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2,863 results for “Challenge”
COSINE-100: Full dataset challenges the annual modulation signal of DAMA/LIBRA
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A distinct neurogenomic response to a tradeoff between social challenge and opportunity in male sticklebacks (Gasterosteus aculeatus)
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Data from: Island-wide characterization of agricultural production challenges the demographic collapse hypothesis for Rapa Nui (Easter Island)
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Challenges in the early ontogeny of a mutualistic plant: Resource availability and plant defense in juvenile Cecropia ant-plants
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Data from: Treated like dirt: Robust forensic and ecological inferences from soil eDNA after challenging sample storage
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Data from: Counteracting cascades challenge the heterogeneity – stability relationship
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Data from: The comparative biogeography of Philippine geckos challenges predictions from a paradigm of climate-driven vicariant diversification across an island archipelago
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Population genomic and morphological datasets from: An evolutionary mosaic challenges traditional monitoring of a foundation species in a coastal environment - the Baltic Fucus vesiculosus
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GO Tables from/for: Oligogalactolipid production during cold challenge is conserved in early diverging lineages
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Adult presence does not ameliorate juvenile feeding challenges in a leaf-footed bug
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Immune challenges increase network centrality in a queenless ant
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Data from: Immune challenge affects risk sensitivity and locomotion in mosquitofish (Gambusia holbrooki)
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Data from: Resistance to gapeworm parasite has both additive and dominant genetic components in house sparrows, with evolutionary consequences for ability to respond to parasite challenge
<p>Host parasite relationships are likely to change over the coming decades in response to climate change and increased anthropogenic stressors. Understanding the genetic architecture of parasite resistance will aid prediction of species' responses to intensified parasite challenge. The gapeworm "Syngamus trachea" is prevalent in natural bird populations and causes symptomatic infections ranging from mild to severe. The parasite may affect ecological processes by curtailing bird populations and is important due to its propensity to spread to commercially farmed birds. Our large scale dataset on an insular house sparrow metapopulation in northern Norway includes information on gapeworm prevalence and infection intensity, allowing assessment of the genetics of parasite resistance in a natural system. To determine whether parasite resistance has a heritable genetic component, we performed variance component analyses using animal models. Resistance to gapeworm had substantial additive genetic and dominance variance, and genome wide association studies to identify SNPs associated with gapeworm resistance yielded multiple loci linked to immune function. Together with genome partitioning results, this indicates that resistance to gapeworm is under polygenic control in the house sparrow, and likely in other bird species. Hence, our results provide the foundation needed to study any eco-evolutionary processes related to gapeworm infection, and show that it is necessary to use methods suitable for polygenic and non-additive genetic effects on the phenotype.</p>
Beef database for prediction of main environmental challenges to resilience and efficiency in cattle production systems at regional resolution
<p>The beef database comprises average values for a wide range of variables (110 or 119), available in 4 worksheets, and is further differentiated into All Beef, Breeders and Fatteners: BasicFarmType (18 rows), DetailedFarmType (75 rows), ClimateClass+BasicFarmType (270 rows), NUTS+BasicFarmType (2074 rows). Data are omitted when the sample size (n) is below 15, as per the confidentiality agreement under FADN data use rules.</p> <p>A combined farm characterisation database was constructed using two major data sources, the Farm Accountancy Data Network (FADN), and the Gridded Agro-Meteorological Data in Europe (AGRI4CAST). The database initially constructed was further enhanced through the addition of forage and crop yield data from the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) developed Agro-Ecological Zones (AEZ) methodology database (FAO, 2012). The data was processed and is presented in D1.2 as two databases (dairy and beef), as averages for a wide range of variables at basic or detailed farm types, and at NUTS2 regional scale.</p> <p>Detailed FADN data (anonymised individual farm data) was requested for all ruminant and mixed farm types, over 10 years and the most recent data available at request (2011-2013) was utilised for the analysis. Following receipt of the data (~250k farms) this has been compiled into two consistent datasets, one for dairy (141,961) farms and one for beef farms (54,417). Each dataset comprises some values directly from the FADN data, but also a large number of calculated variables, to identify dairy or beef enterprise performance at per animal, per output product unit or per hectare. These values were calculated according to the respective dairy and beef enterprise allocation methodologies described by FADN. Further economic and structural variables have been calculated as necessary, as described in GenTORE D1.1 (Quiédeville et al., 2019).</p> <p>For each farm within the dataset, the structural, production and economic data from the FADN data is supplemented with the addition of meteorological data. The daily meteorological data was downloaded from the AGRI4STAT database web portal at a NUTS2 scale. For each NUTS2 region data was available for a number of weather stations. This large dataset was processed through scripts in STATA software to generate annual values for a wide range of climatic variables, including Temperature Humidity Index (THI), and indicators of drought and seasonality of weather. Furthermore, the altitude values per weather station allowed for a sub-grouping of weather station data by altitude zone (aligned with values available in the FADN dataset).</p> <p>Using a Latent Class Analysis process, the meteorological data was analysed to identify consistent environmental regions in Europe. Selected climatic variables, together with altitude zone, were utilised to statistically identify differing zones, and to classify each NUTS2 region to a zone, resulting in 6 lowland zones and 3 upland zones (above 600m) The LCA process enhanced an earlier method of manually overlaying the Metzger et al. (20054) pedo-climatic zone allocation, but closely correlates. Therefore for each farm in the dairy and beef datasets, meteorological and environmental zone data was allocated on a NUTS2 by altitude zone basis and this dataset has been subsequently assessed and submitted as papers; Quiédeville et al., (submitted May 2020) and Grovermann et al. (submitted May 2020).</p> <p>The GAEZ forage and crop yield data was downloaded from the GAEZ data portal as baseline and two future climate prediction periods: Baseline (1961-2000), 2020s (2011-2040), and 2050s (2041-2070), for the Hadley CM3 model and IPCC scenario A (the most extreme scenario). See: <a href="http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/">http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/#</a>). A zonal statistics was applied to the GAEZ layers to aggregate the data to NUT2 region and altitude zone (0-300m, 300-600m, 600m+) with raster package in R. The result is an average yield<a href="#_ftn1">[1]</a> for varying forages and crops for each altitude zone in each nuts2, for both the baseline and the future climate scenario. This data allows further analysis of the future impacts on cattle farming at both a regional scale, but also by farm type or system, which may be affected differently (Moakes et al. in preparation).</p> <p>All variable processing from FADN data is shown in the Annex, as performed in Stata software.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> The mean was performed on non-zero yield pixels in order to exclude non-suitable areas from average.</p>
Dairy database for prediction of main environmental challenges to resilience and efficiency in cattle production systems at regional resolution
<p>The dairy database comprises average values for a wide range of variables (110 or 119), available in 4 worksheets: BasicFarmType (18 rows), DetailedFarmType (10 rows), ClimateClass+BasicFarmType (100 rows), NUTS+DetailedFarmType (1452 rows). Data are omitted when the sample size (n) is below 15, as per the confidentiality agreement under FADN data use rules.</p> <p>A combined farm characterisation database was constructed using two major data sources, the Farm Accountancy Data Network (FADN), and the Gridded Agro-Meteorological Data in Europe (AGRI4CAST). The database initially constructed was further enhanced through the addition of forage and crop yield data from the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) developed Agro-Ecological Zones (AEZ) methodology database (FAO, 2012). The data was processed and is presented in D1.2 as two databases (dairy and beef), as averages for a wide range of variables at basic or detailed farm types, and at NUTS2 regional scale.</p> <p>Detailed FADN data (anonymised individual farm data) was requested for all ruminant and mixed farm types, over 10 years and the most recent data available at request (2011-2013) was utilised for the analysis. Following receipt of the data (~250k farms) this has been compiled into two consistent datasets, one for dairy (141,961) farms and one for beef farms (54,417). Each dataset comprises some values directly from the FADN data, but also a large number of calculated variables, to identify dairy or beef enterprise performance at per animal, per output product unit or per hectare. These values were calculated according to the respective dairy and beef enterprise allocation methodologies described by FADN. Further economic and structural variables have been calculated as necessary, as described in GenTORE D1.1 (Quiédeville et al., 2019).</p> <p>For each farm within the dataset, the structural, production and economic data from the FADN data is supplemented with the addition of meteorological data. The daily meteorological data was downloaded from the AGRI4STAT database web portal at a NUTS2 scale. For each NUTS2 region data was available for a number of weather stations. This large dataset was processed through scripts in STATA software to generate annual values for a wide range of climatic variables, including Temperature Humidity Index (THI), and indicators of drought and seasonality of weather. Furthermore, the altitude values per weather station allowed for a sub-grouping of weather station data by altitude zone (aligned with values available in the FADN dataset).</p> <p>Using a Latent Class Analysis process, the meteorological data was analysed to identify consistent environmental regions in Europe. Selected climatic variables, together with altitude zone, were utilised to statistically identify differing zones, and to classify each NUTS2 region to a zone, resulting in 6 lowland zones and 3 upland zones (above 600m) The LCA process enhanced an earlier method of manually overlaying the Metzger et al. (20054) pedo-climatic zone allocation, but closely correlates. Therefore for each farm in the dairy and beef datasets, meteorological and environmental zone data was allocated on a NUTS2 by altitude zone basis and this dataset has been subsequently assessed and submitted as papers; Quiédeville et al., (submitted May 2020) and Grovermann et al. (submitted May 2020).</p> <p>The GAEZ forage and crop yield data was downloaded from the GAEZ data portal as baseline and two future climate prediction periods: Baseline (1961-2000), 2020s (2011-2040), and 2050s (2041-2070), for the Hadley CM3 model and IPCC scenario A (the most extreme scenario). See: <a href="http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/">http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/#</a>). A zonal statistics was applied to the GAEZ layers to aggregate the data to NUT2 region and altitude zone (0-300m, 300-600m, 600m+) with raster package in R. The result is an average yield<a href="#_ftn1">[1]</a> for varying forages and crops for each altitude zone in each nuts2, for both the baseline and the future climate scenario. This data allows further analysis of the future impacts on cattle farming at both a regional scale, but also by farm type or system, which may be affected differently (Moakes et al. in preparation).</p> <p>All variable processing from FADN data is shown in the Annex, as performed in Stata software.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> The mean was performed on non-zero yield pixels in order to exclude non-suitable areas from average.</p>
LoDoPaB-CT Challenge Set
<p>Observation data for the <a href="https://lodopab.grand-challenge.org/">LoDoPaB-CT challenge</a>, which asks to reconstruct CT images of the human lung from (simulated) low photon count measurements.</p> <p>The setting is identical to the one of the <a href="https://zenodo.org/record/3384092">LoDoPaB-CT dataset</a> (documented in this <a href="https://www.nature.com/articles/s41597-021-00893-z">Data Descriptor article</a>), which is supposed to be employed for training learned methods. This challenge set contains observations for a separate set of patients.</p> <p>Python utilities for accessing this challenge set and creating the submission file are available at <a href="https://github.com/jleuschn/lodopab_challenge">github.com/jleuschn/lodopab_challenge</a>. The LoDoPaB-CT dataset for training can be accessed using the <a href="https://zenodo.org/record/3970517">DIVal</a> python library (<a href="https://github.com/jleuschn/dival">github.com/jleuschn/dival</a>).</p> <p>Like for the LoDoPaB-CT dataset, reconstructions from the <a href="http://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX">LIDC/IDRI dataset</a> are used as a basis for this challenge set.</p> <p> </p> <p>The ZIP file contains multiple <a href="https://www.hdfgroup.org/HDF5/">HDF5</a> files. Each HDF5 file contains one HDF5 dataset named <code>"data"</code>, that provides a number of samples (128 except for the last file). For example, the <code>n</code>-th observation sample is stored in the file <code>"observation_challenge_%03d.hdf5"</code> where <code>"%03d"</code> is <code>floor(n / 128)</code>, at row <code>(n mod 128)</code> of <code>"data"</code>.</p> <p>For this challenge set no patient IDs are provided (in contrast to the fully public parts of the LoDoPaB-CT dataset), since the reconstruction algorithm should not rely on this information.</p> <p><em>Acknowledgements</em></p> <p>Johannes Leuschner, Maximilian Schmidt and Daniel Otero Baguer acknowledge the support by the Deutsche<br> Forschungsgemeinschaft (DFG) within the framework of GRK 2224/1 “π3: Parameter Identification – Analysis,<br> Algorithms, Applications”. We thank Simon Arridge, Ozan Öktem, Carola-Bibiane Schönlieb and Christian<br> Etmann for the fruitful discussion about the procedure, and Felix Lucka and Jonas Adler for their ideas and<br> helpful feedback on the simulation setup. The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study.</p>
Data from: Scented nectar and the challenge of measuring honest signals in pollination
<p>1. Nectar scents are thought to function as honest signals of reward used by pollinators, but this hypothesis has rarely been tested. 2. Using <i>Penstemon digitalis</i>, we examined honest signalling of the nectar volatile (S)-(+)-linalool and pollinator responses to linalool in both field and laboratory settings. Because our previous work showed that linalool emission was associated with higher female fitness and that nectar is scented with linalool, we hypothesized that linalool was an honest signal of nectar reward. To assess honesty, we measured linalool-nectar associations including nectar volume, sugar amount, concentration, and production rate for inflorescences and flowers in several populations. We also assessed whether Bombus impatiens, the main pollinator of P. digitalis at our sites, can use linalool as a foraging signal. We supplemented real or artificial flowers in the field and lab with varying linalool-nectar combinations to measure pollinator behavioural responses. 3. We found that an inflorescence's linalool emissions could be used to predict nectar rewards in P. digitalis, but this was driven by indirect associations with display size rather than directly advertising more profitable flowers. For flowers within inflorescences there was also no evidence for an association between signal and reward. Field tests of bumblebee behaviour were inconclusive. However, in laboratory assays bumblebees generally used variation in linalool emissions to choose more profitable flowers, demonstrating they can detect differences in linalool emitted by P. digitalis and associate them with reward profitability. These results suggest experiments that decouple display size, scent and reward are necessary to assess whether (and when) bees prefer higher linalool emissions. Bees preferred nectars with lower linalool concentrations when linalool flavoured the nectar solution, suggesting potential for conflicting pressures on scent emission in the field. 4. Synthesis: Our results highlight the challenges of assessing function for traits important to fitness and suggest that the perception of floral signalling honesty may depend on whether pollinators use inflorescences or flowers within inflorescences when making foraging decisions. We conclude that future research on honest signalling in flowering plants, as well as its connection to phenotypic selection, should explicitly consider among- and within-inflorescence honesty, in theoretical and experimental contexts.</p>
Reliably predicting pollinator abundance: challenges of calibrating process-based ecological models
<p>1. Pollination is a key ecosystem service for global agriculture but evidence of pollinator population declines is growing. Reliable spatial modelling of pollinator abundance is essential if we are to identify areas at risk of pollination service deficit and effectively target resources to support pollinator populations. Many models exist which predict pollinator abundance but few have been calibrated against observational data from multiple habitats to ensure their predictions are accurate.</p> <p>2. We selected the most advanced process-based pollinator abundance model available and calibrated it for bumblebees and solitary bees using survey data collected at 239 sites across Great Britain. We compared three versions of the model: one parameterised using estimates based on expert opinion, one where the parameters are calibrated using a purely data-driven approach and one where we allow the expert opinion estimates to inform the calibration process.</p> <p>3. All three model versions showed significant agreement with the survey data, demonstrating this model's potential to reliably map pollinator abundance. However, there were significant differences between the nesting/floral attractiveness scores obtained by the two calibration methods and from the original expert opinion scores.</p> <p>4. Our results highlight a key universal challenge of calibrating spatially-explicit, process-based ecological models. Notably, the desire to reliably represent complex ecological processes in finely mapped landscapes necessarily generates a large number of parameters, which are challenging to calibrate with ecological and geographical data that is often noisy, biased, asynchronous and sometimes inaccurate. Purely data-driven calibration can therefore result in unrealistic parameter values, despite appearing to improve model-data agreement over initial expert opinion estimates. We therefore advocate a combined approach where data-driven calibration and expert opinion are integrated into an iterative Delphi-like process, which simultaneously combines model calibration and credibility assessment. This may provide the best opportunity to obtain realistic parameter estimates and reliable model predictions for ecological systems with expert knowledge gaps and patchy ecological data.</p>
Assessing a Multidisciplinary Group of Undergraduate Students Applying the Challenge Based Learning Methodology to Learn Mobile Development
<p>That video presents the paper entitled "Assessing a Multidisciplinary Group of Undergraduate Students Applying the Challenge Based Learning Methodology to Learn Mobile Development" accepted in SBES 2020 conference (Education track).</p>
Teaching and learning in ecology: a horizon scan of emerging challenges and solutions
<p>We currently face significant, anthropogenic, global environmental challenges and therole of ecologists in mitigating these challenges is arguably more important than ever. Consequently there is an urgent need to recruit and train future generations of ecologists, both those whose main area is ecology, but also those involved in the geological, biological and environmental sciences. Here we present the results of a horizon scanning exercise that identified current and future challenges facing the teaching of ecology, through surveys of teachers, students and employers of ecologists. Key challenges identified were grouped in terms of the perspectives of three groups: students, for example the increasing disconnect between people and nature; teachers, for example the challenges associated with teaching the quantitative skills that are inherent to the study of ecology; and society, for example poor societal perceptions of the field of ecology. In addition to the challenges identified, we propose a number of solutions developed at a workshop by a team of ecology teaching experts, with supporting evidence of their potential to address many of the problems raised. These proposed solutions include developing living labs, teaching students to be ecological entrepreneurs and influencers, embedding skills-based learning and coding in the curriculum, an increased role for learned societies in teaching and learning, and using new technology to enhance fieldwork studies including virtual reality, artificial intelligence and realtime spoken language translation. Our findings are focused towards UK higher education, but they should be informative for students and teachers of a wide range of educational levels, policy makers and professional ecologists worldwide.</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.