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271 results for “ecological research”
Systematic Review on the Research Topic "Learning Ecologies" - Dataset and Analysis
<p>The concept of learning ecologies (Barron, 2004, 2006; Williams, 2011) emerged in a context of educational change. In such context, there was emphasis on searching models and applications facing the new forms of learning as continuum from structured, formal spaces to fluid, informal spaces, with the support of digital technologies (Sangra, Gonzalez-Sanmamed, & Guitert, 2013; Tabuenca, Ternier, & Specht, 2013).</p> <p>While the construct “learning ecologies” has offered a broad semantic space to characterize innovative ways of learning, it is also true that its potential to promote innovative educational interventions could have been hindered by its same broadness. According to this assumption, the authors have carried out a systematic review of the literature with the aim of analyzing a) the several definitions given to the concept, encompassing the underlying ontological perspective on the phenomena studied; b) the methodological approaches adopted to study the phenomenon; c) the applications of the research on this topic. Throughout this analysis, the authors attempt to represent the criticalities of the existing research, as well as the potential areas of development with fruitful alignment of the theoretical/ontological issues, the methodological approaches and the educational applications.</p> <p>The current dataset introduces all the stages of data analysis: from the 337 articles identified (covering the period 1991-2018, containing the key term “learning ecolog*” and selected from 5 relevant scientific databases in the area of education: SCOPUS, ISI WOS, DOAJ, EDITLIB, ERIC) to the final group of 84 papers which were analyzed.</p> <p>Each article was coded according to a set of 20 categories developed by the authors on theoretical basis.</p> <p>The dataset presents:</p> <p>A) The Codebook with the categories used to code each article </p> <p>B) The first, raw selection of articles from each database</p> <p>C) The second integrated dataset will the articles merged, without overlaps</p> <p>D) The third final database where the articles were coded; each row reprents an article. Data filters have been applied to facilitate dynamic searches and explore the categories.</p> <p>E) The PRISMA workflow, with the data extracted from A,B,C</p> <p>F) The tables adopted to make the Kappa calculations</p> <p>G) Some pivot tables with data analysis extracted D</p> <p> </p>
Pervasive gaps in Amazonian ecological research
<p>Raw data and code for the analyses on research probability across the Brazilian Amazonia. Briefly, the code reproduces the Random Forest models and the intersection of research probability with susceptibility to current and future anthropogenic disturbances, performs a site-level analysis of the resulting outputs; and then illustrates the building of figures used in the main manuscript.</p> <p>The modelling framework was built using the directory structure informed in the README.pdf file. The R-code provided has steps designed to replicate the directory structure as reported, but understanding it is a good starting point to navigate the output produced. There are five zip files whose content we described below:</p> <p><strong>AvgOutputs.zip:</strong> a folder containing four files with the results layer from the research probability model. The files represent the final result for each habitat type and the average across habitat types.</p> <p><strong>Datasets.zip</strong>: represents the "Datasets" folder directory, as illustrated in the README.pdf. This zip file contains the files ‘SamplingSites.csv’, which informs metadata for the entire list of occurrence data of community data (organism group, coordinates (long and lat) and habitat); and the ‘VarImportance.csv’, which was produced in the Script2_RCode_RandomForestModels.R, and used to build Figure 3 in the manuscript.</p> <p><strong>GlobalChangeLayers.zip</strong>: folder with input and intermediary layers used for generating Figure 4. Degradation files are from Lapola et al. (2023, DOI: <a href="https://doi.org/10.1126/science.abp8622">10.1126/science.abp8622</a>), while climate layers are from IPCC interactive atlas (Gutiérrez et al. 2021, DOI: <a href="https://dx.doi.org/10.1017/9781009157896.021">10.1017/9781009157896.021</a>.).</p> <p><strong>Predictors.zip</strong>: includes the raster files produced for the "Predictors" folder. There are five rasters representing the layers: DryMonths, LandTenure, NearbtDND, ResearchEduc, and TravelTime. All rasters were generated at the spatial resolution of 1 km spatial resolution using the files in Script1_GEECode_PredictorLayerPreparation.zip. </p> <p><strong>Projections.zip:</strong> the projections of research probability for each habitat type and organism are contained in this folder. There are 11 files, named as aquatic_benthos.tif, aquatic_fishes.tif, aquatic_heteropterans.tif, aquatic_macrophytes.tif, aquatic_odonates.ti, upland_ants.tif, upland_beetles.tif, upland_birds.tif, upland_trees.tif, wetland_birds.tif, wetland_trees.tif.</p> <p><strong>RasterMasks.zip</strong>: contains the masks created for each habitat to remove areas outside each habitat and areas without forest. The files are aquatic.tif, upland.tif and wetland.tif. They were created in the Google Earth Engine platform</p> <p><strong>RData.zip:</strong> represents the "RData" folder directory, which is designed to store RData files produced while running the R-scripts. It currently includes three files to facilitate the plotting of main figures.</p> <p>Scripts: a set of seven files, described in extra details in the following.</p> <ul> <li> <p><strong>Script1_GEECode_PredictorLayerPreparation.zip</strong>: a set of three GEE-scripts developed to prepare raster masks and predictor layers in the Google Earth Engine platform.</p> </li> <li> <p><strong>Script2_RCode_RandomForestModels.R</strong>: R-script to perform data partition, Random Forest model training, model validation, and extraction of variable importance and partial effects per predictor.</p> </li> <li> <p><strong>Script3_RCode_DeltaClimate.R</strong>: R-script to compute the metric of absolute climate change.</p> </li> <li> <p><strong>Script4_RCode_Figure1.R</strong>: R-script to build graphical pieces composing the Figure 1.</p> </li> <li> <p><strong>Script5_RCode_Figure2.R:</strong> R-script to build the Figure 2.</p> </li> <li> <p><strong>Script6_RCode_Figure3.R:</strong> R-script to build graphical pieces composing the Figure 3.</p> </li> <li> <p><strong>Script7_RCode_Figure 4.R</strong>: R-script to build graphical pieces composing the Figure 4.</p> </li> </ul> <p><strong>Shapefiles.zip:</strong> represents the "Shapefiles" folder directory, as illustrated in the README.pdf. There is one shapefile in this zip, which corresponds to the study area limits according to Bullock et al. (2020, DOI: 10.1111/gcb.15029).</p> <p> </p>
Data from: A remote-controlled observatory for behavioural and ecological research: a case study on emperor penguins
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Data from: A social-ecological database to advance research on infrastructure development impacts in the Brazilian Amazon
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Disconnects between ecological theory and data in phenological mismatch research
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Data from: Through the eye of a Gobi khulan – application of camera collars for ecological research of far-ranging species in remote and highly variable ecosystems
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Data from: The geographical and institutional distribution of ecological research in the tropics
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Data from: Visualizing connectivity of ecological and evolutionary concepts – an exploration of research on plant species rarity
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Data from: X-ray computed tomography and its potential in ecological research: a review of studies and optimization of specimen preparation
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Regionalized dynamic climate series for ecological climate impact research in modern controlled environment facilities
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Cultivating scientific literacy and a sense of place through course-based urban ecology research
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Micro-personality traits and their implications for behavioural and movement ecology research
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Data from: Improper data practices erode the quality of global ecological databases and impede the progress of ecological research
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Fig 3 from: Bam A, Addison P, Conlong D (2020) Acridid ecology in the sugarcane agro-ecosystem in the Zululand region of KwaZulu-Natal, South Africa. Journal of Orthoptera Research 29(1): 9-16. https://doi.org/10.3897/jor.29.34626
Fig 3 Population survey showing the relative abundance of the five most prominent acridid species in sugarcane in relation to the damage rating index on the secondary y axis.
Fig 5 from: Bam A, Addison P, Conlong D (2020) Acridid ecology in the sugarcane agro-ecosystem in the Zululand region of KwaZulu-Natal, South Africa. Journal of Orthoptera Research 29(1): 9-16. https://doi.org/10.3897/jor.29.34626
Fig 5 Association between grasshopper species and habitat. Correspondence analysis showing the association between grasshopper species in relation to sugarcane and grassland survey sites sampled over a seven-week period from 21 November 2012–19 February 2013.
Fig 4 from: Bam A, Addison P, Conlong D (2020) Acridid ecology in the sugarcane agro-ecosystem in the Zululand region of KwaZulu-Natal, South Africa. Journal of Orthoptera Research 29(1): 9-16. https://doi.org/10.3897/jor.29.34626
Fig 4 Mean abundance (± SE) of six species of grasshoppers surveyed at the four sugarcane sites and the two grassland sites for the period 21 November 2012–19 February 2013.
Fig 2 from: Bam A, Addison P, Conlong D (2020) Acridid ecology in the sugarcane agro-ecosystem in the Zululand region of KwaZulu-Natal, South Africa. Journal of Orthoptera Research 29(1): 9-16. https://doi.org/10.3897/jor.29.34626
Fig 2 Rank abundance plot of the five most prominent acridid species found in sugarcane in Zululand, South Africa (1: Petamella prosternalis; 2: Nomadacris septemfasciata; 3: Cataloipus zuluensis; 4: Cyrtacanthacris aeruginosa; 5: Ornithacris cyanea), based on population surveys carried out from May 2012 to May 2013 in four study sites.
Fig 1 from: Bam A, Addison P, Conlong D (2020) Acridid ecology in the sugarcane agro-ecosystem in the Zululand region of KwaZulu-Natal, South Africa. Journal of Orthoptera Research 29(1): 9-16. https://doi.org/10.3897/jor.29.34626
Fig 1 Aerial view of four farms where surveys took place indicating the five 100 m transects per farm (red lines). Yellow lines indicate the two survey areas in natural habitats. A. Tedder (Magazulu) farm; B. Crystal Holdings; C. GSA farm; and D. Jengro.
Figure 3 from: Müller C, Bräutigam A, Eilers EJ, Junker RR, Schnitzler J-P, Steppuhn A, Unsicker SB, van Dam NM, Weisser WW, Wittmann MJ (2020) Ecology and Evolution of Intraspecific Chemodiversity of Plants. Research Ideas and Outcomes 6: e49810. https://doi.org/10.3897/rio.6.e49810
Figure 3 Scheme of the collaborative ring trial within the RU. For details see text. C – control; H – herbivore-treated.
Figure 1 from: Müller C, Bräutigam A, Eilers EJ, Junker RR, Schnitzler J-P, Steppuhn A, Unsicker SB, van Dam NM, Weisser WW, Wittmann MJ (2020) Ecology and Evolution of Intraspecific Chemodiversity of Plants. Research Ideas and Outcomes 6: e49810. https://doi.org/10.3897/rio.6.e49810
Figure 1 Conceptual framework of the proposed RU on the ecology and evolution of intraspecific plant chemodiversity. We will study chemical variation in different plant parts (flowers, nectar, pollen, leaves, phloem sap; roots will be included in a potential second funding period), among plant individuals within populations and among populations (left) as well as consequences on the plant-associated community (right) over space and time. The projects will focus on the tree Populus nigra and the herbs Solanum dulcamara and Tanacetum vulgare (lower panel, from left to right).
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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.