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Supplemental Material of Relationships between Economic, Legal, Social, and Global Risks with the Success in International Construction Projects at Caribbean Islands with British Influence
<p>Suplemental Material consisting on: 1. Instrument for collecting data and, 2. Survey results of the study.</p>
Video 1 Dissemination strategy WP10 EcoStack (EU2020) project
<p>First video of EcoStack project presentation. In this video Project goals are presented.</p> <p> </p>
Hindsight is 2020: Reviewing How the ORION Project Impacted Open Science
<p><strong>Episode Summary: </strong></p> <p>Season 2 is here! We start by looking back at the last year, and talking for the first time about the Open Science project that we are part of ORION. In particular, we consider the aim and impact of Open Science training and interview two participants: Malte Schäfer from TU Braunschweig and Dr Deirdre Winrow from University College Dublin, from our workshop and our MOOC. </p> <p><strong>Links:</strong></p> <ul> <li><a href="http://tiny.cc/ORIONMOOC2">Enroll in the MOOC 2.0</a></li> <li><a href="https://www.orion-openscience.eu/publications/training-materials">ORION Training Materials</a></li> <li><a href="https://www.orion-openscience.eu/activities/training">ORION Training Workshops</a></li> </ul> <p><strong>Quotes: </strong></p> <p>"He radicalised me"</p> <p>"It gives them space to reflect on Open Science"</p> <p>"The system of science has changed"</p>
Compound Interest: Discussing the EU Open Screen Project at the MDC
<p><strong>Episode Summary</strong></p> <p>In this episode we talk to Dr. Jens Peter von Kries and Dr Katja Herzog from the Max-Delbrück-Center for Molecular Medicine (MDC) about their work on the open access technology platform EU Open Screen Project. </p> <p><strong>Episode Links</strong></p> <p><a href="https://www.mdc-berlin.de/screening-unit">EU Open Screen MDC</a></p> <p><a href="https://www.eu-openscreen.eu/">EU Open Screen </a></p> <p><strong>Quotes</strong></p> <p>"From bench to bedside, this really shows that from academia there is really a chance to help people in the clinics"</p>
MOTIVE - tiMe-OpTimized contextual Information flow on unmanned VEhicles project experimental results
<p>The experimentation data were collected during the 6th Fed4FIRE+ Open Call (https://www.fed4fire.eu/) using the mobile nodes of the w-iLab.t (link) testbed. During this Open Call we proposed to evaluate the performance of an optimization model for temporal control of the transmission of messages from an IoT mobile device. This mechanism is based on a network condition model that transits from favourable to adverse conditions and vice versa. All these transitions are monitored and validated through our system (change detection and optimal stopping). If a network is performing properly then the transmission control can be relaxed to exploit available resources.The main contribution of MOTIVE is to apply a sequential decision-making process (DMP) on IoT devices that leverages the on-line derived network statistics to efficiently control their telemetry measurements transmission.</p> <p>Our data collected from the experimentation using the testbed's devices consists of network related information, packet error rate and latency. We collected data from a completely functional mobile node that operates in a saturated network and five mobile nodes in the same circumstances. Saturated conditions were generated by data produced by twenty static sensors for the duration of the each experiment. On each run we collected more than 2 * 105 samples. The comparative assessment we did was based on five different policies of decision making: i) no-policy model, ii) the heuristic threshold based model in which the transmission of the messages is paused when a specific threshold of quality network is below a threshold, iii) TOCP model which overviews the quality of network in normal mode and gets in pausing mode when a change is detected; the pausing period lasts for a specific threshold and then it is activated again, iv) TOCP-DRP and the v) fair TOCP-DRP which enters in pausing mode if a change is indicated by TOCP decision making model.</p> <p>The datasets include the packet error rate and the latency of each device, for each experiment, and the critical areas found during the experiments (most saturated areas).</p> <p>The datatset includes for each scenario(experiment) the participating devices and the Latency and Packet Error Rate(PER) per policy( NoPolicy, Heuristic, TOCP, TOCPDRP, TOCPDRPFAIR), apart from scenarioA(single node) where only four policies are present, since the TOCPDRPFAIR applies with more that one participating nodes.</p>
Corpus metadata "Features of Timelessness" project
<p>This is the meta data of a full text corpus belonging to a paper accepted at the ADHO Digital Humanities Conference 2020 (https://dh2020.adho.org/)</p>
Data collected from the project "Experimental Investigation of Sediment Stability at Reservoirs on the Rhône River"
<p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------<br> Data collected from the project "Experimental Investigation of Sediment Stability at Reservoirs on the Rhône River"</p> <p>Details on the project including information on the materials and methods applied are provided in the available technical report<br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>A_Bulk_Density<br> - contains the vertical bulk density measurements of all withdrawn sediment cores from the Motz and Logis-Neuf reservoir<br> Motz: MZ08, MZ09, MZ10, MZ11, MZ12, MZ13, MZ14, MZ15, MZ16<br> Logis-Neuf: LN01, LN02, LN03, LN04, LN05, LN06, LN07, LN08, LN09, LN10, LN11, LN12, LN14<br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------<br> --> selected cores for detailed experimental investigations: MZ09, MZ14 and LN10, LN14<br> <br> B_Sediment_Composition_and_ParticleSizes<br> - contains the results of the depth-dependent particle size analyses (distribution curves)<br> - contains the derived sediment composition over core depth<br> <br> C_TOC_and_CEC<br> - contains the results of the Total Organic Carbon (TOC) measurements over core depth<br> - contains the results of the Cation Exchange Capacity (CEC) measurements over core depth</p> <p>D_Sediment_Stability<br> - contains the results of the measured sediment stability over core depth<br> <br> E_Sediment_Erosionrates<br> - contains the results of the measured erosion rates over core depth<br> --> two methods are considered: "last measured erosion rate" and "mean erosion rate"<br> <br> F_Sediment_Erosion_Volumes<br> - contains the raw data of the erosion volume measurements conducted with SETEG/PHOTOSED<br> --> the data is provided separately for each investigated sediment layer over core depth (denoted by "D**")<br> --> the data is evaluated for five considered time intervals dt =15s, dt =30s, dt =60s, dt =100s, dt =120s<br> <br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------</p>
hannahcgunderman/INSC590_DataPreservation: Release for INSC590: Data Preservation Project
<p>This is the version 2 release of the repository for the Data Preservation Project for INSC590: Data Management at the School of Information Sciences at UT-Knoxville.</p>
Ensemble projections elucidate effects of uncertainty in terrestrial nitrogen limitation on future carbon uptake
<p>Simulation output as described in Meyerholt, J., Sickel K., and Zaehle, S., (2020), Ensemble projections elucidate effects of uncertainty in terrestrial nitrogen limitation on future carbon uptake, Global Change Biology, doi:10.1111/gcb.15114</p> <p>Data in the file <a href="https://zenodo.org/api/files/62e7c4a2-96e9-46b8-810f-c4c3c24b06c0/ocn4magicc_carbon_model.nc?versionId=cb25734e-52ce-42fa-b238-9b7192baf4dd">ocn4magicc_carbon_model.nc</a> describe the carbon-only version of the model, <a href="https://zenodo.org/api/files/62e7c4a2-96e9-46b8-810f-c4c3c24b06c0/ocn4magicc_carbon_model.nc?versionId=cb25734e-52ce-42fa-b238-9b7192baf4dd">ocn4magicc_nitrogen_models.nc </a>describe the carbon-nitrogen model outputs.</p>
CoDEC Dataset - Data underlying the paper "A high-resolution global dataset of extreme sea levels, tides and storm surges including future projections "
<p>The world’s coastal areas are increasingly at risk of coastal flooding due to sea-level rise (SLR). We present a novel global dataset of extreme sea levels, the Coastal Dataset for the Evaluation of Climate Impact (CoDEC), which can be used to accurately map the impact of climate change on coastal regions around the world. The third generation Global Tide and Surge Model (GTSM), with a coastal resolution of 2.5 km (1.25 km in Europe), was used to simulate extreme sea levels for the ERA5 climate reanalysis from 1979 to 2017, as well as for future climate scenarios from 2040 to 2100. The validation against observed sea levels demonstrated a good performance, and the annual maxima had a mean bias (MB) of -0.04 m, which is 50% lower than the MB of the previous GTSR dataset. The CoDEC-ERA5 dataset is the successor of GTSR <a href="https://www.nature.com/articles/ncomms11969">(Muis et al., 2016)</a> and is based on the next generation climate and hydrodynamic models. The main improvements are summarized in Table 2 of the accompanying paper <a href="https://www.frontiersin.org/articles/10.3389/fmars.2020.00263/abstract">(Muis et al., 2020)</a>.</p> <p><br> </p>
Data Supplement: U.S. state-level projections of the spatial distribution of population consistent with Shared Socioeconomic Pathways.
<p>These data are to supplement the following in-press publication: </p> <p>Zoraghein, H., and O'Neill B. (2020). U.S. state-level projections of the spatial distribution of population consistent with Shared Socioeconomic Pathways. Sustainability.</p> <p>The data herein were generated using the `population_gravity` model which can be found here: <a href="https://github.com/IMMM-SFA/population_gravity">https://github.com/IMMM-SFA/population_gravity</a></p> <p>CONTENTS:</p> <p><strong>zoraghein-oneill_population_gravity_inputs_outputs.zip</strong></p> <ul> <li>contains a directory for each U.S. state for inputs and outputs</li> <li><strong>inputs</strong> contain the following: <ul> <li><strong><state-name>_<urban or rural>_<yr>_1km.tif: </strong> Urban and Rural population GeoTIF rasters at a 1km resolution <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong><state-name>_mask_short_term.tif: </strong> Mask GeoTIF rasters at a 1km resolution that contain values from 0.0 to 1.0 for each 1 km grid cell to help calculate suitability depending on topographic and land use and land cover characteristics <ul> <li>value per grid cell: values from 0.0 to 1.0 (float) that are generated from topographic and land use and land cover characteristics to inform suitability as outlined in the companion publication</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong><state-name>_<ssp>_popproj.csv: </strong> Population projection CSV files for urban, rural, and total population (number of humans; float) for SSPs 2, 3, and 5 for years 2010-2100</li> <li><strong><state-name>_coordinates.csv: </strong>CSV file containing the coordinates for each 1 km grid cell within the target state. File includes a header with the fields XCoord, YCoord, FID.,Where data types and field descriptions are as follows: (XCoord, float, X coordinate in meters),(YCoord, float, Y coordinate in meters),(FID, int, Unique feature id)</li> <li><strong><state-name>_within_indices.txt: </strong>text file containing a file structured as a Python list (e.g. [0, 1]) that contains the index of each grid cell when flattened from a 2D array to a 1D array for the target state.</li> <li><strong><state-name>_<ssp>_params.csv: </strong>CSV file containing the calibration parameters (alpha_rural, beta_rural, alpha_urban, beta_urban; float) for the `population_gravity` model for each year from 2010-2100 in 10-year time-steps as described in the companion publication</li> </ul> </li> <li><strong>outputs</strong> contain the following: <ul> <li><strong>jones_oneill</strong> directory; these are the comparison datasets used to build Figures 7 and 8 in the companion publication <ul> <li>contains three directories: SSP2, SSP3, and SSP5 that each contain a GeoTIF representing total population (number of humans; float) at 1km resolution for years 2050 and 2100. <ul> <li><strong><state-name>_1km_<ssp>_total_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>model</strong> directory; these are the model outputs from `population_gravity` for SSP2, SSP3, and SSP5 that each contain a GeoTIF representing urban, rural, and total population (number of humans; float) at 1km resolution for years 2020-2100 in 10-year time-steps. <ul> <li><strong><state-name>_1km_<ssp>_<urban, rural, or total>_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <p><strong>zoraghein-oneill_population_gravity_national-ssp-maps.zip</strong></p> <ul> <li>Results of the `population_gravity` model mosaicked to the National scale at a 1km resolution and the comparison Jones and O'Neill research. These are used to generate Figure 6 of the companion paper <ul> <li><strong>National_1km_<ssp>_<urban, rural, or total>_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong>National_1km_<ssp>_<urban, rural, or total>_<year>.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> <p> </p>
Financed projects from Government Delegation grants for the National Plan on Drugs (Spain), 2000-2016
<p>Dataset in a xlsx file used by Aleixandre-Benavent & collaborators in the research “Financed researchs from Government Delegation grants for the National Plan on Drugs: research assessment and scientific impact”.</p>
Annual rainfall erosivity projected differences in Greece
<p>This dataset contains the raster files of the differences between projected and historical mean average values of R in Greece from the paper: </p> <p>Vantas, K.; Sidiropoulos, E.; Loukas, A. Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests. Water 2020, 12, 687, DOI: <a href="https://doi.org/10.3390/w12030687">https://doi.org/10.3390/w12030687</a></p> <p> </p>
Code Review for Conformance to Project Coding Style? Revisit using OpenStack Patches
<p>This is a dataset of the paper name "Code Review for Conformance to Project Coding Style? Revisit using OpenStack Patches".</p> <p>It is used for the peer review process in SANER 2021</p>
QualyGridS project abstract
<p>Project abstract as submitted to the Call for proposals</p>
Watershed shapes for the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E)
<p>This dataset provides the shapefiles of the 46 calibration and 7 validation watersheds used within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E).</p> <p>The watersheds draining towards the following WSC and USGS gauge stations have been used for:</p> <p><strong>1a. calibration of objective 1</strong> (low-human impact)<br> (28 stations in total; 13 of them in both objective 1 and 2)<br> 02GA010 02GA018 02GA038 02GA047 02GB007<br> 02GC002 02GC010 02GC018 02GD004 02GE007<br> 02GG002 02GG003 02GG006 02GG009 02GG013<br> 04159492 04159900 04160600 04161820 04164000<br> 04165500 04166100 04177000 04196800 04197100<br> 04207200 04208504 04213000</p> <p><strong>1b. calibration of objective 2</strong> (most-downstream gauges closest to Lake Erie)<br> (31 stations in total; 13 of them in both objective 1 and 2)<br> 02GB001 02GB007 02GC002 02GC007 02GC018<br> 02GC026 02GE007 02GG003 02GG009 02GG013<br> 04159900 04160600 04165500 04166500 04174500<br> 04176500 04177000 04193500 04195820 04198000<br> 04199000 04199500 04200500 04208504 04209000<br> 04212100 04213000 04213500 04214500 04215000<br> 04215500</p> <p><strong>2. (spatial) validation</strong><br> (7 stations in total)<br> 02GE003 04167000 04168000 04185000 04195500<br> 04201500 04208000</p> <p>===============================================================</p> <p>These data have been derived under the Great Lakes Runoff Intercomparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program.</p>
mHM_UFZ gridded simulations for the Great Lakes Runoff Inter-comparison Project for Lake Erie
<p>This dataset provides gridded model simulations in netcdf format over the Lake Erie using the mHM model (Samaniego, et al., 2010, Kumar et al, 2013), done within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E).</p> <p>Model code is available under: <a href="https://git.ufz.de/mhm/mhm">https://git.ufz.de/mhm/mhm</a>, revision number: 8271b54</p> <p>Domain boundaries (WGS84 system): lon_min = -86.0, lon_max = -78.0, lat_min = 40.0, lat_max = 45.0</p> <p>Model variables are simulated at spatial grid of 0.125deg x 0.125deg, daily time step. </p> <p>Simulation period: 01 Jan 2011 - 31 Dec 2014 (with 1year -2010- warm-up)</p> <p>Meteorological input data, see Mai et al. 2020 (in prep)</p> <p>Following model variables are provided for two mHM_parameter.nml realizations (*obj1 and *obj2, as defined in Mai et al. 2020 in prep). </p> <p>double <strong>snowpack</strong>(time, northing, easting) ;<br> snowpack:long_name = "depth of snowpack" ;<br> snowpack:unit = "mm" ;<br> double <strong>SM_Lall</strong>(time, northing, easting) ;<br> SM_Lall:long_name = "average soil moisture over all layers" ;<br> SM_Lall:unit = "mm mm-1" ;<br> double <strong>unsatSTW</strong>(time, northing, easting) ;<br> unsatSTW:long_name = "reservoir of unsaturated zone" ;<br> unsatSTW:unit = "mm" ;<br> double <strong>satSTW</strong>(time, northing, easting) ;<br> satSTW:long_name = "water level in groundwater reservoir" ;<br> satSTW:unit = "mm" ;<br> double <strong>aET</strong>(time, northing, easting) ;<br> aET:long_name = "actual Evapotranspiration" ;<br> aET:unit = "mm d-1" ;<br> double <strong>Q</strong>(time, northing, easting) ;<br> Q:long_name = "total runoff generated by every cell" ;<br> Q:unit = "mm d-1" ;<br> double <strong>QD</strong>(time, northing, easting) ;<br> QD:long_name = "direct runoff generated by every cell (runoffSeal)" ;<br> QD:unit = "mm d-1" ;<br> double <strong>QIf</strong>(time, northing, easting) ;<br> QIf:long_name = "fast interflow generated by every cell (fastRunoff)" ;<br> QIf:unit = "mm d-1" ;<br> double <strong>QIs</strong>(time, northing, easting) ;<br> QIs:long_name = "slow interflow generated by every cell (slowRunoff)" ;<br> QIs:unit = "mm d-1" ;<br> double <strong>QB</strong>(time, northing, easting) ;<br> QB:long_name = "baseflow generated by every cell" ;<br> QB:unit = "mm d-1" ;<br> double <strong>recharge</strong>(time, northing, easting) ;<br> recharge:long_name = "groundwater recharge" ;<br> recharge:unit = "mm d-1" ;</p> <p>===============================================================</p> <p>These data and model runs have been performed under the Great Lakes Runoff Intercomparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program. This work has received funding from the Initiative and Networking Fund of the Helmholtz Association through the project Advanced Earth System Modelling Capacity (ESM) (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/www.esm-project.net">www.esm-project.net</a>).</p>
Using the history of the Antarctic Ice Sheet to reduce uncertainties in projections of global sea level rise
<p>Ice sheet models are the most descriptive tools available to simulate the future evolution of the Antarctic Ice Sheet (AIS), including its contribution towards changes in global sea level. However, our knowledge of the dynamics of the coupled ice-ocean-lithosphere system is inevitably limited, in part due to a lack of observations. Furthermore, to build computationally efficient models that can be run for multiple millennia, it is necessary to use simplified descriptions of ice dynamics. Ice sheet modelling is therefore a poorly constrained exercise. The past evolution of the AIS provides an opportunity to improve the description of physical processes within ice sheet models and, therefore, to constrain our understanding of the role of the AIS in driving changes in global sea level.</p> <p>We use the Parallel Ice Sheet Model (PISM) to demonstrate how past changes can be used to improve our ability to predict the future evolution of the AIS. A large perturbed-physics ensemble is generated, spanning uncertainty in the parameterisations of key physical processes within the model. A Latin hypercube approach is used to optimally sample the range of uncertainty in parameter values. This perturbed-physics ensemble is used to simulate the evolution of the AIS from the Last Glacial Maximum (21,000 years ago) until 5,000 years into the future. Records of past ice sheet thickness and extent are then used to determine which ensemble members are the most realistic. This allows us to use the known history of the AIS to constrain our understanding of its past contribution towards changes in global sea level. Critically, it also allows us to determine which ensemble members are most likely to generate realistic projections of the future evolution of the AIS. This enables us to use past changes in the AIS to reduce uncertainty in projections of future sea level rise.</p>
Reducing uncertainties in projections of global sea level rise
<p>Ice sheet models are the most descriptive tools available to simulate the future evolution of the Antarctic Ice Sheet (AIS), including its contribution towards changes in global sea level. However, our knowledge of the dynamics of the coupled ice-ocean- lithosphere system is inevitably limited, in part due to a lack of observations. Furthermore, to build computationally efficient models that can be run for multiple millennia, it is necessary to use simplified descriptions of ice dynamics. Ice sheet modelling is therefore a poorly constrained exercise. The past evolution of the AIS provides an opportunity to improve the description of physical processes within ice sheet models and, therefore, to constrain our understanding of the role of the AIS in driving changes in global sea level.<br> <br> We use the Parallel Ice Sheet Model (PISM) to demonstrate how past changes can be used to improve our ability to predict the future evolution of the AIS. A large perturbed-physics ensemble is generated, spanning uncertainty in the parameterisations of key physical processes within the model. A Latin hypercube approach is used to optimally sample the range of uncertainty in parameter values. This perturbed-physics ensemble is used to simulate the evolution of the AIS from the Last Glacial Maximum (21,000 years ago) until 5,000 years into the future. Records of past ice sheet thickness and extent are then used to determine which ensemble members are the most realistic. This allows us to use the known history of the AIS to constrain our understanding of its past contribution towards changes in global sea level. Critically, it also allows us to determine which ensemble members are most likely to generate realistic projections of the future evolution of the AIS. This enables us to use past changes in the AIS to reduce uncertainty in projections of future sea level rise.</p>
Danish Research Projects within Smart Grids 2010-2019
<p>List and assessment of nationally funded projects in Denmark within the Smart Grids field, covering years 2010-2019. The data regarding the projects is retrieved from www.energiforskning.dk.</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.