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DeepPull: Deep Learning-Based Approach for Predicting Reopening, Decision, and Lifetime of Pull Requests on GitHub Open-Source Projects
<p>This dataset is from the paper titled "DeepPull: A Deep Learning-Based Approach for Predicting Reopening, Decision, and Lifetime of Pull Requests in GitHub Open-Source Projects," authored by P. Banyongrakkul and S. Phoomvuthisarn.</p><p>The dataset includes 288,121 pull requests from 83 open-source GitHub projects, spanning 6 different programming languages: Python, R, Java, Ruby, PHP, and C++. These pull requests were submitted between the periods of August 2010 and September 2023. The dataset consists of 30 tabular features, 2 textual features, 3 target features, and 5 metadata properties.</p>
A Mediation Model of the Usability and Intergroup Relation for Online Project Management Community Effectiveness with Microsoft Teams
<p>A Mediation Model of the Usability and Intergroup Relation for Online Project Management Community Effectiveness with Microsoft Teams</p>
Ancient roman road for Metateca Project
Phogrammetric model of an ancient roman road from "Aquinum". 3D models realized for **Metateca Project**. For more info https://www.youtube.com/watch?v=2-wgzQlbD6A http://www.metateca.it/ https://www.academia.edu/37421551/Dallo_scavo_allesperienza_immersiva_Il_progetto_Metateca_nellarea_archeologica_di_Aquinum Source: Objaverse 1.0 / Sketchfab
Crowd-sourced collected building attributes of the Colouring Dresden project (from 06 March 2023 to 01 October 2023)
<p><strong>Abstract</strong></p> <p>The building attribute data was collected by citizens as part of the citizen science project “Colouring Dresden”. Main goal was to collect information about buildings (like building age, usage, number of storeys, roof shape etc.) in an interactive online map. Different action formats (like dialogue series, presentations, mapathons, hackathons, and monthly meetings) were organised to bring citizens into action and discuss building-related topics. Focus was the sustainable construction of buildings and the question how good current buildings of the city are prepared for natural catastrophes like floods, heavy rain incidents or heat stress. The project period was from October 2022 to September 2023 and the mapping platform was launched in March 2023.</p> <p>Project page: <a href="https://colouring.dresden.ioer.info/">https://colouring.dresden.ioer.info/</a></p> <p>The project is part of the international research network “Colouring Cities Research Programme” (CCRP) and the first local project in Germany. Colouring Dresden is currently coordinating the European Hub of CCRP.</p> <p>The project was led by Leibniz Institute of Ecological Urban and Regional Development Dresden, Germany in cooperation with:</p> <p>- <strong>Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden (SLUB) - Regionalportal Saxorum</strong></p> <p>- <strong>Bund Deutscher Architektinnen und Architekten (BDA</strong>)</p> <p>- <strong>Zentrum für Baukultur Sachsen (ZfBK)</strong></p> <p>- <strong> Technische Sammlungen Dresden (TSD) / DLR_School_Lab TU Dresden</strong></p> <p>- <strong>Zentralbibliothek der Städtische Bibliotheken Dresden (SBD)</strong></p> <p> </p> <p><strong>Data description</strong></p> <p>The data contains several files:</p> <p>- <strong>building_atributes_geometry_20231001.gpkg</strong>: building geometries and building attributes in Geopackage file format</p> <p>- <strong>building_attributes.csv</strong>: building attributes</p> <p>- <strong>building_verification.csv</strong>: building verification data (crowd-sourced)</p> <p>- <strong>edit_history.csv</strong>: edit history of collected building_attributes</p> <p>- <strong>README.md</strong></p> <p> </p> <p><strong>Building geometries:</strong></p> <p><strong>Data source</strong>: 3D-Stadtmodell (3D city model) LoD2, Open Data Dresden</p> <p><strong>URL</strong>: <a href="https://daten.dresden.de/SONST/Datenbeschreibung_3D_Gebaeude_Dresden_OpenData.pdf">https://daten.dresden.de/SONST/Datenbeschreibung_3D_Gebaeude_Dresden_OpenData.pdf</a></p> <p><strong>Contact</strong>: opendata@dresden.de</p> <p><strong>Data license</strong>: Data license Germany – Attribution – Version 2.0 <a href="https://www.govdata.de/dl-de/by-2-0">https://www.govdata.de/dl-de/by-2-0</a>., Attribution: “Datenquelle: Landeshauptstadt Dresden, dl-de/by-2-0, opendata.dresden.de”</p> <p><strong>Spatial reference</strong>: WGS 84 / Web Mercator (EPSG: 3857)</p> <p><strong>Spatial extent</strong>: City of Dresden, Germany</p> <p><strong>Number of buildings</strong>: 135598</p> <p><strong>Completeness</strong>: Almost complete. Recently built buildings are not included.</p> <p><strong>Positional accuracy</strong>: Building footprints based on official cadastre data (ALKIS)</p> <p><strong>Up-to-dateness</strong>: Data is from 4<sup>th</sup> of May, 2023. Recently built buildings are not included.</p> <p> </p> <p> </p> <p><strong>Building attributes:</strong></p> <p><strong>(</strong>Including<strong> building verification </strong>and<strong> edit history)</strong></p> <p><strong>Data source:</strong> crowd-sourced collected building attributes from citizen science project “Colouring Dresden”, led by Leibniz Institute of Ecological Urban and Regional Development Dresden, Germany</p> <p><strong>Data collection:</strong> most attributes were collected by citizens using Citizen Science platform “Colouring Dresden”. Some attributes (address, centroid or identifier of external datasets like OpenStreetMap, Wikipedia or Wikidata) were linked automated.</p> <p><strong>URL:</strong> <a href="https://colouring.dresden.ioer.info/">https://colouring.dresden.ioer.info/</a></p> <p><strong>Contact:</strong> Dr Robert Hecht, <a href="https://www.ioer.de/institut/beschaeftigte/hecht">https://www.ioer.de/institut/beschaeftigte/hecht</a></p> <p><strong>Data license:</strong> Open Data Commons Open Database License (ODbL), Attribution: “Colouring Dresden contributors”, <a href="https://opendatacommons.org/licenses/odbl/">https://opendatacommons.org/licenses/odbl/</a></p> <p><strong>Spatial extent:</strong> City of Dresden, Germany</p> <p><strong>Number of buildings:</strong> 135598</p> <p><strong>Completeness: </strong></p> <p>Completeness of collected building attributes differs and depends on mapping activities of the Citizen Scientists from March to September in 2023. Completeness of automated linked attributes is 47.6% or more. Completeness of crowd-sourced attributes is up to 5.3%.</p> <p>100.0% building_id; ref_toid; location_latitude; location_longitude; revision_id; size_height_apex</p> <p>47.6% location_number; location_street; location_postcode; location_town</p> <p>5.3% building_attachment_form</p> <p>2.0% is_domestic</p> <p>1.1% size_storeys_core</p> <p>1.0% architectural_style; architectural_style_source; use_building_current</p> <p>0.6% size_storeys_attic; size_roof_shape</p> <p>0.3% use_building_origin</p> <p>And more attributes</p> <p><strong>Up-to-dateness:</strong> the data extract is from 1<sup>st</sup> of October, 2023 and includes the collecting period from March to September in 2023.</p>
SHEET project Deliverable D2.3-D2.4 - Apple Fruit position and temperature data
<p>The data publlished here are an outcome of the SHEET project <a href="https://ictagrifood.eu/node/44656" rel="nofollow">https://ictagrifood.eu/node/44656</a>.</p> <p>More information regarding the project and the collected data can be found in "SHEET_D2.1-D2.4.docx":<br>- D2.2, D2.3, D2.2-Continuation for information regarding the RGB-D/Thermal scanning platform prototype<br>- D2.4 for dataset information</p> <p>Data are organized by years of collection for seasons 2021, 2022, and 2023.</p> <p>Data recorded includes the following:<br>- Continuos weather data of the trial site in open field.<br>- Continuos microclimatic data of orchard conditions tested (2021, 2022 only).</p> <p>- Discrete radiation measurements of the illumination condition of each tested treatment , on a representative tree. </p> <p>- Continuos measurements of apple fruit surface temperature collected with thermocouples.<br>- Discrete measurements of apple fruit surface temperature collected with handheld thermal camera (2022, 2023 only)</p> <p>- Position of the fruit monitored for surface temperature</p> <p>- Discrete measurements of apple fruit surface temperature and position collected with an RGB-D/ Thermal scanning platform prototype (2022 only) </p> <p>-Discrete measurement reporting the moment of sunburn damage occurrence, its level, L*a*b* chroma and Hue color information, and DA-meter index values.</p> <p><strong>More information regarding SHEET Project activity carried out from the UniBo group can be found at the <a href="https://github.com/ECOPOM/SHEET_project_repo">dedicated GitHub repository</a></strong>.</p>
ASSESSING THE IMPACT OF YOUTH INVOLVEMENT ON NATIONAL DEVELOPMENT PROJECTS: A STUDY ON THE AGRICULTURAL DEVELOPMENT PROJECTS LED BY YALLA YALLA GROUP (YYG) UNDER RAB-RWANDA
<div> <p><strong><span> </span></strong></p> </div> <p><strong><span> </span></strong></p> <p><strong><span>ABSTRACT</span></strong></p> <p><span>The Study assessed he impact of Youth Involvement on National Development Projects: A Study on the Agricultural </span><span>development projects</span><span> Led by </span><span>Yalla Yalla Group (YYG) </span><span>under RAB-Rwanda</span><span> <span>a country which is experiencing rapid economic growth and characterized by a predominantly youthful population. Despite economic progress, Rwanda faces challenges such as insufficient job creation and inadequate economic infrastructure, leading to persistent issues of unemployment and underemployment, particularly among the youth. In response to these challenges, Yalla Yalla Group (YYG), comprising young professionals, directs its efforts toward agriculture development projects. The research objectives encompass evaluating the effectiveness of YYG's projects, examining socio-economic benefits, assessing the impact on community development, and exploring potential barriers to success. The study population comprises 198 individuals actively involved in YYG projects, with a sample of 132 determined using Slovin's formula through convenient sampling. Quantitative data is collected through a structured questionnaire and statistical analyses, including SPSS and multiple linear regression, are employed to scrutinize hypotheses. The findings reveal a highly significant positive correlation (r = 0.977, p < 0.01) between "country development" and "young people involvement," indicating a strong linear relationship. The F-statistic of 219.903 emphasizes that the predictors significantly explain variance in country development beyond chance. Specifically, the study highlights that increased involvement in agriculture is associated with higher levels of country development (coefficient: 0.506, t = 5.500, p < 0.001), underscoring a positive relationship. Livestock involvement, while statistically significant (coefficient: 0.017, t = 0.097, p = 0.003), exhibits a limited impact on country development. In contrast, higher engagement in business and entrepreneurship significantly correlates with increased country development (coefficient: 0.919, t = 4.963, p < 0.001), emphasizing a strong positive relationship. Additionally, heightened participation in environmental conservation is linked to elevated country development (coefficient: 0.157, t = 3.049, p = 0.003). </span></span></p> <p><strong><span>Keywords:</span></strong><span> </span><em><span>Youth Involvement;<span> </span>National Development Projects; Agricultural Development; Youth-Led Initiatives; Sustainable Development</span></em></p>
Dataset related to ADHD phase 1 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to the article "Transitioning to adult mental health services for young people with ADHD: an Italian-based survey on practices for pediatric and adult services" (phase 1 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health - RF-2019-12371228).</p>
The 1st video of XR4Human project:What is all about?
<p>The first XR4Human video delivers all the essential details you need to know about our project. In addition, it describes the project rationale, the project's contribution to the European XR Community and it's key outputs. Enjoy it and stay tuned! https://xr4human.eu/</p>
LI 850 sensor data of the Antarctic Modeling Observation System (ATMOS) project of the 40th Brazilian Antarctic Operation (OPERANTAR XL) to calculate the CO2 flux (FCO2)
<p>LI 850 sensor data of the Antarctic Modeling Observation System (ATMOS) project of the 40th Brazilian Antarctic Operation (OPERANTAR XL) to calculate the CO2 flux (FCO2)</p>
Data for STRIDE Project P2 "Development of Guidance for Scheduling of Freeway Work Zones to Minimize Congestion Impacts"
<p>This dataset contains the raw traffic data from sensors located on I-95 at two locations in Georgia as well as analysis of the raw data for trends key perofrmance measures such as speed and flow rate. These data were used in the research for STRIDE Project P2 "Development of Guidance for Scheduling of Freeway Work Zones to Minimize Congestion Impacts" and described in the STRIDE Final Project for this project.</p>
Database of feed efficiency indicators of intensive fattening lambs of sheep breeds in Latvia in 2nd trial (A23) with-in the framework of the project of the Latvian Council of Science LZP-2021/1-0489 project
<p><span>Project of The Latvian Council of Science (LCS) - LZP-2021/1-0489 project: “<strong>Development of an innovative approach to identify biological determinants involved in the between-animal variation in feed efficiency in sheep farming</strong>”.</span></p> <p><span>The <strong>aim of the project</strong> is</span><span> to determine whether the feed efficiency status of <span>Latvian meat sheep breeds</span> could be predicted using a panel of genetic and molecular markers previously found to be associated with divergent FE status in a training population of lambs when fed the same diet.</span></p> <p><strong><span>Novelty</span></strong><span>: to determine the parameters predicting the most productive result of lamb rearing, we set out to develop the cheapest and most effective method for determining markers of feed efficiency - based on molecular and genetic markers obtained from the blood of live lambs. </span></p> <p><strong><span>About the project:</span></strong></p> <p><span>The costs associated with lambing (buying or keeping sheep) and preparing or purchasing feed are the two most significant components of variable costs in sheep raising. Feed costs are high due to poor grain growing conditions in major producing countries, the use of feed grains in ethanol production, and increased competition for land in crop production compared to urban development<span>.</span> <span>Feed efficiency </span>in growing lambs (i.e., the animal’s ability to reach a market or adult body weight (BW) with the least feed intake) is a critical factor in the sheep industry. <span>Improving FE reduces production costs. Improving FE by 5% can bring economic benefits up to four times higher than a 5% increase in average daily gain (ADG).</span></span></p> <p><span>Traditionally, meat breeding programs have focused on outputs due mainly to the routine availability of phenotypic data on outputs or correlated traits. Currently, no marker has successfully explained enough of the variability in <span>feed efficiency </span>that they were used as part of a routine improvement program. According to our data, no genetic parameters for performance and feed efficiency traits are available for sheep. <span>The physiological determinants of feed efficiency or putative biomarkers used to analyse animal-to-animal variation in live lambs could be a cost-effective and rapid tool for genetic selection or management decisions.</span></span></p> <p><strong><span>About the data of the project:</span></strong></p> <p><span>LZP-2021/1-0489 project data on lamb samples of the second year: 2023, or A23 group, which consists of 92 intensively fattened lambs from eight breeds, including LT breed, which were raised in the meadow and are semi-sibi lambs within the scope of the study.</span></p> <p><span>The database contains data on ultrasonography measurements of lambs during the beginning and end of fattening, intensive fattening data - actual and calculated on the 90th and 150th day, the amount of feed used and slaughter data. Ultrasonography data were used to calculate muscle and/or fat depth changes at the 13th rib during the fattening period.</span></p> <p><span><span>Based on the requirements of the breeding program of the breeds (LAAA, 2022), every year, the offspring of the sire ram, certified for breeding activity, are selected and analysed to estimate the sire rams.</span></span><span><span> </span></span><span><span>All lambs were born as twins, triplets or quadruplets from different ewes and health status was assessed before inclusion in the study so that there were at least two lambs per sire ram from the breed. This study was carried out in cooperation with the Latvian Sheep Breeders' Association </span></span><span>at<span> the ram breeding control station.</span></span></p> <p><strong><span> </span></strong></p> <p><span>The data is the<strong><span> joint property</span></strong> of the participants of the LCS project: the University of Latvia and the Latvian University of Life Sciences and Technologies.</span></p>
Database of methods of significant SNPs analysed in DNA of the 2nd trial (A23) within the framework of the project of the Latvian Council of Science LZP-2021/1-0489 project
<p>Project of The Latvian Council of Science (LCS) - LZP-2021/1-0489 project: “<strong>Development of an innovative approach to identify biological determinants involved in the between-animal variation in feed efficiency in sheep farming</strong>”.</p> <p>The <strong>aim of the project</strong> is to determine whether the feed efficiency status of Latvian meat sheep breeds could be predicted using a panel of genetic and molecular markers previously found to be associated with divergent FE status in a training population of lambs when fed the same diet.</p> <p><strong>Novelty</strong>: to determine the parameters predicting the most productive result of lamb rearing, we set out to develop the cheapest and most effective method for determining markers of feed efficiency based on molecular and genetic markers obtained from the blood of live lambs.</p> <p><strong>About the project:</strong></p> <p>The costs associated with lambing (buying or keeping sheep) and preparing or purchasing feed are the two most significant components of variable costs in sheep raising. Feed costs are high due to poor grain growing conditions in major producing countries, the use of feed grains in ethanol production, and increased competition for land in crop production compared to urban development. Feed efficiency in growing lambs (i.e., the animal’s ability to reach a market or adult body weight (BW) with the least feed intake) is a critical factor in the sheep industry. Improving FE reduces production costs. Improving FE by 5% can bring economic benefits up to four times higher than a 5% increase in average daily gain (ADG).</p> <p>Traditionally, meat breeding programs have focused on outputs due mainly to the routine availability of phenotypic data on outputs or correlated traits. Currently, no marker has successfully explained enough of the variability in feed efficiency that they were used as part of a routine improvement program. According to our data, no genetic parameters for performance and feed efficiency traits are available for sheep. The physiological determinants of feed efficiency or putative biomarkers used to analyse animal-to-animal variation in live lambs could be a cost-effective and rapid tool for genetic selection or management decisions.</p> <p><strong>About the data of the project:</strong></p> <p>The database contains methodologies for 57 SNPs, for which a statistically significant association with one of the feed efficiency indicators was found in the project's first group (A22). A laboratory-based genotyping method was developed for each selected SNP, which was tested using samples from the A22 group; the results were obtained using the NGS methodology.</p> <p> </p> <p><strong> </strong></p> <p>The data is the<strong> joint property</strong> of the participants of the LCS project: the University of Latvia and the Latvian University of Life Sciences and Technologies.</p>
Database of biochemistry data of intensive fattening lambs of sheep breeds raised in Latvia in 2022 within the framework of project of the Latvian Council of Science: LZP-2021/1-0489
<p><span>Project of The Latvian Council of Science (LCS) - LZP-2021/1-0489 project: “<strong>Development of an innovative approach to identify biological determinants involved in the between-animal variation in feed efficiency in sheep farming</strong>”.</span></p> <p><span>The <strong>aim of the project</strong> is</span><span> to determine whether the feed efficiency status of <span>Latvian meat sheep breeds</span> could be predicted using a panel of genetic and molecular markers previously found to be associated with divergent FE status in a training population of lambs when fed the same diet.</span></p> <p><strong><span>Novelty</span></strong><span>: To determine the parameters predicting the most productive result of lamb rearing, we set out to develop the cheapest and most effective method for determining markers of feed efficiency based on molecular and genetic markers obtained from the blood of live lambs. </span></p> <p><strong><span>About the project:</span></strong></p> <p><span>The costs associated with lambing (buying or keeping sheep) and preparing or purchasing feed are the two most significant components of variable costs in sheep raising. Feed costs are high due to poor grain growing conditions in major producing countries, the use of feed grains in ethanol production, and increased competition for land in crop production compared to urban development<span>.</span> <span>Feed efficiency </span>in growing lambs (i.e., the animal’s ability to reach a market or adult body weight (BW) with the least feed intake) is a critical factor in the sheep industry. <span>Improving FE reduces production costs. Improving FE by 5% can bring economic benefits up to four times higher than a 5% increase in average daily gain (ADG).</span></span></p> <p><span>Traditionally, meat breeding programs have focused on outputs due mainly to the routine availability of phenotypic data on outputs or correlated traits. Currently, no marker has successfully explained enough of the variability in <span>feed efficiency </span>that they were used as part of a routine improvement program. According to our data, no genetic parameters for performance and feed efficiency traits are available for sheep. <span>The physiological determinants of feed efficiency or putative biomarkers used to analyse animal-to-animal variation in live lambs could be a cost-effective and rapid tool for genetic selection or management decisions.</span></span></p> <p><strong><span>About the data of the project:</span></strong></p> <p><span>LZP-2021/1-0489 project data on lamb samples of the second year: 2023, or A23 group, which consists of 92 intensively fattened lambs from eight breeds, including the LT breed, which were raised in the meadow and are semi-sibi lambs within the scope of the study. The dataset includes data on biochemical indicators: Insulin-like growth factor 1, Insulin, Total thyroxine, Adrenocorticotropic hormone, Haematocrit, Hemoglobin, and Glucose. The blood samples for tests were taken before intensive fattening at a mean age of 92 days (interval from 64 to 109 days). Fattening starting age depends on body weight when lambs are separated from their mothers.</span></p> <p><span><span>Based on the requirements of the breeding program of the breeds (LAAA, 2022), every year, the offspring of the sire ram, certified for breeding activity, are selected and analysed to estimate the sire rams.</span></span><span><span> </span></span><span><span>All lambs were born as twins, triplets, or quadruplets from different ewes, and health status was assessed before inclusion in the study so that there were at least two lambs per sire ram from the breed. This study was carried out in cooperation with the Latvian Sheep Breeders' Association </span></span><span>at<span> the ram breeding control station. </span></span></p> <p><strong><span> </span></strong></p> <p><span>The data is the<strong><span> joint property</span></strong> of the participants of the LCS project: the University of Latvia and the Latvian University of Life Sciences and Technologies.</span></p>
Database of expression analysis of samples of the 1st trial (A22) of sheep breeds in Latvia within the framework of the project of the Latvian Council of Science LZP-2021/1-0489 project
<p><span>Project of The Latvian Council of Science (LCS) - LZP-2021/1-0489 project: “<strong>Development of an innovative approach to identify biological determinants involved in the between-animal variation in feed efficiency in sheep farming</strong>”.</span></p> <p><span>The <strong>aim of the project</strong> is</span><span> to determine whether the feed efficiency status of <span>Latvian meat sheep breeds</span> could be predicted using a panel of genetic and molecular markers previously found to be associated with divergent FE status in a training population of lambs when fed the same diet.</span></p> <p><strong><span>Novelty</span></strong><span>: to determine the parameters predicting the most productive result of lamb rearing, we set out to develop the cheapest and most effective method for determining markers of feed efficiency - based on molecular and genetic markers obtained from the blood of live lambs. </span></p> <p><strong><span>About the project:</span></strong></p> <p><span>The costs associated with lambing (buying or keeping sheep) and preparing or purchasing feed are the two most significant components of variable costs in sheep raising. Feed costs are high due to poor grain growing conditions in major producing countries, the use of feed grains in ethanol production, and increased competition for land in crop production compared to urban development<span>.</span> <span>Feed efficiency </span>in growing lambs (i.e., the animal’s ability to reach a market or adult body weight (BW) with the least feed intake) is a critical factor in the sheep industry. <span>Improving FE reduces production costs. Improving FE by 5% can bring economic benefits up to four times higher than a 5% increase in average daily gain (ADG).</span></span></p> <p><span>Traditionally, meat breeding programs have focused on outputs due mainly to the routine availability of phenotypic data on outputs or correlated traits. Currently, no marker has successfully explained enough of the variability in <span>feed efficiency </span>that they were used as part of a routine improvement program. According to our data, no genetic parameters for performance and feed efficiency traits are available for sheep. <span>The physiological determinants of feed efficiency or putative biomarkers used to analyse animal-to-animal variation in live lambs could be a cost-effective and rapid tool for genetic selection or management decisions.</span></span></p> <p><strong><span>About the data of the project:</span></strong></p> <p><span>LZP-2021/1-0489 project data on lamb samples of the year 2022, or the A22 group, which consists of 76 intensively fattened lambs from six breeds. The blood samples for RNA extraction were taken after intensive fattening at an average body weight of 45 – 50 kg.</span></p> <p><span>Gene expression levels were determined using qPCR methodology using the GAPDH gene as a reference. Three genes were analysed in the project: MTOR (The mechanistic mammalian target of rapamycin), CAST (Calpastatin) and UPC3 (uncoupling protein 3).</span></p> <p><span>The data obtained will be used to determine the changes in expression levels related to gene variations (MTOR: <a href="https://doi.org/10.5281/zenodo.8146731">https://doi.org/10.5281/zenodo.8146731</a>; CAST: <a href="https://doi.org/10.5281/zenodo.8146372">https://doi.org/10.5281/zenodo.8146372</a>; UPC3: <a href="https://doi.org/10.5281/zenodo.8146778">https://doi.org/10.5281/zenodo.8146778</a>); <span> </span>as well as to analyze the relationship of expression levels with biochemical parameters (<a href="https://doi.org/10.5281/zenodo.8143225">https://doi.org/10.5281/zenodo.8143225</a>) and feed efficiency indicators (<a href="https://doi.org/10.5281/zenodo.8143244">https://doi.org/10.5281/zenodo.8143244</a>) of lambs.</span></p> <p><strong><span> </span></strong></p> <p><span>The data is the<strong><span> joint property</span></strong> of the participants of the LCS project: the University of Latvia and the Latvian University of Life Sciences and Technologies.</span></p>
Database of MSTN gene multi-loci genotypes of samples of the 1st trial (A22) of sheep breeds of Latvia within the framework of the project of the Latvian Council of Science LZP-2021/1-0489 project
<p><span>Project of The Latvian Council of Science (LCS) - LZP-2021/1-0489 project: “<strong>Development of an innovative approach to identify biological determinants involved in the between-animal variation in feed efficiency in sheep farming</strong>”.</span></p> <p><span>The <strong>aim of the project</strong> is</span><span> to determine whether the feed efficiency status of <span>Latvian meat sheep breeds</span> could be predicted using a panel of genetic and molecular markers previously found to be associated with divergent FE status in a training population of lambs when fed the same diet.</span></p> <p><strong><span>Novelty</span></strong><span>: to determine the parameters predicting the most productive result of lamb rearing, we set out to develop the cheapest and most effective method for determining markers of feed efficiency - based on molecular and genetic markers obtained from the blood of live lambs. </span></p> <p><strong><span>About the project:</span></strong></p> <p><span>The costs associated with lambing (buying or keeping sheep) and preparing or purchasing feed are the two most significant components of variable costs in sheep raising. Feed costs are high due to poor grain growing conditions in major producing countries, the use of feed grains in ethanol production, and increased competition for land in crop production compared to urban development<span>.</span> <span>Feed efficiency </span>in growing lambs (i.e., the animal’s ability to reach a market or adult body weight (BW) with the least feed intake) is a critical factor in the sheep industry. <span>Improving FE reduces production costs. Improving FE by 5% can bring economic benefits up to four times higher than a 5% increase in average daily gain (ADG).</span></span></p> <p><span>Traditionally, meat breeding programs have focused on outputs due mainly to the routine availability of phenotypic data on outputs or correlated traits. Currently, no marker has successfully explained enough of the variability in <span>feed efficiency </span>that they were used as part of a routine improvement program. According to our data, no genetic parameters for performance and feed efficiency traits are available for sheep. <span>The physiological determinants of feed efficiency or putative biomarkers used to analyse animal-to-animal variation in live lambs could be a cost-effective and rapid tool for genetic selection or management decisions.</span></span></p> <p><span>Myostatin (MSTN), a highly conserved member of the transforming growth factor-beta (TGF-b) superfamily, also known as growth/differentiation factor 8 (GDF8), the major regulator of myogenesis, functions as a negative regulator of muscle growth and development in mammals. Myostatin is a transforming growth factor (TGF)-β superfamily member and cannot be classified into the existing TGF-β subfamilies, such as inhibins or bone morphogenic proteins.<span> Mutations <em><span>MSTN</span></em> produce a “double-muscle” phenotype, making it commercially invaluable for improving livestock meat production and providing high-quality human protein. However, mutations at different loci of the <em><span>MSTN</span></em> often produce a variety of different phenotypes.</span></span></p> <p><strong><span>About the data of the project:</span></strong></p> <p><span>LZP-2021/1-0489 project data on lamb samples of the year 2022, or the A22 group, which consists of 76 intensively fattened lambs from six breeds.</span></p> <p><span><span>The whole MSTN gene was sequenced using Illumina NGS technology (AmpliSeq).</span></span><span><span> </span></span><span><span>The MSTN gene has been fully sequenced in lambs of Latvia (in sheep research in Latvia) for the first time. Information about 23 loci (</span></span><span><a href="https://doi.org/10.5281/zenodo.8146724"><span>https://doi.org/10.5281/zenodo.8146724</span></a></span><u><span>)</span></u><span><span> <span>compared with the last </span></span></span><span>reference sequences <span>ARS_UI_Ramb_v2.0.</span></span></p> <p><span>All variable SNPs in the MSTN gene were constructed by combining 24 distinct multi-loci genotypes. Reducing the number of loci to 16 by excluding those variables in only one sample or complete linkage disequilibrium did not decrease the number of genotypes. Among these 16 loci, 12 multi-locus genotypes were found in only one sample. To streamline the association analysis, the MSTN gene was divided into four regions: the promoter and exon 1, intron 1, intron 2, and the 3′UTR. This division helped reduce the number of multilocus genotypes.</span></p> <p><span>The database contains information on multiloci genotypes for four regions of the MSTN gene. </span><span>The multi-locus genotypes are presented using standard IUB/IUPAC nucleic acid codes, representing the genotype of two alleles at a single locus with a single letter.</span></p> <p> </p> <p><span><span>The data is the<strong><span> joint property</span></strong> of the participants of the LCS project: the University of Latvia and the Latvian University of Life Sciences and Technologies</span></span></p>
Database of genotypes of significant SNPs of samples of the 2nd trial (A23) of sheep breeds in Latvia within the framework of the project of the Latvian Council of Science LZP-2021/1-0489
<p><span>Project of The Latvian Council of Science (LCS) - LZP-2021/1-0489 project: “<strong>Development of an innovative approach to identify biological determinants involved in the between-animal variation in feed efficiency in sheep farming</strong>”.</span></p> <p><span>The <strong>aim of the project</strong> is</span><span> to determine whether the feed efficiency status of <span>Latvian meat sheep breeds</span> could be predicted using a panel of genetic and molecular markers previously found to be associated with divergent FE status in a training population of lambs when fed the same diet.</span></p> <p><strong><span>Novelty</span></strong><span>: to determine the parameters predicting the most productive result of lamb rearing, we set out to develop the cheapest and most effective method for determining markers of feed efficiency - based on molecular and genetic markers obtained from the blood of live lambs. </span></p> <p><strong><span>About the project:</span></strong></p> <p><span>The costs associated with lambing (buying or keeping sheep) and preparing or purchasing feed are the two most significant components of variable costs in sheep raising. Feed costs are high due to poor grain growing conditions in major producing countries, the use of feed grains in ethanol production, and increased competition for land in crop production compared to urban development<span>.</span> <span>Feed efficiency </span>in growing lambs (i.e., the animal’s ability to reach a market or adult body weight (BW) with the least feed intake) is a critical factor in the sheep industry. <span>Improving FE reduces production costs. Improving FE by 5% can bring economic benefits up to four times higher than a 5% increase in average daily gain (ADG).</span></span></p> <p><span>Traditionally, meat breeding programs have focused on outputs due mainly to the routine availability of phenotypic data on outputs or correlated traits. Currently, no marker has successfully explained enough of the variability in <span>feed efficiency </span>that they were used as part of a routine improvement program. According to our data, no genetic parameters for performance and feed efficiency traits are available for sheep. <span>The physiological determinants of feed efficiency or putative biomarkers used to analyse animal-to-animal variation in live lambs could be a cost-effective and rapid tool for genetic selection or management decisions.</span></span></p> <p><strong><span>About the data of the project:</span></strong></p> <p><span>LZP-2021/1-0489 project data on lamb samples of the second year: 2023, or A23 group, which consists of 92 intensively fattened lambs from eight breeds, including LT breed, which were raised in the meadow and are semi-sibi lambs within the scope of the study.</span></p> <p><span>The database contains genotyping results for 57 SNPs, for which a statistically significant association with one of the feed digestion indicators was found in the project's first group (A22). A laboratory-based genotyping method was developed for each selected SNP, which was tested using samples from the A22 group; the results were obtained using the NGS methodology.</span></p> <p><span>In the database, SNPs are coded with a laboratory code and an rs ID number. The gene in which the SNP is located is also indicated. The genotypes of each SNP are presented using standard IUB/IUPAC nucleic acid codes, representing the genotype of two alleles at a single locus with a single letter. </span></p> <p><strong><span> </span></strong></p> <p><span>The data is the<strong><span> joint property</span></strong> of the participants of the LCS project: the University of Latvia and the Latvian University of Life Sciences and Technologies.</span></p>
Database of genotypes of significant SNPs of samples of the 1st trial (A22) of sheep breeds inLatvia within the framework of the project of the Latvian Louncil of Lcience LZP-2021/1-0489
<p><span>Project of The Latvian Council of Science (LCS) - LZP-2021/1-0489 project: “<strong>Development of an innovative approach to identify biological determinants involved in the between-animal variation in feed efficiency in sheep farming</strong>”.</span></p> <p><span>The <strong>aim of the project</strong> is</span><span> to determine whether the feed efficiency status of <span>Latvian meat sheep breeds</span> could be predicted using a panel of genetic and molecular markers previously found to be associated with divergent FE status in a training population of lambs when fed the same diet.</span></p> <p><strong><span>Novelty</span></strong><span>: to determine the parameters predicting the most productive result of lamb rearing, we set out to develop the cheapest and most effective method for determining markers of feed efficiency - based on molecular and genetic markers obtained from the blood of live lambs. </span></p> <p><strong><span>About the project:</span></strong></p> <p><span>The costs associated with lambing (buying or keeping sheep) and preparing or purchasing feed are the two most significant components of variable costs in sheep raising. Feed costs are high due to poor grain growing conditions in major producing countries, the use of feed grains in ethanol production, and increased competition for land in crop production compared to urban development<span>.</span> <span>Feed efficiency </span>in growing lambs (i.e., the animal’s ability to reach a market or adult body weight (BW) with the least feed intake) is a critical factor in the sheep industry. <span>Improving FE reduces production costs. Improving FE by 5% can bring economic benefits up to four times higher than a 5% increase in average daily gain (ADG).</span></span></p> <p><span>Traditionally, meat breeding programs have focused on outputs due mainly to the routine availability of phenotypic data on outputs or correlated traits. Currently, no marker has successfully explained enough of the variability in <span>feed efficiency </span>that they were used as part of a routine improvement program. According to our data, no genetic parameters for performance and feed efficiency traits are available for sheep. <span>The physiological determinants of feed efficiency or putative biomarkers used to analyse animal-to-animal variation in live lambs could be a cost-effective and rapid tool for genetic selection or management decisions.</span></span></p> <p><strong><span>About the data of the project:</span></strong></p> <p><span>LZP-2021/1-0489 project data on lamb samples of the year 2022, or the A22 group, which consists of 76 intensively fattened lambs from six breeds.</span></p> <p><span>The database contains genotyping results for 57 SNPs, for which a statistically significant association with one of the feed efficiency indicators was established within the project, using NGS methodology and association statistical methods. The specific SNPs were selected for further work as the first DNA molecular markers for future sheep breeding programs in Latvia.</span></p> <p><span>In the database, SNPs are coded with a laboratory code and an rs ID number. The gene in which the SNP is located is also indicated. The genotypes of each SNP are presented using standard IUB/IUPAC nucleic acid codes, representing the genotype of two alleles at a single locus with a single letter. </span></p> <p><strong><span> </span></strong></p> <p><span>The data is the<strong><span> joint property</span></strong> of the participants of the LCS project: the University of Latvia and the Latvian University of Life Sciences and Technologies.</span></p>
Data related to SoloBat Project
<p>This dataset includes the data related to SoloBat project.</p>
ARTEMIS project - Long-term field experiment (LTE) dataset for crop modelling
<p>The spreadsheets aim at collecting general information and specific data (e.g., crop yield, soil properties and weather) from long-term field experiments (LTE's) that are used to calibrate and validate ARMOSA model for ARTEMIS project - WP3 task 3.1 activity. Particularly, information reported will be useful for providing an overview of the distribution of agro-ecological systems, management strategies, field measures, and weather data available, useful to run ARMOSA model under current and future climate change scenarios.</p>
SECURES-Energy: Hourly electricity demand and supply profiles for historical climate and climate change projections in Europe until 2100
<p><strong>SECURES-Energy</strong></p> <p>Weather-dependent renewable electricity systems are vulnerable to climate change impacts. Electricity generation and demand profiles considering weather and climate impacts are needed in energy system modelling. We present a consistent and high-quality energy database in data formats useful for energy system modelling and keeping the high spatiotemporal complexity of climate data. The open-access dataset SECURES-Energy contains all relevant electricity demand and supply components for the EU and several additional European countries in hourly resolution covering the period 1981-2100. It is based on reanalysis data ERA5(-Land) for the historical period and two EURO-CORDEX emission scenarios (RCP 4.5 and RCP 8.5). On the generation side, impacts on onshore and offshore wind power generation, solar PV generation, and hydropower generation (run-of-river and reservoirs) – which is often missing in comparable datasets – are provided. On the demand side, all demand components relevant to future electricity systems including e-heating, e-cooling, e-mobility, and electricity demand in industry, are provided.</p> <p>The detailed methods are described in the final project report (see link below) in Chapter 2.2 and Chapter 4.3 and a related journal publication is currently in preparation.</p> <p><strong>Further information:</strong></p> <ul> <li>Project website SECURES: https://www.secures.at/</li> <li>All project-related publications: https://www.secures.at/publications</li> <li>Final SECURES project report: https://www.secures.at/fileadmin/cmc/Final_Report_SECURES.pdf and https://www.klimafonds.gv.at/wp-content/uploads/sites/16/C061007-ACRP12-SECURES-KR19AC0K17532-EB.pdf</li> </ul> <p>The SECURES-Energy dataset provides variables visible in the table.</p> <ol> <li>Hourly profiles ERA5-Land 1981-2010</li> <li>Hourly profiles RCP 4.5/RCP 8.5 2011-2100</li> </ol> <p> </p> <p><strong>Production profiles:</strong></p> <table> <tbody> <tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Temporal resolution</th> </tr> <tr> <th>Photovoltaics</th> <td>pv</td> <td>-</td> <td>hourly</td> </tr> <tr> <th>Wind onshore</th> <td>wind</td> <td>-</td> <td>hourly</td> </tr> <tr> <th>Wind offshore</th> <td>wind_offshore</td> <td>-</td> <td>hourly</td> </tr> <tr> <th>Hydro run-of-river</th> <td>hydro_ror</td> <td>-</td> <td>hourly</td> </tr> </tbody> </table> <p> </p> <p><strong>Demand profiles:</strong></p> <table> <tbody> <tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Explanation</th> </tr> <tr> <th>Temperature</th> <td>temperature</td> <td> <p>°C</p> </td> <td> <p>Population-weighted mean temperature (2 m)</p> </td> </tr> <tr> <th> <p>Rounded temperature</p> </th> <td>rounded_temperature</td> <td>°C</td> <td>Temperature values rounded to zero decimal places</td> </tr> <tr> <th>Daytype</th> <td>day type</td> <td>-</td> <td> <p>weekdays = typeday 0; Saturday or day before a holiday = typeday 1; Sunday or holiday = typeday 2</p> </td> </tr> <tr> <th>Month<strong><br></strong></th> <td> <p>month</p> </td> <td> <p>-</p> </td> <td> <p> The column “month” refers to the month of the year. 1 = January, 2 = February etc.</p> </td> </tr> <tr> <th> Season</th> <td>season</td> <td>-</td> <td> <p>0 = Summer (15/05 - 14/09)</p> <p>1 = Winter (1/11 - 20/3)</p> <p>2 = Transition (21/3 - 14/5 & 15/9 - 31/10)</p> </td> </tr> <tr> <th>Load e-mobilty</th> <td> <p>load_emobility</p> </td> <td> <p>-</p> </td> <td> <p>E-mobility electricity demand profile, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Non-metallic minerals</th> <td> <p>non_metallic_minerals</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile of the industrial sector non-metallic minerals, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</p> </td> </tr> <tr> <th>Paper</th> <td> <p>paper</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile of the industrial sector paper, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</p> </td> </tr> <tr> <th>Iron and steel</th> <td> <p>iron_and_steel</p> </td> <td> <p>-</p> </td> <td>Electricity demand profile of the industrial sector iron and steel, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</td> </tr> <tr> <th>Chemicals and petrochemicals</th> <td> <p>chemicals_and_petrochemicals</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile of the industrial sector chemicals and petrochemicals, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</p> </td> </tr> <tr> <th>Food and tobacco</th> <td> <p>food_and_tobacco</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile of the industrial sector food and tobacco, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</p> </td> </tr> <tr> <th>SHW residential</th> <td> <p>shw_residential</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for sanitary hot water in the residential sector, normalized to an annual demand of 1,000,000 (non-weather-dependent)</p> </td> </tr> <tr> <th>SHW tertiary<strong><br></strong></th> <td> <p>shw_tertiary</p> </td> <td> <p> </p> </td> <td> <p>Electricity demand profile for sanitary hot water in the tertiary sector, normalized to an annual demand of 1,000,000 (non-weather-dependent)</p> </td> </tr> <tr> <th>Cooling residential<strong><br></strong></th> <td> <p>cooling_residential</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for cooling in the residential sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Heating residential<strong><br></strong></th> <td> <p>heating_residential</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for heating in the residential sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Cooling tertiary</th> <td> <p>cooling_tertiary</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for cooling in the tertiary sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Heating tertiary<strong><br></strong></th> <td> <p>heating_tertiary</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for heating in the tertiary sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Rest<strong><br></strong></th> <td> <p>rest</p> </td> <td> <p>-</p> </td> <td> <p>Rest electricity demand profile, normalized to an annual demand of 1,000,000 (non-weather-dependent)</p> </td> </tr> <tr> <th>Exogenous H2<strong><br></strong></th> <td> <p>exogenous_H2</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for electrolysis (flat profile), normalized to an annual demand of 1,000,000 (non-weather-dependent)</p> </td> </tr> <tr> <th>Total<strong><br></strong></th> <td> <p>total</p> </td> <td> <p>-</p> </td> <td> <p>Total electricity demand profile containing all components above (e-mobility, industry, residential heating, residential sanitary hot water, residential cooling, tertiary heating, tertiary sanitary hot water, tertiary cooling, rest, and exogenous H2 electricity demand), normalized to an annual demand of 10,000,000 in the reference year 2010</p> </td> </tr> </tbody> </table> <p>Electricity supply profiles for wind (onshore and offshore), hydro (run-of-river), and solar generation are provided for almost all European countries, namely: Andorra (AD), Albania (AL), Austria (AT), Bosnia and Herzegovina (BA), Belgium (BE), Bulgaria (BG), Switzerland (CH), Czech Republic (CZ), Germany (DE), Denmark (DK), Estonia (EE), Spain (ES), Finland (FI), France (FR), United Kingdom of Great Britain and Northern Ireland (GB), Greece (GR), Croatia (HR), Hungary (HU), Republic of Ireland (IE), Italy (IT), Liechtenstein (LI), Lithuania (LT), Luxembourg (LU), Latvia (LV), Montenegro (ME), North Macedonia (MK), Malta (MT), Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Sweden (SE), Slovenia (SI), Slovakia (SK), San Marino (SM), Ukraine (UA), Vatican (VA), and Kosovo (XK). The countries covered by the electricity demand profiles are the EU27 countries (except for Cyprus), CH, GB, and NO.</p> <p>Industrial, heating, and cooling demand profiles are based on regressions developed in the H2020 Hotmaps project [1] [2]. </p> <p>SECURES-Energy is available in a tabular csv format for the historical period (1981-2010) created from ERA5 and ERA5-Land and two future emission scenarios (<strong>RCP 4.5 </strong>and <strong>RCP 8.5</strong>, both 2011-2100) created from one CMIP5 EURO-CORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E) on the<strong> </strong>spatial aggregation level<strong> NUTS0 </strong>(country-wide).</p> <p>The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shapefiles of the different NUTS levels.</p> <p>Hydro reservoir profiles are also published and can be found in the related dataset SECURES-Met: https://zenodo.org/records/7907883.</p> <p>The project SECURES and corresponding publications are funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.</p> <p>[1] Fallahnejad M. Hotmaps-data-repository-structure 2019. https://wiki.hotmaps.eu/en/Hotmaps-open-data-repositories.</p> <p>[2] Pezzutto S, Zambotti S, Croce S, Zambelli P, Garegnani G, Scaramuzzino C, et al. HOTMAPS - D2.3 WP2 Report – Open Data Set for the EU28. 2019.</p>
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.