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79 results for “Data definition”
Replication code and data for: Recalculating ... How Uncertainty in Local Labor Market Definitions Affects Empirical Findings
<p>This repository contains the code and data to replicate all the analyses in our paper "Recalculating ... : How Uncertainty in Local Labor Market Definitions Affects Empirical Findings." Some of the data can also be used in other researchers' analyses to investigate the robustness of their results when they use commuting zones to aggregate or collect data.</p>
Data from: Trait-based formal definition of plant functional types and functional communities in the multi-species and multi-traits context
<p>The concepts of traits, plant functional types (PFT), and functional communities are effective tools for the study of complex phenomena such as plant community assembly. Here, we (1) suggest a procedure formalising the classification of response traits to construct a PFT system; (2) integrate the PFT, and species compositional data to formally define functional communities; and, (3) identify environmental drivers that underpin the functional-community patterns.A species–trait data set featuring species pooled from two study sites (Eneabba and Cooljarloo, Western Australia), both supporting kwongan vegetation (sclerophyllous scrub and woodland communities), was subjected to classification to define PFTs. Species of both study sites were replaced with the newly derived PFTs and projected cover abundance-weighted means calculated for every plot. Functional communities were defined by classifications of the abundance-weighted PFT data in the respective sites. Distance-based redundancy analysis (using the abundance-weighted community and environmental data) was used to infer drivers of the functional community patterns for each site.A classification based on trait data assisted in reducing trait-space complexity in the studied vegetation and revealed 26 PFTs shared across the study sites. In total, seven functional communities were identified. We demonstrate a putative functional-community pattern-driving effect of soil-texture (clay—sand) gradients at Eneabba (42% of the total inertia explained) and that of water repellence at Cooljarloo (36%). Synthesis. This paper presents a procedure formalising the classification of multiple response traits leading to the delineation of PFTs and functional communities. This step captures plant responses to stresses and disturbance characteristic of kwongan vegetation, including low nutrient status, water stress, and fire (a landscape-level disturbance factor). Our study is the first to introduce a formal procedure assisting their formal recognition. Our results support the role of short-term abiotic drivers structuring the formation of fine-scale functional community patterns in a complex, species-rich vegetation of Western Australia.</p>
Output data from: From land productivity trends to land degradation assessment in Mozambique: Effects of climate, human activities and stakeholder definitions
<p>This repository includes output data from the following article:</p> <p><strong>Montfort, F., Bégué, A., Leroux, L., Blanc, L., Gond, V., Cambule, A.H., Remane, I.A.D., Grinand, C., 2020. From land productivity trends to land degradation assessment in Mozambique: Effects of climate, human activities and stakeholder definitions. <em>Land Degrad Dev</em>.; 32: 49– 65. <a href="https://doi.org/10.1002/ldr.3704">https://doi.org/10.1002/ldr.3704</a></strong></p> <p>This study aimed at characterizing and mapping the underlying factors (human or climatic) in land productivity changes over the 2000-2016 period, in order to assess land degradation in Mozambique.</p> <p>The methodology is based on remote sensing methodology. Land productivity change were first analyzed using MODIS NDVI time-series (2000–2016), and a two-step framework was then used to understand the main factors of these productivity changes, using climate times series (CHIRPS data for rainfall and CRU data for temperature), Land Use and Land Cover Change (LULCC) maps (Laurel project LULCC map), and ground knowledge.</p> <p>This repository includes raster data of annual land productivity change over the 2000-2016 period, annual land productivity climate factors, potential land productivity decrease factors and potential productivity increase factors. Data are available as GeoTIFF raster files at 250 m resolution in the UTM 37S projection (EPSG: 32737).</p>
Supplementary material 2: Definitions and Concepts from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Definitions and concepts in the context of the main paper.
Supplementary data for ENACT: End-to-End Analysis and Cell Type Annotation for Visium High Definition (HD) Slides
<p>This project contains the datasets used to evaluate and reproduce the results of ENACT (End-to-End Analysis and Cell Type<br>Annotation for Visium HD Slides). The dataset consists of:</p> <ul> <li>a sample Visium HD sample of human colorectal cancer, courtesy of 10X Genomics (<a href="https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-human-crc">Visium HD Spatial Gene Expression Library, Human Colorectal Cancer (FFPE) - 10x Genomics</a>). All credit goes to 10X Genomics.</li> <li>configuration files to be used to reproduce the results provided in the ENACT publication, </li> <li>evaluation plots used in the ENACT publication, and</li> <li>results obtained after running ENACT on the human colorectal cancer sample using the four bin-to-cell assignment methods (naive, weighted_by_area, weighted_by_transcript, and weighted_by_cluster) and the three cell annotation methods (Sargent, CellAssign, CellTypist)</li> </ul> <p>Additionally, results from running ENACT on the following three public VisiumHD samples are provided to showcase ENACT’s tissue-agnostic nature:</p> <div> <div> <div> <ul> <li> <p><a href="https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-human-lung-cancer-post-xenium-expt">Human Lung FFPE</a> sample from a subject with Adenocarcinoma (age and gender unspecified),</p> </li> <li> <p><a href="https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-human-tonsil-fresh-frozen">Human Tonsil Fresh Frozen</a> sample from a 21 year old male subject with Reactive Follicular Hyperplasia,</p> </li> <li> <p><a href="https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-human-breast-cancer-fresh-frozen">Human Breast Fresh Frozen</a> sample from a 58 year old female subject with Ductal Carcinoma in Situ (DCIS).</p> </li> </ul> </div> </div> </div> <p> </p>
Data and code for: Does the definition of a novel environment affect the ability to detect cryptic genetic variation?
<p>Anthropogenic change exposes populations to environments that have been rare or entirely absent from their evolutionary past. Such novel environments are hypothesised to release cryptic genetic variation, a hidden store of variance that can fuel evolution. However, support for this hypothesis is mixed. One possible reason is a lack of clarity in what is meant by 'novel environment', an umbrella term encompassing conditions with potentially contrasting effects on the exposure or concealment of cryptic variation. Here, we use a meta-analysis approach to investigate changes in the total genetic variance of multivariate traits in ancestral versus novel environments. To determine whether the definition of a novel environment could explain the mixed support for a release of cryptic genetic variation, we compared absolute novel environments, those not represented in a population's evolutionary past, to extreme novel environments, those involving frequency or magnitude changes to environments present in a population's ancestry. Despite sufficient statistical power, we detected no broadscale pattern of increased genetic variance in novel environments, finding the type of novel environment did not explain any significant variation in effect sizes. When effect sizes were partitioned by experimental design, we found increased genetic variation in studies based on broad-sense measures of variance, and decreased variation in narrow-sense studies, in support of previous research. Therefore, the source of genetic variance, not the definition of a novel environment, was key to understanding environment-dependant genetic variation, highlighting non-additive genetic variance as an important component of cryptic genetic variation and avenue for future research.</p>
Digital Twin or Digital Model: An Analysis of Definitions along the Product Lifecycle - Research data
<p>This research data contains the statements of the authors Grieves, Stark and Tao with regard to selected characteristics of Digital Twins. According to these statements different case studies along the product life cycle are classified as Digital Twin or Digital Model.</p> <p>Version 2 added a change in characteristic 2.</p>
High Definition Modular Multilevel Converter (Final Report and Data)
<p>These are the raw data generated in the 2do Joint Experiments sponsored by IRP Wind EU project</p>
Fig. 7. Sketch profile from the locality 10 in New Stratigraphic Data from the Erlian Basin: Implications for the Division, Correlation, and Definition of Paleogene Lithological Units in Nei Mongol (Inner Mongolia)
Fig. 7. Sketch profile from the locality 10 miles southwest of the Camp Margetts (Granger, 1930).
Data from: Trait-based formal definition of plant functional types and functional communities in the multi-species and multi-traits context
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Impact of data density and endmember definitions on long-term trends in ground cover fractions across European grasslands
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Data from: Observation definitions and their implications in machine learning-based predictions of excessive rainfall
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Data from: A graduated nativeness definition applied to the vascular flora of Denmark
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Data and code for: Does the definition of a novel environment affect the ability to detect cryptic genetic variation?
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Data from: Assessment of polytraumatized patients according to the Berlin Definition: Does the addition of physiological data really improve interobserver reliability?
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Data from: Morphometric analysis of the conodont Chiosella timorensis (Nogami) from the early Anisian of Romania and China, and its significance for the definition of the Olenekian-Anisian Boundary
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CFFGKBS Ver. 5 (All Tables) Data Definition - Updated Jan 2020
<p>A compilation of data definition for the Global Knowledge Base for Underutilised Crops.</p>
Data from: Integrating fuzzy logic and statistics to improve reliabile definition of biogeographic regions and transition zones
The present study uses the amphibian species of the Mediterranean Region to develop a consistent procedure based on fuzzy sets with which biogeographic regions and biotic transition zones can be objectively detected and reliably mapped. Biogeographical regionalizations are abstractions of the geographical organization of life on Earth that provide frameworks for cataloguing species and ecosystems, for answering basic questions in biogeography, evolutionary biology and systematics, and for assessing priorities for conservation. On the other hand, limits between regions may form sharply defined boundaries along some parts of their borders, whereas elsewhere they may consist of broad transition zones. The fuzzy set approach provided a heuristic way to analyze the complexity of the biota within an area; significantly different regions were detected whose mutual limits were sometimes fuzzy, sometimes clearly crisp. Most of the regionalizations described in the literature for the Mediterranean Region present a certain degree of convergence when they are compared within the context of fuzzy interpretation, as many of the differences found between regionalizations are located in transition zones, according to our case study. Compared to other classification procedures based on fuzzy sets, the novelty of our method is that both fuzzy logic and statistics are used together in a synergy in order to avoid arbitrary decisions in the definition of biogeographic regions and transition zones.
Data from: Contrasting definitive hosts as determinants of the genetic structure in a parasite with complex life cycle along the Southeastern Pacific
The spatial genetic structure (and gene flow) of parasites with complex life cycles, such as digeneans, has been attributed mainly to the dispersion ability of the most mobile host, which most often corresponds to the definitive host (DH). In this study, we compared the genetic structure and diversity of adult Neolebouria georgenascimentoi in two fish species (DHs) that are extensively distributed along the Southeastern Pacific (SEP). The analysis was based on the cytochrome oxidase subunit I gene sequences of parasites collected between 23°S and 45°S. In total, 202 sequences of N. georgenascimentoi in Pinguipes chilensis isolated from 9 sites and 136 sequences of Prolatilus jugularis from 5 sites were analyzed. Our results showed that N. georgenascimentoi is a species complex that includes three different parasite species; however, in this study, only group 1 and 2 found in P. chilensis and P. jugularis, respectively, were studied because they are widely distributed along the coastline. Group 1 parasites had two common haplotypes with wide distribution and unique haplotypes in northern sites. Group 2 had only one common haplotype with wide distribution and a large number of unique haplotypes with greater genetic diversity. Both groups have experienced recent population expansion. Only group 1 exhibited a genetic structure that was mainly associated with a biogeographic break at approximately 30°S along the SEP. Our finding suggests that host access to different prey (=intermediate hosts) could affect the genetic structure of the parasite complex discovered here. Consequently, difference between these patterns suggests that factors other than DH dispersal are involved in the genetic structure of autogenic parasites.
Explainable Clustering Applied to the Definition of Terrestrial Biomes - data
<p>Data used for analysis in "Explainable Clustering Applied to the Definition of Terrestrial Biome" - using Decision Tree and Clustering techniques to identify biomes.</p> <p>Land surface properties:</p> <ul> <li><strong>TreeCover </strong>- Vegetation Continuous Fields (VCF) collection 6 fractional tree cover from DiMiceli et al. 2015, regridded as per Kelley et al. 2019</li> <li><strong>NonTreeCover </strong>- VCF fractional herb cover</li> <li><strong>Urban </strong>cover from the History Database of the Global Environment, Version 3.1 (HYDE) Klein Goldewijk et al. 2011</li> <li><strong>Crop </strong>cover (from HYDE)</li> <li><strong>Pasture </strong>Cover (from HYDE)</li> <li><strong>PopDen </strong>(population density from HYDE)</li> </ul> <p>Climate:</p> <ul> <li><strong>MAP_CRU </strong>- Mean annual precipitation from version 4.01 of the Climatic Research Unit Time Series high resolution gridded dataset (CRU TS v4.01) (Harris & Jones 2017)</li> <li><strong>MAT </strong>- Mean annual temperature from CRU)</li> <li><strong>MADD_CRU </strong>- Mean annual dry days from CRU - i.e seasonality of rainfall</li> <li><strong>MTWM </strong>- Mean Maximum Temperature of the warmest month from CRU</li> <li><strong>MTCM </strong>- Mean Minumum Temperature of the coldest month from CRU</li> <li><strong>SW1 </strong>- direct downwards SW simulated using the SLASH model using CRU cload cover</li> <li><strong>SW2 </strong>- diffuse downwards SW simulated using the SLASH model using CRU cload cover</li> <li><strong>BurntArea_GFED_four_s </strong>- Burnt area from Global Fire Emissions Database, Version 4.1 (GFEDv4.1) (Van Der Werf et al. 2017)</li> <li><strong>MaxWind</strong><em><strong> </strong></em>(Mean Max Windspeed from CRU-(National Centers for Environmental Prediction (Harris 2019)</li> </ul> <p>Dimiceli, C., Carroll, M., Sohlberg, R., Kim, D. H.,Kelly, M., and Townshend, J. R. G. (2015). Mod44bmodis/terra vegetation continuous fields yearly l3global 250m sin grid v006 (v006).</p> <p>Harris, I. (2019). CRU JRA v1. 1: A forcings dataset ofgridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data, January1901–December 2017, University of East Anglia Climatic Research Unit, Centre for Environmental DataAnalysis.</p> <p>Harris, I. and Jones, P. (2017). CRU TS4. 01: Climatic Re-search Unit (CRU) Time-Series (TS) version 4.01 ofhigh-resolution gridded data of month-by-month vari-ation in climate (Jan. 1901–Dec. 2016).Centre forEnvironmental Data Analysis, 25.</p> <p>Kelley, D. I., Bistinas, I., Whitley, R., Burton, C., Marthews,T. R., and Dong, N. (2019). How contemporary biocli-matic and human controls change global fire regimes</p> <p>Klein Goldewijk, K., Beusen, A., Van Drecht, G., and DeVos, M. (2011). The HYDE 3.1 spatially explicitdatabase of human-induced global land-use changeover the past 12,000 years.Global Ecology and Bio-geography, 20(1):73–86.</p> <p>Van Der Werf, G. R., Randerson, J. T., Giglio, L.,Van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M.,Van Marle, M. J., Morton, D. C., Collatz, G. J., et al.(2017). Global fire emissions estimates during 1997–2016.Earth System Science Data, 9(2):697–720.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.