et al. "Elucidating Ecological Complexity: Unsupervised Learning determines global marine eco-provinces".</p> <p>NOTE: A complexity >12 is recommended.</p>
Data from: Intense upper ocean mixing due to large aggregations of spawning fish
<p>This dataset includes data collected during the cruise REMEDIOS-TL in the Ría de Pontevedra (NW Iberia) at station P2 (42.357°N, 8.773°W) from 29 June to 18 July 2018 onboard of the Research Vessel Ramón Margalef belonging to the Spanish Institude of Oceanography. The REMEDIOS project is funded by the Spanish Ministry of Economy and Inno-445vation under the research project REMEDIOS (CTM2016-75451-C2-1-R) and leaded by Beatriz Mouriño Carballido.</p> <p>The archived data are described in a manuscript entitled "Intense upper ocean mixing due to large aggregations of spawning fish" by Fernández Castro et al. published in Nature Geoscience:</p> <p>Fernández Castro, B., Peña, M., Nogueira, E. <em>et al.</em> Intense upper ocean mixing due to large aggregations of spawning fish. <em>Nat. Geosci.</em> <strong>15, </strong>287–292 (2022). https://doi.org/10.1038/s41561-022-00916-3</p> <p>The manuscript presents evidence that night-time aggregations of anchovies produce intense ocean turbulence and mixing. All the data needed to support the conclusions of the article are included in this dataset.</p> <p>The dataset includes:</p> <p>- Microstructure profiles collected with a MSS Sea&Sun profiler during the three intensive samplings of the cruise (I01, I02, I03)</p> <p>- Ocean currents measured with a bottom moored RD Instruments acoustic Doppler profiler (ADCP, 300Khz) for the duration of the cruise</p> <p>- Acoustic backscatter from a ship-borne echosounder Simrad EK80 for the frequencies 18, 38, 70, 120 and 200 KHz and the three intensive samplings of the cruise (I01, I02, I03)</p> <p>- European anchovy (Engraulis encrasicolus) egg counts from plankton hauls samplings.</p>
MRI data for "Stress-inducible phosphoprotein 1 (HOP/STI1/STIP1) regulates the spreading, aggregation, and toxicity of α-synuclein in vivo"
<p>Repository for magnetic resonance imaging data from the project titled "Stress-inducible phosphoprotein 1 (HOP/STI1/STIP1) regulates the spreading, aggregation, and toxicity of α-synuclein in vivo"</p> <p>Contains the pre-processed ex vivo T1-weighted images (Bruker 7T; 70 micron isotropic voxel resolution) for WT mice, M83 homozygous, and M83 homozygous mice with one copy of the TPR transgene. Full subject list can be viewed with the prado_MRI_M83dTPR_subjectlist_final.csv file.</p> <p>Whole brain region segmentations are available for these mice. These labels were generated using the MAGeT-Brain segementation pipleine (https://github.com/CobraLab/MAGeTbrain) and using a modified/merged version of the Allen brain atlas. The full list of regions included can be found in the file named Allen_brain_mapping_final.csv.</p> <p>See readme.txt file for more information</p>
Reference map illustrating the major Philippine faunal regions as defined by the Pleistocene Aggregate Island Complexes (PAICs). Selected island groups, such as the Babuyans, Batanes, the Romblon Island Group (RIG), and the Sulu Archipelago, are also indicated. in Synopsis of the Snakes of the Philippines A Synthesis of Data from Biodiversity Repositories, Field Studies, and the Literature
Reference map illustrating the major Philippine faunal regions as defined by the Pleistocene Aggregate Island Complexes (PAICs). Selected island groups, such as the Babuyans, Batanes, the Romblon Island Group (RIG), and the Sulu Archipelago, are also indicated.
Aggregated frequencies of transcription initiations observed in FANTOM5 CAGE data on GRCh38, including alignments with low mapping qualities
<p><strong>Overview</strong></p> <p>Aligned reads of the FANTOM5 CAGE data have been used after filtering (ones with mapping quality less than 20 or percent identity less than 85% were discarded) for general purpose, resulting in the data set consisting of only the reads aligned with confidence. The filtering process made possible to interpret the data without ambiguity, however it also limited interpretation of paralogous or duplicated regions within the genome. Here all of the 5'-ends of the CAGE read alignments, including the ones with low mapping quality, were counted. The counts in the individual profiles were aggregated and summed up. </p> <p> </p> <p><strong>Special usage note</strong></p> <p>As noted above, this data derived from the alignments with low mapping qualities, as well as the ones with high mapping qualities. The result has to be examined very carefully: observations on the genome does not support transcription initiation with confidence, and even absence of such observation does not support silence of transcription with confidence. For example, file size on the forward strand is substantially larger than the one on the reverse strand, which is likely caused by an arbitrary preference of the alignment process. It does not mean transcription happens more frequently on the forward strand. Interpretation has to be made always in comparison with the standard data (BED files under http://fantom.gsc.riken.jp/5/datafiles/reprocessed/hg38_v4/basic/ or bigWig files under http://fantom.gsc.riken.jp/5/datahub/hg38/reads/).</p> <p> </p> <p><strong>Data files</strong></p> <p>The resulting data files are formatted as bigWig (https://genome.ucsc.edu/FAQ/FAQformat.html#format6.1). '*.fwd.bw' and '*.rev.bw' represent forward and reverse strand on the genome, respectively. </p> <p> </p> <p><strong>Methods</strong></p> <p>The BAM files under http://fantom.gsc.riken.jp/5/datafiles/reprocessed/hg38_v4/basic/ were subjected to 5'-end counting by bedtools v2.27.1 (https://github.com/arq5x/bedtools2), followed by conversion into bigWig with jksrc v357 (http://hgdownload.cse.ucsc.edu/admin/).</p> <p> </p>
Data for: Resource landscapes explain contrasting patterns of aggregation and site fidelity by red knots at two wintering sites
<p>This repository contains data for the paper: Oudman et al. 2018. Resource landscapes explain contrasting patterns of aggregation and site fidelity by red knots at two wintering sites. <em>Movement Ecology</em> 6(14) 1-12. https://doi.org/10.1186/s40462-018-0142-4.</p> <p>Please cite the original publication when using this data.</p>
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Allen Brain Atlas
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OpenNeuro
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