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Madidi Project Full Dataset
<p>This item contains <strong>version 5.0</strong> of the Madidi Project's full dataset. The zip file contains (1) raw data, which was downloaded from Tropicos (www.tropicos.org) on August 18, 2020; (2) R scripts used to modify, correct, and clean the raw data; (3) clean data that are the output of the R scripts, and which are the point of departure for most uses of the Madidi Dataset; (4) post-cleaning scripts that obtain additional but non-essential information from the clean data (e.g. by extracting environmental data from rasters); and (5) a miscellaneous collection of additional non-essential information and figures. This item also includes the <strong>Data Use Policy</strong> for this dataset.</p> <p>The core dataset of the Madidi Project consists of a network of ~500 forest plots distributed in and around the Madidi National Park in Bolivia. This network contains 50 permanently marked large plots (1-ha), as well as >450 temporary small plots (0.1-ha). Within the large plots, all woody individuals with a dbh ≥10 cm have been mapped, tagged, measured, and identified. Some of these plots have also been re-visited and information on mortality, recruitment, and growth exists. Within the small plots, all woody individuals with a dbh ≥2.5 cm have been measured and identified. Each plot has some edaphic and topographic information, and some large plots have information on various plant functional traits.</p> <p>The Madidi Project is a collaborative research effort to document and study plant biodiversity in the Amazonia and Tropical Andes of northwestern Bolivia. The project is currently lead by the Missouri Botanical Garden (MBG), in collaboration with the Herbario Nacional de Bolivia. The management of the project is at MBG, where J. Sebastian Tello (sebastian.tello@mobot.org) is the scientific director. The director oversees the activities of a research team based in Bolivia. MBG works in collaboration with other data contributors (currently: Manuel J. Macía [manuel.macia@uam.es], Gabriel Arellano [gabriel.arellano.torres@gmail.com] and Beatriz Nieto [sonneratia@gmail.com]), with a representative from the Herbario Nacional de Bolivia (LPB; Carla Maldonado [carla.maldonado1@gmail.com]), as well as with other close associated researchers from various institutions. For more information regarding the organization and objectives of the Madidi Project, you can visit the project’s website (<strong>www.madidiproject.weebly.com</strong>).</p>
sample data for project
<p>some details</p>
Figure 1 in Diversity of Chironomidae (Diptera) breeding in the Great Stour, Kent: baseline results from the Westgate Parks non-biting midge project
Figure 1. Location of the Great Stour in Kent, UK, and six sampling sites of Chironomidae genera in Westgate Parks and nearby areas. (a) Map of the UK with Kent county highlighted. (b) Kent county showing the Great Stour divided into the Upper Great Stour, the East Stour and the main Great Stour with the sampling locations in the city of Canterbury. (c) Sampling locations, including Rheims Way (Site 1), Westgate Gardens (Site 2), Westgate Towers (Site 3), Bingley Island (Site 4; a side stream of the Great Stour), Horton (Site 5; a site 3 km upstream from Westgate Parks) and Kingsmead Field (Site 6 a site 1 km downstream from Westgate Parks).
Datasets for HLU7 of the CROSSBOW project.
<p>Datasets for HLU7 of the CROSSBOW project.</p>
Cellesce Z projection 2D dataset - Colorectal organoids
<p>Cellesce Z projection 2D dataset - Colorectal organoids </p>
Berg and Breede River Rehabilitation Project Data
<p>This study investigates the efficiency of active versus passive interventions and investment on riparian rehabilitation success in two local catchments. Our research conducted at 11 riparian rehabilitation sites varying in size across these catchments. To evaluate and compare the success of these active and passive rehabilitation, we conducted vegetation, soil and geomorphological sampling and analysed the associated climate data. The cost of active rehabilitation was quantified to determine the effect of investment on rehabilitation success.</p>
Coversion model and dataset of pressure sensors from voltage to pressure in SocketSense Project WP3
<p>This is the conversion model to convert data from voltage to pressure in SocketSense Project WP3</p>
Projection image of self-designed CT
<p>A projection data from my self-designed CT.</p>
Database of biochemistry data of intensive fattening lambs of sheep breeds raised in Latvia in 2022 ithin the framework of project of the Latvian Council of Science: LZP-2021/1-0489
<p>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><strong>The 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 largest 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 amount of feed intake) is a key factor in the sheep industry. Improving Feed efficiencyreduces production costs. Improving Feed efficiencyby 5% can bring economic benefits that are 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, as well as 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 analyze animal-to-animal variation in live lambs could be used as a cost-effective and rapid tool for genetic selection or management decisions.</p> <p>The endocrine system could affect Feed efficiency through its role in regulating feed intake and nutrient utilization. The gastrointestinal tract with attached glands is the source of secretes hormones that play an important role in regulating feed intake (such as insulin and ghrelin) and nutrient use (i.e., glucose-dependent insulinotropic polypeptide, insulin and IGF-1). Existing data indicate specific hormones related to nutrient metabolism may have a role in divergent feed efficiency status and are studied in more detail.</p> <p>Considering the fact that heat is generated as a side product of energy metabolism, Feed efficiency correlates inversely with heat production. Physiological indicators of FE such as hematocrit, hemoglobin and glucose are useful blood traits to identify efficient animals.</p> <p><strong>About the data of the project:</strong></p> <p>LZP-2021/1-0489 project data on lamb samples of the year 2022, or A22 group, which consists of 76 intensively fattened lambs from six breeds and nine samples from 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, Glucose, and Leptin. Data on Leptin are only for the first 50 samples because the data wasn’t different, and the level was too low for the detection method.</p> <p>Based on the requirements of the breeding program of the breeds, every year, the offspring of the sire ram, certified for breeding activity, are selected and analyzed to estimate the sire rams. 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 Latvian Sheep Breeders' Association at the ram breeding control station. Lambs were fattened for 66.38 ± 1.27 days with an interval of 44 to 83 days.</p> <p> </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 feed efficiency indicators 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 project
<p>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><strong>The 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 largest 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 amount of feed intake) is a key factor in the sheep industry. Improving FE reduces production costs. Improving FE by 5% can bring economic benefits that are 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, as well as 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 analyze animal-to-animal variation in live lambs could be used as a cost-effective and rapid tool for genetic selection or management decisions.</p> <p><strong>About the data of the project:</strong></p> <p>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.</p> <p>The dataset includes data on feed efficiency indicators: (1) ratio traits: Feed efficiency (FE), Feed conversion ratio (FCR), Relative growth rate (RGR), Kleiber’s ratio (KR), or (2) regression or residual traits: Residual feed intake (RFI), Residual weight gain (RWG) and Residual intake and body weight gain (RIG). Calculations were made by using formulas from Lima et al., 2017 (<a href="https://doi.org/10.1590/S1806-92902017001000005">https://doi.org/10.1590/S1806-92902017001000005</a>).</p> <p>Based on the requirements of the breeding program of the breeds, every year, the offspring of the sire ram, certified for breeding activity, are selected and analyzed to estimate the sire rams. 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 Latvian Sheep Breeders' Association at the ram breeding control station. Lambs were fattened for 66.38 ± 1.27 days with an interval of 44 to 83 days.</p> <p> </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 parameters related to intensive fattening of lambs of sheep breeds raised in Latvia in 2022 within the framework of 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 Feed efficiency 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 largest 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 amount of feed intake) is a key factor in the sheep industry. Improving Feed efficiency reduces production costs. Improving Feed efficiency by 5% can bring economic benefits that are 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, as well as 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 analyze animal-to-animal variation in live lambs could be used as a cost-effective and rapid tool for genetic selection or management decisions.</p> <p><strong>About the data of the project:</strong></p> <p>LZP-2021/1-0489 project data on lamb samples of the year 2022, or A22 group, which consists of 76 intensively fattened lambs from six breeds and nine samples from the LT breed, which were raised in the meadow and are semi-sibi lambs within the scope of the study.</p> <p>Based on the requirements of the breeding program of the breeds, every year, the offspring of the sire ram, certified for breeding activity, are selected and analyzed to estimate the sire rams. 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 Latvian Sheep Breeders' Association at the ram breeding control station.</p> <p>The database contains data on ultrasonography measurements of lambs during the beginning and end of fattening, intensive fattening data - real 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.</p> <p> </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> <p> </p>
Database of average values of parameters of sire rams of Latvian dark-head breed calculated from values of intensive fattening lambs 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><strong>The 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 Feed efficiency 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 largest 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 amount of feed intake), is a key factor in the sheep industry. Improving Feed efficiency reduces production costs. Improving Feed efficiency by 5% can bring economic benefits that are 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, as well as 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 analyze animal-to-animal variation in live lambs could be used as a cost-effective and rapid tool for genetic selection or management decisions.</p> <p>As of 2018, there were 49.5 thousand ewes and 73 sire rams in Latvia from a flock of 134.29K sheep. In 2018, Latvian Dark-Head (Latvijas tumšgalve; LT) sheep were bred to implement the sheep breeding program in 35 farms, which had a total of 3,752 ewes, while 13 farms had 43 breeding rams. According to the EUROSTAT data, the number of sheep in Latvia in 2018 was 107.29K, but in December 2021 was 90.34K.</p> <p>The Latvian Dark-Head sheep breed was created by crossing local Latvian sheep with Shropshire and Oxfordshire rams imported from Sweden and England. The first pedigree or breeder's book for LT rams was issued in 1939. Currently, the live weight of ewes of the Latvian Dark-Head breed is about 55 – 65 kg, rams - 95 – 120 kg, wool cut 3.5 – 4.5 and 5.0 – 6.0 kg, respectively. The average prolificacy of ewes is 150 - 160%.</p> <p>Breeding of Latvian Dark-Head sheep in Latvia is carried out according to phenotypic and bloodline data, without information about values of feed efficiency indicators or carrying out genetic breeding. Therefore, this study aimed to analyze the Latvian Dark-Head breeds rams according to lambs' feed efficiency values.</p> <p><strong>About the data of the project:</strong></p> <p>LZP-2021/1-0489 project data on Latvian dark-head (LT; Latvijas tumšgalve) of the year 2022 lamb samples, or A22_LT group. In the group were 48 lambs from 13 sire rams.</p> <p>Based on the requirements of the breeding program of the breeds, every year, the offspring of the sire ram, certified for breeding activity, are selected and analyzed to estimate the sire rams. All lambs were born as twins, triplets or quadruplets from different ewes and health status was assessed prior to 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 Latvian Sheep Breeders' Association at the ram breeding control station.</p> <p>The dataset includes average data of each sire ram: (1) ultrasonography measurements of lambs during the beginning and end of fattening, (2) intensive fattening data - real and calculated on the 90th and 150th day, (3) the amount of feed used, (4) slaughter data, (5) biochemical indicators: Insulin-like growth factor 1, Insulin, Total thyroxine, Adrenocorticotropic hormone, Haematocrit, Hemoglobin, Glucose, (6) feed efficiency indicators: Feed efficiency (FE), Feed conversion ratio (FCR), Relative growth rate (RGR), Kleiber’s ratio (KR), Residual feed intake (RFI), Residual weight gain (RWG) and Residual intake and body weight gain (RIG).</p> <p> </p> <p>The data is the joint property of the participants of the LCS project: the University of Latvia and the Latvian University of Life Sciences and Technologies.</p>
Multilevel Interventions for Mental Health in SMEs and Public Workplaces - H-Work EU Project Dataset
<p>This Dataset includes data collected as part of the H-Work project, at timepoints T1-T7. The Data is merged from multiple sites and timepoints, for more information see info file.</p>
Example project
data project of a test Source: Objaverse 1.0 / Sketchfab
Project_001_Castle_No_Textures
Hi i'm Surijan Bakker And this is my first ever Maya creation Source: Objaverse 1.0 / Sketchfab
Projection room full photogrammetry scan
Crystal Palace 1920's Cinema Projection Room square made using the photos dataset provided by Simon Che de Boer (realityvirtual.co) Thanks a lot for sharing!! :-) More info about Crystal Palace https://vimeo.com/132294910 Source: Objaverse 1.0 / Sketchfab
Weekend Projects 06
low poly cars of the 60s and 70s plus a 1930 ford Source: Objaverse 1.0 / Sketchfab
FLEX4H2 project video
Open the record for dataset details and reuse information.
The MI-HEART Project
ClinicalTrials.gov study NCT00013741. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Project to Assess Feasibility of a Rapid Access Metastatic Bone Disease Program
ClinicalTrials.gov study NCT03134404. IPD Sharing: NO. Countries: 0. Publications: 0.
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.