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6,782 results for “project”
Data from: Developmental nutrition modulates metabolic responses to projected climate change
<ol> <li>Current policy has the world on track to experience around 3°C of warming by 2100. The responses of organisms to our warming world will be mediated by changes in physiological processes, including metabolic rate. Metabolic rate represents the energetic cost of living, and is fundamental to understanding the energy required to sustain populations. Current evidence indicates that animals have a limited capacity to adapt to warmer environments by reducing their metabolic rate. Consequently, animals may be more reliant on metabolic plasticity to ameliorate the thermodynamic effect of rising temperatures on physiological rates. However, metabolic plasticity is influenced by other environmental factors, including the nutritional quality of food. Elevated levels of atmospheric CO<sub>2</sub> are expected to reduce the protein and increase the carbohydrate concentration in plants, but we do not know how this will affect the response of metabolic rate to climate warming.</li> <li>Here we test the interactive effects of developmental dietary protein and carbohydrate concentrations on the metabolic plasticity of adult <i>Drosophila melanogaster</i> in response to a 3°C increase in temperature while accounting for variation associated with body mass and activity (resting metabolic rate).</li> <li>We show that the thermal sensitivity of resting metabolic rate is modulated by developmental nutrition with animals reared on nutritionally poor, low-protein diets showing the greatest increase in resting metabolic rate in response to simulated climate warming. We also show that if the nutritional quality of resources is unaffected by climate change, then temperature-induced increases in resting metabolic rate will be offset by decreases in mass, but the absolute energy requirements of animals will be elevated relative to current conditions despite this. If, on the other hand, temperatures rise and resources become more calorie-dense and carbohydrate-rich, then the resting metabolic rate of animals will remain relatively unchanged, but decreases in mass and activity may drive down the absolute energy requirements of animals.</li> <li>In the absence of evolutionary adaptation, these findings suggest that the combined plastic response of physiological, morphological, and behavioural traits to temperature and nutrition may be an important determinant of the ultimate outcome of climate change for populations.</li> </ol>
Project F2 - Discovering Potential Market for the Integration of Public Transportation and Emerging Shared-Mobility Services
<p>This dataset compiles the results of ridesharing trajectory data aggregation, the analysis result of the transit and ridesharing trip data, and raw data of transit station coordinates and schedules in Chengdu, China. This is part of STRIDE project F2 titled "Discovering Potential Market for the Integration of Public Transportation and Emerging Shared-Mobility Services."</p>
Data set of trials perfomed in Pont Saint Martin Data Center, pilot of CATALYST Project
<blockquote> <p>The zip file contains data sets being the input and/or output of the test case exploited in Pont Saint Martin Data Center, as pilot of CATALYST Project (H2020-EE-2016-2017)</p> </blockquote>
Results of "Sensitivity of Greenland ice sheet projections to spatial resolution in higher-order simulations: the AWI contribution to ISMIP6-Greenland using ISSM"
<p>This archive provides ice sheet model output produced as part of the publication:</p> <p>Rückamp, M., Goelzer, H., and Humbert, A.: Sensitivity of Greenland ice sheet projections to spatial resolution in higher-order simulations: the AWI contribution to ISMIP6-Greenland using ISSM, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-329, in review, 2020.</p> <p>About the data:<br> The archive contains 4 tar-files for the 4 different resolutions. Each tar-file contains Matlab files for the dfferent scenarios. An examplary file structure looks as follows:</p> <p>results =</p> <p> struct with fields</p> <p>mesh_description: {'x=x_EPSG3413(m)' 'y=y_EPSG3413(m)'}<br> inputs_description: {'friction coefficient k^2 (s/m)' 'bedrock (m)'}<br> data_description: {1x13 cell}<br> mesh: [1x1 struct]<br> inputs: [1x1 struct]<br> data: [1x87 struct]<br> model_description: {'output from ISMIP6 AWI-ISSM, simulation: G4000 ctrl_proj, correspondence Martin Rueckamp (AWI): marin.rueckamp@awi.de'}<br> data_citation: {'Rückamp, M., Goelzer, H., and Humbert, A.: Results of "Sensitivity of Greenland ice sheet projections to spatial resolution in higher-order simulations: the AWI contribution to ISMIP6-Greenland using ISSM", Zenodo, https://doi.org/10.5281/zenodo.3992605, 2020.'}<br> model_citation: {'Rückamp, M., Goelzer, H., and Humbert, A.: Sensitivity of Greenland ice sheet projections to spatial resolution in higher-order simulations: the AWI contribution to ISMIP6-Greenland using ISSM, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-329, in review, 2020.'}</p> <p>The fields results.[mesh,input,data]_description contain header information. The field results.mesh contains [x,y]-coordinates of the grid. The field results.inputs contains time-independent quantities such as bedrock. The field results.data contains results of the transient simulations, such as ice thickness or surface velocity. The fields results.[model,data]_citation contain information about data citation.</p>
All Fish catch data at the SAFE project 2011-2019
<b>Description: </b><p>All fish catch data of all methods across all streams sampled at the SAFE project, Danum Valley and surrounding oil palm areas. Please note this is the raw dataset and may contain errors that need correcting/editing. I have tried to correct the majority of errors, but all were corrected in seperate files prior to analysis, so please see specific datasets in relation to specific papers already published. All changes are detailed in the notes column or listed here if more general. <br><br>All Rasbora caught in 2013 are labelled as cf. sumatrana as they were not differentiated from R. hubbsi. All Systomus changed to Barbodes. All Tor labelled as T. tambra, with no differentiation between tambra and tambroides. From 2018 onwards, all R. sumatrana have been renamed as R. pycnopeza. New captures were not pit tagged. Fish mortality are tracked in the data. <br><br>In 2018, some sites were sampled twice. <br><br>In this version, several changes and new spreadsheets have been added on top of previous versions. This includes: 1. Electrofishing setup - the voltage used in electrofishing corresponding to the conductivity measured; 2. Stream _variables along transect- detailed environmental data collect along transect at each site; 3. Riparian survey, with data on the riparian surveys along the fishing transect. The sheet "Data" has been renamed Capture_Data. Environmental data 2017 sheet has been renamed and updated to Environmental Data site level, to include summarized data by site and by year.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/57"><b>Composition and abundance of tropical freshwater vertebrate communities across a land use gradient</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=4072959">here</a></p><p><b>Files: </b>This consists of 1 file: RawDataFish_with_Riparian_V3.0.xlsx</p><p><b>RawDataFish_with_Riparian_V3.0.xlsx</b></p><p>This file contains dataset metadata and 6 data tables:</p><ol><li><p><b>Capture_Data</b> (described in worksheet Capture_Data)</p><p>Description: Fish catch data </p><p>Number of fields: 18</p><p>Number of data rows: 18994</p><p>Fields: </p><ul><li><b>Date</b>: Date that stream was sampled (Field type: date)</li><li><b>Stream</b>: Which stream was sampled (Field type: location)</li><li><b>RA.</b>: Which research assistant helped do the work? (Field type: comments)</li><li><b>Researcher</b>: Which researcher conducted the work? (Field type: categorical)</li><li><b>Method</b>: What method was used to catch the fish (Field type: categorical)</li><li><b>Cast_or_Trap_Numer</b>: The cast or trap numer that the fish was caught at (Field type: numeric)</li><li><b>Distance</b>: The distance along the transect in which the fish was caught (Field type: numeric)</li><li><b>Taxon</b>: Species sampled (Field type: taxa)</li><li><b>N</b>: The number of fish caught at that stream, location and date (Field type: numeric)</li><li><b>Pittagnumber</b>: The 10 digit pit tag number (Field type: id)</li><li><b>Recapture</b>: Has this fish been caught before? (Field type: categorical)</li><li><b>Length</b>: Total length of fish (Field type: numeric)</li><li><b>Weight</b>: Weight of fish (Field type: numeric)</li><li><b>Specimen</b>: Was this fish taken as a specimen? (Field type: categorical)</li><li><b>Stable_isotope_Fin</b>: Number of the stable isotope sample of all fin clips if they were taken (Field type: numeric)</li><li><b>Stable_isotope_Muscle</b>: Number of the stable isotope sample of all muscle samples if they were taken (Field type: numeric)</li><li><b>Notes</b>: Notes, includes any data changes, fish deaths etc. (Field type: comments)</li><li><b>Mortality</b>: Mortality - fish deaths (Field type: categorical)</li></ul></li><li><p><b>Electofishing_Setup</b> (described in worksheet Electofishing_Setup)</p><p>Description: The voltage and conductivity used for electrofishing</p><p>Number of fields: 7</p><p>Number of data rows: 60</p><p>Fields: </p><ul><li><b>Stream</b>: Which stream was sampled (Field type: location)</li><li><b>Date</b>: Date that stream was sampled (Field type: date)</li><li><b>Conductivity_of_stream</b>: Stream conductivity (Field type: numeric)</li><li><b>Stream_temperature</b>: Stream water temperature (Field type: numeric)</li><li><b>RunNum</b>: Which electrofishing pass (run) (Field type: numeric)</li><li><b>Voltage</b>: Voltage set on the electrofishing kit (Field type: numeric)</li><li><b>Electrofisher</b>: Data collectors (Field type: id)</li></ul></li><li><p><b>Stream_Variables along transect</b> (described in worksheet Stream_Variables along transect)</p><p>Description: Stream environmental data along fishing transect of each site. Data from 2018 onwards</p><p>Number of fields: 13</p><p>Number of data rows: 131</p><p>Fields: </p><ul><li><b>Stream</b>: Which stream was sampled (Field type: location)</li><li><b>Date</b>: Date measured (Field type: date)</li><li><b>Landuse</b>: Landuse of the stream sampled (Field type: categorical)</li><li><b>Distance_on_stream</b>: Distance of measured stream variables along transect (Field type: numeric)</li><li><b>Flow</b>: Average flow of the stream (Field type: numeric)</li><li><b>Wetted_width</b>: wetted width of the stream (Field type: numeric)</li><li><b>Depth</b>: Average stream depth (Field type: numeric)</li><li><b>sand</b>: Stream substrate (Field type: numeric)</li><li><b>gravel</b>: Stream substrate (Field type: numeric)</li><li><b>pebbles</b>: Stream substrate (Field type: numeric)</li><li><b>large_rocks</b>: Stream substrate (Field type: numeric)</li><li><b>bedrock</b>: Stream substrate (Field type: numeric)</li><li><b>Notes</b>: Notes, includes any data changes, fish deaths etc. (Field type: comments)</li></ul></li><li><p><b>Riparian survey along transect</b> (described in worksheet Riparian_survey_along_transect)</p><p>Description: Riparian surveys of each stream along the 500m transect. Data from 2018 onwards</p><p>Number of fields: 15</p><p>Number of data rows: 352</p><p>Fields: </p><ul><li><b>Stream</b>: Which stream was sampled (Field type: location)</li><li><b>Date</b>: Sampling date (Field type: date)</li><li><b>Landuse</b>: Landuse of the stream sampled (Field type: categorical)</li><li><b>Distance</b>: Distance on the stream from first RT plot point (Field type: numeric)</li><li><b>Upstream</b>: Canopy openness (Field type: numeric)</li><li><b>away_from_stream</b>: Canopy openness (Field type: numeric)</li><li><b>downstream</b>: Canopy opennes (Field type: numeric)</li><li><b>Towards_river</b>: Canopy openness (Field type: numeric)</li><li><b>Average_total_area_not_occupied</b>: Average reading across the 4 (Field type: numeric)</li><li><b>Percent_area_not_occupied</b>: Percent overhead area not occupied by canopy (Field type: numeric)</li><li><b>Overstory_density_</b>: Overstory density - area occupied (Field type: numeric)</li><li><b>Vine_cover</b>: Estimation of vine cover (Field type: numeric)</li><li><b>Forest_quality_score</b>: Visual assessment of the forest quality (Field type: numeric)</li><li><b>Notes</b>: Notes or remarks (Field type: comments)</li><li><b>Ras</b>: Data collectors (Field type: id)</li></ul></li><li><p><b>EnvironmentalData_sitelevel</b> (described in worksheet EnvironmentalData_sitelevel)</p><p>Description: Averaged data of streams on a site level</p><p>Number of fields: 23</p><p>Number of data rows: 51</p><p>Fields: </p><ul><li><b>Stream</b>: Which stream was sampled (Field type: location)</li><li><b>Year</b>: Sampling year (Field type: date)</li><li><b>Landuse</b>: Landuse of the stream sampled (Field type: categorical)</li><li><b>Turbidity</b>: Stream water turbidity (Field type: numeric)</li><li><b>Flow</b>: Average flow of the stream (Field type: numeric)</li><li><b>Wetted_width</b>: Average wetted width of the stream (Field type: numeric)</li><li><b>Depth</b>: Average stream depth (Field type: numeric)</li><li><b>sand</b>: Average stream substrate (Field type: numeric)</li><li><b>gravel</b>: Average Stream substrate (Field type: numeric)</li><li><b>pebbles</b>: Average stream substrate (Field type: numeric)</li><li><b>large_rocks</b>: Average stream substrate (Field type: numeric)</li><li><b>bedrock</b>: Average stream substrate (Field type: numeric)</li><li><b>Littoral.leaf.litter.cover</b>: Average leaf litter on stream bed (Field type: numeric)</li><li><b>pH</b>: Stream pH (Field type: numeric)</li><li><b>Temperature</b>: Stream water temperature (Field type: numeric)</li><li><b>O2</b>: Dissolved oxygen (Field type: numeric)</li><li><b>Conductivity</b>: Stream conductivity (Field type: numeric)</li><li><b>Canopy.openness</b>: Averaged canopy openness above the stream, average from upstream, downstream, left and right bank measurements (Field type: numeric)</li><li><b>Sedimentation</b>: Is there sediment within the stream? Yes or No (Field type: categorical)</li><li><b>CanopyOpennessRiparian</b>: Average canopy openness in the riparian, average from upstream, downstream, away from the stream and towards the stream measurements (Field type: numeric)</li><li><b>Vine</b>: Average % vine cover (Field type: numeric)</li><li><b>FQ</b>: Average SAFE project forest quality score (0-5) (Field type: numeric)</li><li><b>Tree</b>: Average density of trees (Field type: numeric)</li></ul></li><li><p><b>Riparian_OP 2017</b> (described in worksheet Riparian_OP 2017)</p><p>Description: Averaged data by stream of all measured environmental variables for OP streams in 2017</p><p>Number of fields: 24</p><p>Number of data rows: 20</p><p>Fields: </p><ul><li><b>Stream</b>: Which stream was sampled (Field type: location)</li><li><b>Landuse</b>: Landuse of the stream sampled (Field type: categorical)</li><li><b>Turbidity</b>: Stream water turbidity (Field type: numeric)</li><li><b>Flow</b>: Average flow of the stream (Field type: numeric)</li><li><b>Wetted_width</b>: wetted width of the stream (Field type: numeric)</li><li><b>Depth</b>: Average stream depth (Field type: numeric)</li><li><b>sand</b>: Stream substrate (Field type: numeric)</li><li><b>gravel</b>: Stream substrate (Field type: numeric)</li><li><b>pebbles</b>: Stream substrate (Field type: numeric)</li><li><b>large_rocks</b>: Stream substrate (Field type: numeric)</li><li><b>bedrock</b>: Stream substrate (Field type: numeric)</li><li><b>Littoral.leaf.litter.cover</b>: leaf litter on stream bed (Field type: numeric)</li><li><b>pH</b>: Stream pH (Field type: numeric)</li><li><b>Temperature</b>: Stream water temperature (Field type: numeric)</li><li><b>O2</b>: Dissolved oxygen (Field type: numeric)</li><li><b>Conductivity</b>: Stream conductivity (Field type: numeric)</li><li><b>Canopy.openness</b>: Canopy openness above the stream, average from upstream, downstream, left and right bank measurements (Field type: numeric)</li><li><b>Sedimentation</b>: NA (Field type: categorical)</li><li><b>CanopyOpennessRiparian</b>: Canopy openness in the riparian, average from upstream, downstream, away from the stream and towards the stream measurements (Field type: numeric)</li><li><b>Vine</b>: % vine cover (Field type: numeric)</li><li><b>FQ</b>: Average SAFE project forest quality score (0-1) (Field type: numeric)</li><li><b>Tree</b>: Density of trees (Field type: numeric)</li><li><b>DateOPplanted2</b>: Years since oil palm was planted (compared to 2017 sampling date) (Field type: numeric)</li><li><b>Average500_2bank</b>: Average width of riparian buffers as the sum of both sides of the river. Averaged over 11 locations measured on GIS, but with the width of the river removed. (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2011-09-19 to 2020-02-18</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Amphibia <br> -  -  -  Actinopterygii <br> -  -  -  -  Synbranchiformes <br> -  -  -  -  -  Mastacembelidae <br> -  -  -  -  -  -  <i>Mastacembelus</i> <br> -  -  -  -  -  -  -  <i>Mastacembelus unicolor</i> <br> -  -  -  -  -  -  <i>Macrognathus</i> <br> -  -  -  -  -  -  -  <i>Macrognathus keithi</i> <br> -  -  -  -  Anguilliformes <br> -  -  -  -  -  Anguillidae <br> -  -  -  -  -  -  <i>Anguilla</i> <br> -  -  -  -  -  -  -  <i>Anguilla borneensis</i> <br> -  -  -  -  -  -  -  <i>Anguilla marmorata</i> <br> -  -  -  -  Siluriformes <br> -  -  -  -  -  Bagridae <br> -  -  -  -  -  -  <i>Hemibagrus</i> <br> -  -  -  -  -  -  -  <i>Hemibagrus baramensis</i> <br> -  -  -  -  -  -  -  <i>Hemibagrus fortis</i> <br> -  -  -  -  -  Clariidae <br> -  -  -  -  -  -  <i>Clarias</i> <br> -  -  -  -  -  -  -  <i>Clarias anfractus</i> <br> -  -  -  -  -  -  -  <i>Clarias teysmanni</i> <br> -  -  -  -  Cypriniformes <br> -  -  -  -  -  Balitoridae <br> -  -  -  -  -  -  <i>Protomyzon</i> <br> -  -  -  -  -  -  -  <i>Protomyzon borneensis</i> <br> -  -  -  -  -  -  -  <i>Protomyzon griswoldi</i> <br> -  -  -  -  -  -  -  <i>Protomyzon whiteheadi</i> <br> -  -  -  -  -  -  <i>Homalopteroides</i> <br> -  -  -  -  -  -  -  <i>Homalopteroides stephensoni</i> <br> -  -  -  -  -  -  <i>Parhomaloptera</i> <br> -  -  -  -  -  -  -  <i>Parhomaloptera microstoma</i> <br> -  -  -  -  -  -  <i>Gastromyzon</i> <br> -  -  -  -  -  -  -  <i>Gastromyzon ingeri</i> <br> -  -  -  -  -  -  -  <i>Gastromyzon lepidogaster</i> <br> -  -  -  -  -  -  -  <i>Gastromyzon umbrus</i> <br> -  -  -  -  -  Nemacheilidae <br> -  -  -  -  -  -  <i>Nemacheilus</i> <br> -  -  -  -  -  -  -  <i>Nemacheilus olivaceus</i> <br> -  -  -  -  -  Cyprinidae <br> -  -  -  -  -  -  <i>Cyclocheilichthys</i> <br> -  -  -  -  -  -  <i>Luciosoma</i> <br> -  -  -  -  -  -  -  <i>Luciosoma pelligrinii</i> <br> -  -  -  -  -  -  <i>Barbonymus</i> <br> -  -  -  -  -  -  -  <i>Barbodes collingwoodi</i> <br> -  -  -  -  -  -  -  <i>Barbonymus balleroides</i> <br> -  -  -  -  -  -  <i>Rasbora</i> <br> -  -  -  -  -  -  -  <i>Rasbora pycnopeza</i> <br> -  -  -  -  -  -  -  <i>Rasbora elegans</i> <br> -  -  -  -  -  -  -  <i>Rasbora hubbsi</i> <br> -  -  -  -  -  -  <i>Hampala</i> <br> -  -  -  -  -  -  -  <i>Hampala sabana</i> <br> -  -  -  -  -  -  <i>Crossocheilus</i> <br> -  -  -  -  -  -  -  <i>Crossocheilus elegans</i> <br> -  -  -  -  -  -  <i>Leptobarbus</i> <br> -  -  -  -  -  -  -  <i>Leptobarbus melanotaenia</i> <br> -  -  -  -  -  -  <i>Lobocheilos</i> <br> -  -  -  -  -  -  -  <i>Lobocheilos erinaceus</i> <br> -  -  -  -  -  -  -  <i>Lobocheilos unicornis</i> <br> -  -  -  -  -  -  <i>Anematichthys</i> <br> -  -  -  -  -  -  -  <i>Anematichthys repasson</i> (as synonym: <i>Cyclocheilichthys repasson</i>)<br> -  -  -  -  -  -  <i>Nematabramis</i> <br> -  -  -  -  -  -  -  <i>Nematabramis borneensis</i> <br> -  -  -  -  -  -  -  <i>Nematabramis everetti</i> <br> -  -  -  -  -  -  <i>Osteochilus</i> <br> -  -  -  -  -  -  -  <i>Osteochilus chini</i> <br> -  -  -  -  -  -  -  <i>Osteochilus ingeri</i> <br> -  -  -  -  -  -  <i>Tor</i> <br> -  -  -  -  -  -  -  <i>Tor tambra</i> <br> -  -  -  -  -  -  <i>Puntius</i> <br> -  -  -  -  -  -  -  <i>Puntius sealei</i> (as synonym: <i>Barbodes sealei</i>)<br> -  -  -  -  -  -  <i>Garra</i> <br> -  -  -  -  -  -  -  <i>Garra borneensis</i> <br> -  -  -  -  Perciformes <br> -  -  -  -  -  Channidae <br> -  -  -  -  -  -  <i>Channa</i> <br> -  -  -  -  -  -  -  <i>Channa striata</i> <br> -  -  -  -  -  Osphronemidae <br> -  -  -  -  -  -  <i>Betta</i> <br> -  -  -  -  -  -  -  <i>Betta unimaculata</i> <br> -  -  -  -  -  Cichlidae <br> -  -  -  -  -  -  <i>Oreochromis</i> <br> -  -  -  -  -  -  -  <i>Oreochromis mossambicus</i> <br></div><p></p>
Adaptations of Scrum roles in software projects: Survey and Representation Tentative with Feature Models
<p>Vídeo short paper sobre adapatações dos papéis do Scrum</p>
2x6 - a collaborative project in generative literature and translation
<p>5<sup>th</sup> Project Presentation</p>
Data From: Conservation planning in an uncertain climate: identifying projects that remain valuable and feasible across future scenarios
<p>Conservation actors face the challenge of allocating limited resources despite uncertainty about future climate. A key goal is to minimize the potential for negative outcomes under future scenarios. Thus, we address a global conservation challenge: how to allocate conservation investments given high uncertainty about future climate conditions. To that end, we present a method for identifying projects that remain valuable and feasible across climate scenarios and apply our framework to freshwater biodiversity conservation in the South-Central USA. We combine data from a recent high-resolution hydrologic planning tool and species distribution models to estimate the conservation feasibility and biodiversity value of river reaches below 38 major reservoirs in the Red River basin.We find that only 13% of sites have high conservation priority across all future climate scenarios and that spatial patterns of conservation priority largely reflect patterns of water availability and fish biodiversity.</p>
DATS 6103 - Individual Project 2 - Graham Hulsey
<p>Hello! This page contains the results from a Data Mining project I completed for DATS 6103. For this project, I web scraped headlines from 12 major news outlets:</p> <p> </p> <p>1. <a href="https://www.nytimes.com/">New York Times</a><br> 2. <a href="https://www.washingtonpost.com/">Washington Post</a><br> 3. <a href="https://www.cnbc.com">CNBC</a><br> 4. <a href="https://www.aljazeera.com/">Al-Jazeera</a><br> 5. <a href="https://bbc.com/news">BBC</a><br> 6. <a href="https://global.chinadaily.com.cn/">China Daily</a><br> 7. <a href="https://www.foxnews.com/">Fox News</a><br> 8. <a href="https://en.mehrnews.com/">Mehr News</a><br> 9. <a href="https://www.theatlantic.com/">The Atlantic</a><br> 10. <a href="https://www.buzzfeed.com/">Buzzfeed</a><br> 11. <a href="https://www.newyorker.com/">New Yorker</a><br> 12. <a href="https://www.motherjones.com/">Mother Jones</a></p> <p>Using the text data, I wanted to analyze which issues got covered by which outlets, and how news coverage changed in response to major events surround this US election. All graphs and analysis are uploaded with this page. If you would like to download the raw data and code, you can visit my Github page <a href="https://github.com/grahamh39/DATS6103-Project-2-Graham-Hulsey-">here</a>. You can visit my <a href="https://grahamh39.github.io/">github.io page</a> site to view the analysis as well.</p>
Assessment of Compatibility of Mineral Aggregates and Binders Used In Highway Construction and Maintenance Projects
<p>Stripping and delamination have been deemed as some of the major premature pavement distresses to most state Departments of Transportations (DOTs) and highway agencies including the Arkansas Department of Transportation (ARDOT). It is believed that the poor compatibility between asphalt binders and aggregates is one of the major reasons behind this. This study aims to analyze the compatibility of selected asphalt binders and aggregates used in Arkansas. Asphalt binders used in this study include PG 64-22, PG 70-22, and PG 76-22; each prepared from two different crude courses. Additionally, four different types of aggregates (sandstone, limestone, gravel, and dolomite) from four different quarries in Arkansas were evaluated in the laboratory. Selected physical and mechanical properties of the aggregates, rheological properties of the asphalt binders, surface free energy (SFE) measurements of the binders and aggregates, atomic force microscopy (AFM) analyses of binders, and limited laboratory and field performance of asphalt mixture samples were evaluated to determine the compatibility between the asphalt binders and aggregates. The findings of this study are expected to help pavement researchers and highway professionals to find suitable asphalt binder-aggregate combinations for constructing the durable pavements.</p>
Dataset for intercontinental assessment of the educational environmental project "IO-00118-17".
<p>Rams, S., Porcel de Peralta, M. & Odetti, H. (2020). <em>Dataset for intercontinental assessment of the educational environmental project "IO-00118-17".</em> DOI: 10.5281/zenodo.4295551</p>
Previst project; a Swiss campaing within the general population for crime prevention in sex work
<p>In this link, we share private pictures and videos from the campaign "We don't buy a body but a service, part of a crime prevention program in the canton of Vaud (Switzerland). The campaign aimed to prevent antisocial behaviour towards prostitutes by encouraging population's more realistic view of the sex work and by minimising the excuses for crime -the neutralisation techniques as defined by Sykes & Matza (1957).</p> <p>With the slogan “we don’t buy a body but a service”, we installed a panel in the prostitution neighbourhood in Lausanne (the capital of the canton of Vaud). We also organised four artistic performances in the city centre and we advertised the campaign through interviews with the media.</p> <p>Although the impact of this campaign on the behaviour of the population is difficult to assess, we estimated that the number of local citizens reached is non- negligible. We suggest that sex workers should be encouraged to participate in further campaigns towards the general population as well as the academia should collaborate with practitioners for evaluating the impact of such projects.</p>
Database of costs for wave energy projects
<p>The database of costs is a list of costs related to the commercialisation of a wave energy farm. The costs are collected in an Excel sheet divided into categories. This collection of costs is intended to be used in LCoE calculations. The data has been gathered through a thorough literature review.</p>
Repository of Results, Deliverable 2.1 H2020 4RinEU Project
<p>The files reported here form the repository of results provided by a parametric study performed within the Deliverable 2.1 "Geo-clusters and Building Archetypes" of the European H2020 4RinEU Project (https://4rineu.eu/).</p> <p>The repository provides, for the four building typologies in the six geo-clusters considered in 4RinEU, results in the following KPIs thematic area: energy, comfort and environment; besides, economic issues and building site management are implemented in another repository included in Project Deliverable D4.2.</p> <p>The repository consists of four Excel files, one per each building typology: Apartment Block, Multi-Family House, Single Family House, Terraced House. Each row of the file corresponds to one simulation. 1734 simulations are collected per each building typology (289 simulations per each of the 6 geo-cluster, including the PRE-RETROFIT scenario).</p> <p>The use of the repository allows the comparison between different renovation scenarios and it is the starting point for a preliminary analysis on the renovation in the design phase.</p> <p>For more details on the repository structure and 4RinEU Project in general, please visit <a href="https://4rineu.eu/reports/">https://4rineu.eu/reports/</a></p>
data set regarding to project –Polish version of Mini Sarcopenia Risk Assessment (MSRA) Questionnaire
<p><strong>This data set corresponds with the article titled Polish translation and validation of The Mini Sarcopenia Risk Assessment (MSRA) Questionnaire to assess nutritional and non-nutritional risk factors of sarcopenia in older adults.</strong></p>
Official video of the MASTRO project focusing on Intelligent bulk MAterials for Smart TRanspOrt industries
<p>Take a look at our video presenting the concept and objectives of the MASTRO project!</p> <p> For more information visit our website: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbTc3WEE1aEdrUHc5ek1HRmEwSWdWcXZrcTlRUXxBQ3Jtc0ttZzJqYVRWTjZqbFZvTnRuNkFKdDhXNlc0T3hDMVQ2VFZnQjNucVZHVm8xZWtWa21iNWRldmxqSFhNYWphZDRVdndsTVhFTmVxR3dzXzhRbGZTMjdsRmF2NzdwakJOLXJfSlV4dVlVbHlHU3JITzB6TQ&q=https%3A%2F%2Fwww.mastro-h2020.eu%2F">https://www.mastro-h2020.eu/</a> </p>
Novel Science communication – how to build public health knowledge across the social gradient. The Healthy Choices Project (WP5).
<p>Everyday life consists of hundreds of choices of which many may have an impact on our health. The citizens of the modern Nordic welfare states are in general well-educated and thus can be assumed to know what is best for them. Despite this our health care systems face an increasing burden of lifestyle related diseases and there is a difference across the social gradient. Here building public health knowledge through communication is key. The reason for people making unhealthy choices are many and can be that; they do not know what is good for them (lack information) or do not understand (lack understanding) or do not care (carelessness). However it seems likely that it is more complex than that. Maybe they do not receive the information, because gets conveyed through channels that they do not consult or maybe the format of the message does not ‘click’? Could it be, that ‘one media message or channel does not fit all’? </p> <p>Over the last decades there has been an increased focus from both scientists themselves and politicians to scientific and practical field of ‘science communication’. Consequently, various advanced modes of communicating e.g. health issues to the general populations have been developed, and today scholars of Mass Media term this development the ‘scientification’ of media. Furthermore, media and information availability has exploded since the emergence of the Internet in the early 1990s. Research has indicated that social gradient also is a factor affecting a person’s media consumption and habit. </p> <p>In the healthy choices project we aim through novel methods to learn more about these challenges and at the develop new methods of conveying public health knowledge to enable people to make healthier choices across the social gradient. We have so far built a framework for this including a science group, consisting of public health experts, and a communication group consisting of experienced science communicators. The Science group is consulted about risk factors and health endpoints and what to prioritize. Two PhD students will be involved in; 1) assessing media consumption and usage related to health issues across social classes (Low social gradient versus high social gradient) and across the generational gap (young and elderly) and 2) develop and test various media message formats for target groups including considerations of the social gradient. This project will provide new understanding on how to communicate public health messages and reduce social inequalities in health.</p> <p> </p>
Data from: Avoided emissions and conservation of scrub mangroves: a potential Blue Carbon project in the Gulf of California, Mexico
Mangroves are considered ideal ecosystems for Blue Carbon projects. However, because of their short stature, some mangroves ("scrub" mangroves, < 2m) do not fulfil the current definition of "forests" which makes them ineligible for emission reduction programs such as REDD+. Short stature mangroves can be the dominant form of mangroves in arid and poor nutrient landscapes, and emissions from their deforestation and degradation could be substantial. Here, we describe a potential Blue Carbon project in the Gulf of California, Mexico, to illustrate that projects which avoid emissions from deforestation and degradation could provide financial resources to protect mangroves that cannot be included in other emission reduction programs. The goal of the project is to protect 16,058 ha of mangroves through conservation concessions from the Mexican Federal Government. The cumulative avoided emissions of the project are 2.84 million Mg CO2 over 100 years, valued at $US 426,000 per year (US$15 per Mg CO2 in the California market). The funds could be used for community-based projects that will improve mangrove management, such as surveillance, eradication of invasive species, rehabilitation after tropical storms, and environmental education. The strong institutional support, secure financial status, community engagement, and clear project boundaries provide favourable conditions to implement this Blue Carbon project. Financial resources from Blue Carbon projects, even in mangroves of short stature, can provide substantial resources to enhance community resilience and mangrove protection.
Data from: The demographic effects of functional traits: an integral projection model approach reveals population-level consequences of reproduction-defense tradeoffs
Quantitatively linking individual variation in functional traits to demography is a necessary step to advance our understanding of trait-based ecological processes. We constructed a population model for Asclepias syriaca to identify how functional traits affect vital rates and population growth and whether tradeoffs in chemical defense and demography alter population growth. Plants with higher foliar cardenolides had lower fiber, cellulose, and lignin levels, as well as decreased sexual and clonal reproduction. Average cardenolide concentrations had the strongest effect on population growth. In both the sexual and clonal pathway, the tradeoff between reproduction and defense affected population growth. We found that both increasing the mean of the distribution of individual plant values for cardenolides and herbivory decreased population growth. However, increasing the variance in both defense and herbivory increased population growth. Functional traits can impact population growth and quantifying individual-level variation in traits should be included in assessments of population-level processes.
Data from: Early snowmelt projected to cause population decline in a subalpine plant
How climate change influences the dynamics of plant populations is not well understood, as few plant studies have measured responses of vital rates to climatic variables and modeled the impact on population growth. I used 25 years of demographic data to analyze how survival, growth, and fecundity respond to date of spring snow melt for a subalpine plant. Fecundity was estimated by seed production (over 15 years) and also divided into flower number, fruit set, seeds per fruit, and escape from seed predation. Despite no apparent effects on flower number, plants produced more seeds in years with later snowmelt. Survival and probability of flowering were reduced by early snow melt in the previous year. Based on demographic models, earlier snowmelt with warming is expected to lead to negative population growth, driven especially by changes in seedling establishment and seed production. These results provide a rare example of how climate change is expected to influence the dynamics of a plant population. They furthermore illustrate the potential for strong population impacts even in the absence of more commonly reported visual signs, such as earlier blooming or reduced floral display in early melting years.
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.