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4,480 results for “initiative”
liampshaw/Pathogen-host-range: Pathogen-host-range initial code release
<p>Release of code and dataset for publication of associated paper: "The phylogenetic range of bacterial and viral pathogens of vertebrates" (doi: 10.1111/mec.15463).</p>
Where does subduction initiate and cease? A global scale perspective (Supplementary material)
<p>Supplementary material for Where does subduction initiate and cease? A global scale perspective</p>
SIRAH-CoV2 initiative: RNA-dependent RNA polymerase in complex with cofactors Nsp7 and Nsp8 (PDB id:7BTF)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 RNA-dependent RNA polymerase in complex with cofactors Nsp7 and Nsp8 and Zinc (PDB id: 7BTF). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Zinc ions were parameterized as reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The files 7BTF_SIRAHcg_rawdata_0-2us.tar, 7BTF_SIRAHcg_rawdata_2-6us.tar, and 7BTF_SIRAHcg_rawdata_6-10us.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 7BTF_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 7BTF_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 7BTF_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD 1.9.3 using the command line:</p> <p>vmd 7BTF_SIRAHcg_prot.prmtop 7BTF_SIRAHcg_prot.ncrst 7BTF_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martin Soñora (msonora@pasteur.edu.uy) Sergio Pantano (spantano@pasteur.edu.uy).</p>
Video Analysis Scale of Engagement (VASE): Initial and Final Protocols
<p>These files contain the initial and final protocols that were used to develop the Video Analysis Scale for Engagement (VASE). The abstract of our article in Wellcome Open Research, 5:230: https://wellcomeopenresearch.org/articles/5-230/v2:</p> <p><strong>Background</strong>: The current study sought to develop a valid, reliable and unobtrusive tablet computer-based observational measure to assess engagement of people with advanced dementia. The Video Analysis Scale of Engagement (VASE) was designed to enable the rating of moment-by-moment changes in engagement during an activity, which would be useful for both future research and current residential care. <strong>Methods:</strong> An initial version of the VASE was tested. Face validity and content validity were assessed to validate an operational definition of engagement and develop an acceptable protocol for the scale. Thirty-seven non-professional and professional volunteers were recruited to view and rate level of engagement in music activities using the VASE. <strong>Results</strong>: An inter-class coefficient (ICC) test gave a high level of rating agreement across professionals and non-professionals. However, the ICC results of within-professionals were mixed. Linear mixed modelling suggested that the types of interventions (active or passive music listening), the particular intervention session being rated, time period of video and the age of raters could affect the ratings. <strong>Conclusions</strong>: Results suggested that raters used the VASE in a dynamic fashion and that the measure was able to distinguish between interventions. Further investigation and adjustments are warranted for this to be considered a valid and reliable scale in the measurement of engagement of people with advanced dementia in a residential care setting.</p>
SensiLab/Andy-Lomas-Generative-Art-Dataset: Initial release
<p>This is the initial release of the Andy Lomas Generative Art dataset. It contains a collection of 1,774 images of 3D generative art forms created by computational artist <a href="http://www.andylomas.com/">Andy Lomas</a>. This dataset includes images, their generative parameters, artist-defined aesthetic rankings and categorisations of each visual form.</p>
PROTECT project second RAW inertial data for pedestrian inertial localisation (ORDP initiative)
<p><strong>Contact person(s)</strong>: Enrico de Marinis</p> <p><strong>Data collector(s)</strong>: Enrico de Marinis. Fabrizio Pucci, Michele Uliana</p> <p><strong>Data curator(s)</strong>: Guido Rosi</p> <p><strong>Work package leader(s)</strong>: Fabrizio Pucci; Fabio Andreucci</p> <p><strong>Content</strong></p> <p>Inertial Measurement Unit raw data in TXT open and readable format, to be used for processing and testing the pedestrian dead reckoning algorithms by the inertial and indoor tracking scientific community.</p> <p>The raw inertial data have been collected and made publicly available in the frame of the SME Phase 2 project PROTECT (820867), co-funded by the European Commission</p> <p><strong>Experimental data</strong></p> <p>The publicly shared archive contains the following, distinct datasets:</p> <ul> <li>RawData_20200729_141819_000002_000003_007.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_143538_000002_000003_008.decod.grz; collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_150213_000024_000024_004.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_153840_000007_000024_005.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_155152_000024_000003_010.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_113457_000024_000004_006.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_141622_000007_000007_003.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_153554_000007_000007_004.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_YYYYMMDD_HHMMSS_000007_000003_011.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> </ul> <p><strong>Images of the experimental data</strong></p> <p>For each of the above data files, the image of the corresponding PDR (Pedestrian Dead Reckoning) processed track has been added as a geo-referenced JPG capture overlaid on the location satellite image. The image file name is the same as the corresponding data file.</p> <ul> <li>RawData_20200729_141819_000002_000003_007.decod.jpg</li> <li>RawData_20200729_143538_000002_000003_008.decod.jpg</li> <li>RawData_20200729_150213_000024_000024_004.decod.jpg</li> <li>RawData_20200729_153840_000007_000024_005.decod.jpg:</li> <li>RawData_20200729_155152_000024_000003_010.decod.jpg</li> <li>RawData_20200730_113457_000024_000004_006.decod.jpg</li> <li>RawData_20200730_141622_000007_000007_003.decod.jpg</li> <li>RawData_20200730_153554_000007_000007_004.decod.jpg</li> <li>RawData_YYYYMMDD_HHMMSS_000007_000003_011.decod.jpg</li> </ul> <p><strong>Open and Accessible Data format</strong></p> <p>The data format is the following</p> <p>gyro(x) gyro(y) gyro(z) acc(x) acc(y) acc(z) mag(x) mag(y) mag(z) temperature altitude</p> <p>x, y, z indicate the axes of the Inertial Measurement Unit</p> <p>gyro stands for the angular velocity and is in rad/s</p> <p>acc stands for the acceleration and is in m/s^2</p> <p>mag is the magnetic field and is in milligauss</p> <p>temperature is in °C</p> <p>altitude is the output of the altimeter and is expressed in meters</p> <p>All the samples, in all datasets have been recorded with a 200 Hz sampling frequency.</p>
SIRAH-CoV2 initiative: Spike D614G mutation (introduced on PDB id:6XR8)
<p>This dataset contains the raw data of a coarse-grained molecular dynamics simulation of the D614G mutant of Spike protein from SARS-CoV-2. The initial coordinates correspond to the PDB structure 6XR8, where the coordinates of the side chain of Aspartate 614 were deleted to produce a Glycine. Missing loops were introduced with Swiss-Model (https://swissmodel.expasy.org). Simulations were performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Glycan parameters correspond to those reported by <a href="http://doi.org/10.1101/2020.12.18.423446">Garay et at.</a>2020. </p> <p>The files spike_D614G_SIRAHcg_rawdata_0-2us.tar, spike_D614G_SIRAHcg_rawdata_2-4us.tar, spike_D614G_SIRAHcg_rawdata_4-6us.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file spike_D614G_SIRAHcg_prot_10us.tar contains only the protein coordinates, while spike_D614G_SIRAHcg_prot_10us_skip10ns.tar contains one frame every 10ns. Notice that these two files contain a 10-microsecond trajectory, while the raw data is limited to 6 microseconds because of size restrictions on the database. Raw data from microseconds 6 to 10 is available upon request (see below).</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file spike_D614G_SIRAHcg_prot_10us_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd spike_D614G_SIRAHcg_prot.prmtop spike_D614G_SIRAHcg_prot.ncrst spike_D614G_SIRAHcg_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV-2 initiative.</p> <p>For further details, please contact Pablo Garay (pgaray@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: nsp7-nsp8 complex (PDB id:6YHU)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 nsp7-nsp8 complex (PDB id: 6YHU, Bioassembly 1). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The file 6YHU_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6YHU_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6YHU_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6YHU_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6yhu_SIRAHcg_prot.prmtop 6yhu_SIRAHcg_prot.ncrst 6yhu_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martín Soñora (msonora@pasteur.edu.uy) Sergio Pantano (spantano@pasteur.edu.uy).</p>
Transcription initiation peaks based on FANTOM5 CAGE data on hg38 and mm10
<p><strong>Overview</strong></p> <p>Decomposition-based peak identification (DPI, https://github.com/hkawaji/dpi1) is applied to the re-processed (re-aligned) FANTOM5 data, upon hg38 (GRCh38) and mm10 (GRCm38), obtained from below:</p> <ul> <li>http://fantom.gsc.riken.jp/5/datafiles/reprocessed/hg38_v1/basic/</li> <li>http://fantom.gsc.riken.jp/5/datafiles/reprocessed/mm10_v1/basic/</li> </ul> <p>The same parameters to the ones used in the previous paper (Forrest ARR, Kawaji H, Rehli M, et al. Nature 507: 462–470, 2014) was used.</p> <p> </p> <p><strong>Data files</strong></p> <p>Four data files per assembly are prepared as below.</p> <ol> <li>tag cluster in the original definition (*.tc.bed.gz)</li> <li>full set of DPI peaks (*.tc.decompose_smoothing_merged.bed.gz)</li> <li>permissive set of DPI peaks (*.tc.decompose_smoothing_merged.ctssMaxCounts3.bed.gz)</li> <li>robust set of DPI peaks (*.tc.decompose_smoothing_merged.ctssMaxCounts11_ctssMaxTpm1.bed.gz)</li> </ol> <p> </p> <p><strong>Acknowledgement</strong></p> <p>This data set is supported by Research Grant from MEXT to RIKEN Preventive Medicine and Diagnosis Innovation Program, RIKEN Center for Life Science Technologies, and JSPS KAKENHI Grant-in-Aid for Scientific Research No. 16H02902.</p>
Preliminary table of Citizen Science initiatives for monitoring soil health
<p>This matrix is the result of collaborative work for Deliverable 1.1 (WP1; T1.1) of the ECHO project. It facilitated the creation of an overview of the current state of the art in projects, initiatives, or activities that have already involved citizens in monitoring soil health, from both inside and outside the European Union. From this, strategic recommendations for ECHO were derived, ensuring that this project not only makes a valuable contribution to the field of soil health monitoring but also sets a precedent for future citizen science endeavors.</p>
Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries
<p>This dataset contains data collected during a study <a href="https://www.sciencedirect.com/science/article/pii/S0740624X23000989"><em><strong>"Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries"</strong></em></a> conducted by <em>Martin Lnenicka (University of Pardubice, Pardubice, Czech Republic), Anastasija Nikiforova (University of Tartu, Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Kosovska Mitrovica, Serbia), Daniel Rudmark (University of Gothenburg and RISE Research Institutes of Sweden, Gothenburg, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Caterina Santoro (KU Leuven, Leuven, Belgium), Cesar Casiano Flores (University of Twente, Twente, the Netherlands), Marijn Janssen (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).</em></p> <p>It is being made public both to act as supplementary data for "<em>Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries</em>", Government Information Quarterly*, and in order for other researchers to use these data in their own work. </p> <p>***Methodology***</p> <p>The paper focuses on benchmarking of open data initiatives over the years and attempts to identify patterns observed among European countries that could lead to disparities in the development, growth, and sustainability of open data ecosystems. </p> <p>This study examines existing benchmarks, indices, and rankings of open (government) data initiatives to find the contexts by which these initiatives are shaped, both of which then outline a protocol to determine the patterns. The composite benchmarks-driven analytical protocol is used as an instrument to examine the understanding, effects, and expert opinions concerning the development patterns and current state of open data ecosystems implemented in eight European countries - Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. 3-round Delphi method is applied to identify, reach a consensus, and validate the observed development patterns and their effects that could lead to disparities and divides. Specifically, this study conducts a comparative analysis of different patterns of open (government) data initiatives and their effects in the eight selected countries using six open data benchmarks, two e-government reports (57 editions in total), and other relevant resources, covering the period of 2013–2022.</p> <p>***Description of the data in this data set***</p> <p>The file "OpenDataIndex_<em>2013_</em>2022" collects an overview of 27 editions of 6 open data indices - for all countries they cover, providing respective ranks and values for these countries. These indices are:</p> <p>1) Global Open Data Index (GODI) (4 editions)</p> <p>2) Open Data Maturity Report (ODMR) (8 editions)</p> <p>3) Open Data Inventory (ODIN) (6 editions)</p> <p>4) Open Data Barometer (ODB) (5 editions)</p> <p>5) Open, Useful and Re-usable data (OURdata) Index (3 editions)</p> <p>6) Open Government Development Index (OGDI) (2 editions)</p> <p>These data shapes the third context - open data indices and rankings. The second sheet of this file covers countries covered by this study, namely, Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. It serves the basis for Section 4.2 of the paper.</p> <p>Based on the analysis of selected countries, incl. the analysis of their specifics and performance over the years in the indices and benchmarks, covering 57 editions of OGD-oriented reports and indices and e-government-related reports (2013-2022) that shaped a protocol (see paper, Annex 1), 102 patterns that may lead to disparities and divides in the development and benchmarking of ODEs were identified, which after the assessment by expert panel were reduced to a final number of 94 patterns representing four contexts, from which the recommendations defined in the paper were obtained. These patterns are available in the file "OGDdevelopmentPatterns". The first sheet contains the list of patterns, while the second sheet - the list of patterns and their effect as assessed by expert panel.</p> <p>***Format of the file***<br>.xls, .csv (for the first spreadsheet only)</p> <p>***Licenses or restrictions***<br>CC-BY</p> <p> </p> <p>For more info, see README.txt<br> </p>
Agricultural Mitigation Initiatives
<p>This dataset is an ongoing effort to explore the main international mitigation initiatives related to the agrarian sector. The dataset has been created by a research team based at the Geneva Graduate Institute since 2020 based on studies of the sector, conversations with experts and internet searches. The internet search has been conducted using key words, including notions such as:</p><ul><li>Climate change mitigation initiatives/projects/programs/campaigns</li><li>netzero</li><li>nature based solutions</li><li>natural capital</li><li>ecosystem services</li><li>carbon market initiatives</li><li>low carbon agriculture</li><li>afforestation initiatives</li><li>Soil carbon initiatives</li><li>Paludiculture initiatives</li><li>Land use change and climate change mitigation</li></ul><p>Findings have been narrowed down to include only initiatives directly or indirectly related to the agrarian sector. These included netzero campaigns, climate action coalitions, national mitigation programs, non-governmental projects and other mitigation initiatives. Many of the findings were interconnected. For example, a mitigation initiative could be part of a wider mitigation program or coalition. We have therefore tried to reflect these interconnections in the structure of the dataset. For each entry we also included a description with the main characteristics of the entry (coalition, campaign, program or project) the main thematic of the entry and the main actors involved. The main thematic found were:</p><ul><li>carbon sequestration in soils</li><li>advocacy for action</li><li>food systems transformations (climate smart agriculture)</li><li>Land use management</li><li>natural capital initiatives</li><li>carbon accounting standards</li><li>biodiversity conservation and restoration</li><li>technical guidelines for GHGs emission reductions in agriculture</li><li>dissemination</li><li>ecosystems' accounting</li><li>pollution and waste reduction</li></ul><p>The dataset also includes a second spreadsheet where we have started to map some of the actors of the carbon market involved in the development, certification, validation and verification of GHGs emission reductions and removals for agrarian initiatives. </p>
Challenges of constructing and selecting the "perfect" initial and boundary conditions for the LES model PALM
<p><strong>README</strong></p> <p>All the supplementary data needed for the reproduction of the experiment described in the manuscript are provided on this ZENODO repository. The supplementary data includes the following:<br>1. IBC-pre-post-process-revised.zip which contains:<br> - Radio sounding data used for vertical profile statistical and visual comparison. They are stored as "CHMU-soundings.dat" in the CHMU_soundings directory<br> - code for making the figures for vertical profile comparison between the WRF and PALM model<br> - code for performing the statistical analysis for the vertical profiles of PALM and the WRF model<br> - code for making the scatter plots of PALM and WRF vertical profiles<br> - code for making the heatmaps of the PALM model data</p> <p>2. PALM_code.zip contains the source code for the current version of the PALM model used for this experiment</p> <p>3. palm_inputs.zip contains:<br> - static driver file<br> - dynamic driver file<br> - configuration files for the first PALM run (p3d), and the configuration files for the restart runs (p3dr)<br>for each of the performed simulations</p> <p>4. postproc.zip contains:<br> - the code for performing statistical analysis for minimum (min), average (Avg), and maximum (max) three-day averaged differences for the WRF and PALM model outputs<br> - the code for making figures of the differences between selected pairs of WRF and PALM model outputs</p> <p>5. wrf_namelist.zip contains:<br> - list of files in which the setups/configuration for the WRF ensemble used in this experiment</p> <p><strong>PALM MODEL INSTALLATION AND USAGE GUIDE</strong></p> <p>A. Installation:</p> <p>1. First, make sure to satisfy the Software Requirements. On Debian-based Linux Distributions, this can be achieved by the following command:</p> <p><code>sudo apt-get install gfortran g++ make cmake coreutils libopenmpi-dev openmpi-bin libnetcdff-dev netcdf-bin libfftw3-dev python3-pip python3-pyqt5 flex bison ncl-ncarg</code></p> <p>2. Also, some additional python dependencies are needed, which can be installed using pip. In case you want to use a virtual environment for these dependencies, please make sure to create one first. Afterwards, you can install the python dependencies by executing the following command:</p> <p><code>python3 -m pip install -r requirements.txt</code></p> <p>3. Now the PALM model system can be installed with the following commands (please replace with the desired installation directory):</p> <p><code>export install_prefix=""</code><br><code>bash install -p ${install_prefix}</code><br><code>export PATH=${install_prefix}/bin:${PATH}</code></p> <p>4. The following optional command permanently adds this installation to your bash environment:</p> <p><code>echo "export PATH=${install_prefix}/bin:\${PATH}" >> ~/.bashrc</code></p> <p>5. Type <code>bash install -h</code> to get all available options of the install script. During installation, the script calls the respective install script of all packages in this repository and installs them to the chosen directory. Therefore, it is not necessary to manually install any of the packages.</p> <p>You can test your installation with the following commands:</p> <p><code>palmtest --cases urban_environment_restart --cores 4</code></p> <p>B. Usage:</p> <p>After a successful installation, the executables for all packages have been linked into the directory /bin and a default PALM configuration file can be found at /.palm.config.default. In case you have installed the python dependencies inside a virtual environment, that environment needs to be active whenever you wand to use PALM. For usage of each of the packages, please refer to their individual documentation. Next, you need to create your first PALM setup in order to start a simulation. To get a simple preconfigured setup and start your first PALM simulation, please execute the following sequence of commands:</p> <p><code>mkdir -p "${install_prefix}/JOBS/example_cbl/INPUT"</code><br><code>cp "packages/palm/model/tests/cases/example_cbl/INPUT/example_cbl_p3d" "${install_prefix}/JOBS/example_cbl/INPUT/"</code><br><code>cd ${install_prefix}</code><br><code>palmrun -r example_cbl -c default -a "d3#" -X 4 -v -z</code></p>
Omnibus Poll Ukraine - August 2023 (Ilko Kucheriv Democratic Initiatives Foundation + Razumkov Centre) – Random-sample questionnaire-based representative poll
This data collection offers a representative omnibus survey of the Ukrainian population, living in territories controlled by the Ukrainian government without ongoing armed hostilities. The survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation together with the sociological service of the Razumkov Center from 09 to 15 August 2023. The survey was conducted using a stratified multi-stage sample. The structure of the sample reflects the demographic structure of the adult population of the surveyed territories as of the beginning of 2022 (by age, gender, type of settlement). 2019 respondents aged 18 and older were interviewed. The theoretical sampling error does not exceed 2.3%. At the same time, additional systematic sample deviations may be caused by the consequences of Russian aggression, in particular, the forced evacuation of millions of citizens. The survey covers five thematic fields: assessment of the current situation in the country, the Russian war of aggression, energy sector, corruption, volunteering. This data collection contains the original survey data. The SPSS file (.sav) is the original file provided by the Ilko Kucheriv Democratic Initiatives Foundation. It has been exported into an Excel file. The content of the respective xlsx-file should be identical with the original sav-file. The sav-file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation are also included in this data collection as separate pdf-file. Additionally, the data collection contains three files with "selected results" which document some major results of the survey in the form of analytical summaries and descriptive statistics: two in English, covering assessment of the current situation in the country + the Russian war of aggression as well as volunteering; one in Ukrainian covering corruption. New in version 1.1: The numbering of questions in the separate questionnaire (file "DIF_CR_0823-questionnaire-revised.pdf") has been adjusted to the numbering in the original data file ("DIF_CR_0823.sav"). A third file with "selected results" has been added. New in version 1.2: An English translation of the questionnaire has been added under "files".
Omnibus Poll Ukraine - July 2023 (Ilko Kucheriv Democratic Initiatives Foundation + Kyiv International Institute of Sociology) – Random-sample questionnaire-based representative poll
This data collection offers a representative omnibus survey of the Ukrainian population, living in territories controlled by the Ukrainian government without ongoing armed hostilities. The survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation together with the Kyiv International Institute of Sociology from 03 to 17 July 2023. A description of the methodology is given on p.2 of the "selected results" file, which is part of this data collection. The poll covers the following thematic fields: jobs + entrepreneurship, corruption, economic situation, healthcare sector, war, people under Russian occupation. This data collection contains the original survey data. The SPSS file (.sav) is the original file provided by the Ilko Kucheriv Democratic Initiatives Foundation. It has been exported into an Excel file. The content of the respective xlsx-file should be identical with the original sav-file. The sav-file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation are also included in this data collection as separate pdf-files. Additionally, the data collection contains one file with "selected results" which document some major results of the survey in the form of a analytical summaries and descriptive statistics and another file with a clarification concerning the interpretation of question 5.24 about the president's "personal responsibility" for corruption in the country. These files are in Ukrainian only. New in version 1.1: An English translation of the questionnaire has been added under "files".
Underlying Data for Manuscript titled "Hydrothermal Carbonization (HTC) of Dairy Waste: Effect of Temperature and Initial Acidity on the composition and quality of solid and liquid products"
<p>The embodied files include the raw data and initial calculations used to generate the extended data for Manuscript titled "Hydrothermal Carbonization (HTC) of Dairy Waste: Effect of Temperature and Initial Acidity on the composition and quality of solid and liquid products". The files include calculations for Phosphorus Recovery from Hydrochar, as well as Heavy Metals Fractions retrieved by the hydrochar (solid product of Hydrothermal Carbonization).</p>
Simulations from "Using Molecular Gas Observations to Guide Initial Conditions for Star Cluster Simulations"
<p>This dataset contains the simulation results from the article "Using Molecular Gas Observations to Guide Initial Conditions for Star Cluster Simulations" (submitted to MNRAS).</p> <p><br> The data is grouped by simulation and by particle type (gas, sinks and stars). Gas is uploaded with one snapshot per 0.05 Myr, sinks and stars with one snapshot per 0.01 Myr. The data is stored in AMUSE data format, which uses hdf5.</p>
Layens Hive Initial Weight Data
<p>Initial Broodminder under-hive scale data for Layens hive #1. Data is from late September to early October. This data was used on the NMBKA certified beekeepers level II project presentation. This is initial dataset; more to come</p>
GIS data for Bracciano Smart Lake Initiatives
<p>Gis Data relative to lake of bracciano ( RM) supporting <em>Bracciano SmartLake initiative</em> managed by Emanuele Perugini & Guido Tocco.</p> <p><em>Author</em>: Alfonso Crisci IBIMET CNR and & Massimo Perna Consorzio LaMMA</p> <p><em>Data</em>: Perimetral ISTAT administrative boundaries of Bracciano's Area, Bracciano Lake Catchment basin and 5 meter DEM of area ( digital elevation model) obtained troughout TIN interpolation by using elevation data ( CTR5K) retrieved from OpenData Lazio WEB Portal .</p> <p> </p>
geco-bern/leafnp_data: v1.0: Initial release
<div> <h1>leafnp_data</h1> <a href="https://github.com/geco-bern/leafnp_data#leafnp_data"></a></div> <p>This repository contains the leaf Nmass, Pmass, and N:P data (<code>data/leafnp_tian_et_al.csv</code>), based on Tian et al. (2019), complemented by environmental covariates. The code for complementing the original data by Tian et al. (2019) is contained in this repository and is used for the analysis and modelling for the article Tian et al. (in prep.). Code for the data analysis and modelling is contained in a separate repository <a href="https://github.com/stineb/leafnp">leafnp</a>.</p> <div> <h2>Workflow</h2> <a href="https://github.com/geco-bern/leafnp_data#workflow"></a></div> <p>To reproduce the data generation, follow these steps:</p> <ol> <li>Obtain the original data from Tian et al. (2019) (follow link provided therein) and place it locally. In code used here, the file is lives at <code>data-raw/global_leaf_NP_total_Di_20210224.csv</code>.</li> <li>Complement data: <code>analysis/01_complement_environment.R</code> (copied from <code>leafnp/analysis/leafnp.R</code> of the <code>stineb/leafnp</code> repository)</li> <li>Complement missing environmental data: <code>analysis/02_complement_environment_missing.R</code> (copied from leafnp/analysis/leafnp_complement_missing.R of the <code>stineb/leafnp</code> repository)</li> </ol> <p>The code relies on the custom R package {ingestr}. Install it from github, available <a href="https://github.com/geco-bern/ingestr">here</a>.</p> <p>The final file, produced and contained by this repository, is:</p> <p><code>data/leafnp_tian_et_al.csv</code></p> <div> <h2>Repository structure</h2> <a href="https://github.com/geco-bern/leafnp_data#repository-structure"></a></div> <div> <h3>Folder <code>analysis</code></h3> <a href="https://github.com/geco-bern/leafnp_data#folder-analysis"></a></div> <p>Contains scripts (see Workflow above).</p> <div> <h3>Folder <code>data-raw</code></h3> <a href="https://github.com/geco-bern/leafnp_data#folder-data-raw"></a></div> <p>Contains files needed to compile the final file.</p> <div> <h3>Folder <code>data</code></h3> <a href="https://github.com/geco-bern/leafnp_data#folder-data"></a></div> <p>Contains files produced by code of this repo (files not contained by this repo).</p> <div> <h3>Folder <code>vignettes</code></h3> <a href="https://github.com/geco-bern/leafnp_data#folder-vignettes"></a></div> <p>Contains a vignette showcasing the data.</p> <div> <h2>References</h2> <a href="https://github.com/geco-bern/leafnp_data#references"></a></div> <p>Tian, D., Kattge, J., Chen, Y., Han, W., Luo, Y., He, J., Hu, H., Tang, Z., Ma, S., Yan, Z., Lin, Q., Schmid, B., and Fang, J.: A global database of paired leaf nitrogen and phosphorus concentrations of terrestrial plants, Ecology, 100, e02812, <a href="https://doi.org/10.1002/ecy.2812" rel="nofollow">https://doi.org/10.1002/ecy.2812</a>, 2019.</p> <p>Di Tian, Zhengbing Yan, Bernhard Schmid, Jens Kattge, Jingyun Fang, Benjamin D. Stocker: Environmental versus phylogenetic controls on leaf nitrogen and phosphorous concentrations of terrestrial plants. (in review)</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.