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620 results for “sector”
Data for: Parallel recolonisations generate distinct genomic sectors in kelp following high magnitude earthquake disturbance
<p>Large-scale disturbance events have the potential to drastically reshape biodiversity patterns. Notably, newly vacant habitat space cleared by disturbance can be colonised by multiple lineages, which can lead to the evolution of distinct spatial 'sectors' of genetic diversity within a species. We test for disturbance-driven sectoring of genetic diversity in intertidal southern bull kelp, <i>Durvillaea antarctica</i> (Chamisso) Hariot following the high-magnitude 1855 Wairarapa earthquake in New Zealand. Specifically, we use genotyping-by-sequencing (GBS) to analyse fine-scale population structure across the uplift zone to assess the fit of alternative recolonisaton models. Our analysis reveals that specimens from the uplift zone carry genomic signatures distinct from populations in other regions, consistent with recolonisation after the 1855 earthquake. Crucially, our analysis identifies two parapatric spatial-genomic sectors of <i>D. antarctica</i> at Turakirae Head, which experienced the most dramatic uplift. We infer that bull kelp in the Wellington region survived moderate uplift and recolonised the devastated Turakirae Head coastline through two parallel, eastward recolonisation events. By identifying multiple parapatric genotypic sectors within a recently recolonised coastal region, the current study confirms that competing lineage expansions can generate striking spatial structuring of genetic diversity, even in highly dispersive taxa.</p>
Raw data set for Negative Emissions in the Chemical Sector: Lifecycle CO2 Accounting for Biomass and CCS Integration into Ethanol, Ammonia, Urea, and Hydrogen Production.
<p>This repository contains the raw data and code used to generate the results in the paper:</p> <p>Tanzer S.E., Blok K., Ramirez Ramirez A. Negative Emissions in the Chemical Sector: Lifecycle CO2 Accounting for Biomass and CCS Integration into Ethanol, Ammonia, Urea, and Hydrogen Production. 15th International Conference on Greenhouse Gas Control Technologies, GHGT-15. 2021. doi: 10.2139/ssrn.3819778.</p> <p>also published as chapter 4 in the PhD dissertation ”Negative Emissions in the Industrial Sector”. The PhD was the department of Engineering Systems and Services, Faculty of Technology Policy, Management at the Delft University of Technology, between 2017-2022. </p> <p>This is intended to be a record of the exact data and code used to generate the results and graphics used in this publication. It is not necessarily designed for user-friendliness or tested to work on other machines and may contain extraneous data and files.</p> <p>To make use of the python black box modelling library for your own work, please check out the most recent public release, which can be found at https://zenodo.org/record/5800104#.YjUTnC8w30o</p>
GEOS-Chem 2015 Speciated PM2.5 Outputs for "Impact of circular- and single-sector waste-heat reuse pathways on PM2.5-air quality, CO2 emissions, and human health in India; material exchanges more viable to achieve sustainability targets"
<p>Daily PM2.5 and species outputs from GEOS-Chem Modeling over India (0.5° x 0.625°) </p>
Inputs for lake morphology in ISIMIP3 runs of the global lake sector
<p>Empirical evidence demonstrates that lakes and reservoirs are warming across the globe. Consequently, there is an increased need to project future changes in lake thermal structure and resulting changes in lake biogeochemistry in order to plan for the likely impacts. Previous studies of the impacts of climate change on lakes have often relied on a single model forced with limited scenario-driven projections of future climate for a relatively small number of lakes. As a result, our understanding of the effects of climate change on lakes is fragmentary, based on scattered studies using different data sources and modelling protocols, and mainly focused on individual lakes or lake regions. This has precluded identification of the main impacts of climate change on lakes at global and regional scales and has likely contributed to the lack of lake water quality considerations in policy-relevant documents, such as the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). The Lake Sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) was founded for simulating climate change impacts on lakes using an ensemble of lake models and climate change scenarios. The protocol prescribes lake simulations driven by climate forcing from gridded observations and different Earth system models under various Representative Greenhouse Gas Concentration Pathways, all consistently bias-corrected on a 0.5° × 0.5° global grid. The ISIMIP Lake Sector is the largest international effort to project future water temperature, thermal structure, and ice phenology of lakes at local and global scales and paves the way for future simulations of the impacts of climate change on water quality and biogeochemistry in lakes.</p> <p>For a comprehensive description of ISIMIP, sectors, and protocols please see https://www.isimip.org/</p> <p>In order to simulate the impacts of climate change on lakes worldwide, the ISIMIP3 Lake Sector protocol has defined a set of lakes to be modelled by all participating lake models, as well as the basic morphometry (i.e., hypsographic curves) information for each lake. This repository includes all calculations performed to obtain the final set of lake location and mosphometry inpput information for ISIMIP3 runs. For this, available datasets on global lake extension and morphometry were used first for selecting a set of representative lakes on Earth (~40000 lakes, one for each 0.5º pixel of the normalized input/putput grid for ISIMIP across sectors), and then morphological characteristics were assigned to each representative lake using a database on global lake morphology. The final set of files constitute the input data for lake morphology and location for ISIMIP3 Lake Sector runs, which are produced in netCDF format.<br> </p>
A comprehensive and synthetic dataset for global, regional and national greenhouse gas emissions by sector 1970-2018 with an extension to 2019
<p>Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO<sub>2</sub> emissions from fossil fuel combustion and industry (FFI), CH<sub>4</sub> emissions, N<sub>2</sub>O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO<sub>2</sub> emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.</p>
Atmospheric resonant production for light dark sectors
<p>This is the companion dataset to the publication "<em>Atmospheric resonant production for light dark sectors</em>" by the same author. It includes the track length fluxes for electrons, positrons and photons generated by cosmic ray showers, as well as light mesons fluxes obtained by the same process. An example python loader is provided.</p>
Cuadro de las ubicaciones geográficas para los puntos donde se establecieron parcelas de muestreo (P) por cobertura: Bosque fragmentado con vegetación secundaria (Bf) y Bosque de galería y ripario (Bg), sector vereda Larga la vida, Municipio de Dibulla, La Guajira (Colombia).
<p>Publicado en:</p> <p>Pérez Brito, J., Sierra Julio, M. & Acuña Vargas, J. (2022). Estructura y composición de la vegetación riparia en la cuenca baja de la Quebrada Larga la Vida, municipio de Dibulla, La Guajira (Colombia). <em>Ciencia e Ingeniería</em>, 9 (1): e6711270. https://www.doi.org/10.5281/zenodo.6711270</p>
Dendrograma de similaridad de Bray-Curtis para los levantamientos en 0.2 ha de coberturas de la tierra en el Municipio de Dibulla, sector vereda Larga la vida. A medida que los valores de similitud se acercan a 1, es posible determinar que los grupos emparejados con esta tendencia son afines entre sí. BF: Bosque fragmentado con vegetación secundaria; BG: Bosque de galería y ripario; P: Parcela/levantamiento.
<p>Publicado en:</p> <p>Pérez Brito, J., Sierra Julio, M. & Acuña Vargas, J. (2022). Estructura y composición de la vegetación riparia en la cuenca baja de la Quebrada Larga la Vida, municipio de Dibulla, La Guajira (Colombia). <em>Ciencia e Ingeniería</em>, 9 (1): e6711270. https://www.doi.org/10.5281/zenodo.6711270</p>
Distribución de macroplásticos, mesoplásticos y microplásticos en función de los transectos y contenido de las formas de las partículas plásticas en número de ítems/kg reportados en las muestras. (a) Sector A: Colegio de Ingenieros; (b) Sector B: Los Bordones.
<p>Publicado en:</p> <p>Gamboa, A., Pinto, C., Gutiérrez, G. & Ramírez, J. (2022). Basura plástica y microplásticos: contaminantes emergentes presentes en sedimentos de una playa urbana del oriente venezolano. <em>Ciencia e Ingeniería</em>, 9 (1): e6706296. https://www.doi.org/10.5281/zenodo.6706296</p>
National Data Files for Pre-built Sector-coupled Euro-Calliope Model
<p>National time series data derived from the <a href="https://zenodo.org/record/5774988#.YtUQ9-zP3Ph">Sector-coupled Euro-Calliope Pre-built Model</a></p>
Sectoral coverage and prices of carbon pricing mechanisms introduced since 1990
<p>Over the last 30 years, the number of jurisdictions that have implemented a carbon pricing mechanism has grown significantly. Today, 43 national and 32 subnational jurisdictions have such a mechanism in at least one sector. However, a standardized and centralized record of the sectoral scope and prices applied to CO<sub>2</sub> emissions by these mechanisms is lacking.</p> <p>This dataset provides an essential contribution to filling that gap. It covers mechanisms introduced since 1990 at the national and subnational levels and is the most comprehensive attempt at providing a systematic description of carbon pricing mechanisms in terms of their sectoral (and fuel) scope and the associated price signal.</p> <p>A key feature of this dataset is that it provides information structured by territorial jurisdiction, not carbon pricing mechanism. This is achieved by mapping information available for each mechanism onto jurisdictions. It should prove of interest to a wide range of parties, including academic researchers, policy analysts, and interested civil society organizations.</p>
Economics Analysis of Small and Large Farm Size Honey Bee Sub-Sector in Chitwan District, Nepal
<p>This is an SPSS file that can be used for calculating various descriptive statistics and performing statistical tests such as t-tests and chi-square tests. Ranking of scale can also be carried out from these datasets.</p>
Muralla Principal Sector 58 - Puruchuco
Esta muralla de tierra pertenece al la ZONA MONUMENTAL PURUCHUCO HUAQUERONES. Se encuentra en la cima de un cerro (Apu) que domina el sitio arqueológico, en Lima, Perú Source: Objaverse 1.0 / Sketchfab
10. Sector 5000 - 2016
[Scientific Project](http://www.icac.cat/recerca/projectes-de-recerca/projecte/20942/) [Archaeological report 2016](https://www.researchgate.net/publication/311705780_Archaeological_report_-2016-_of_excavation_in_the_visigothic_settlement_of_Valencia_la_Vella_Riba-roja_del_Turia_Valencian_Country_Spain?_iepl%5BviewId%5D=XzB9abVr2fOR1kpvvnyc8cX4&_iepl%5Bcontexts%5D%5B0%5D=projectUpdatesLog&_iepl%5BtargetEntityId%5D=PB%3A311705780&_iepl%5BinteractionType%5D=publicationTitle) Source: Objaverse 1.0 / Sketchfab
1-km bed topography digital elevation model (DEM) of the Weddell Sea sector, West Antarctica
<p>We present a new bed elevation digital elevation model (DEM) of the Weddell Sea sector, West Antarctica. The DEM consists a total area of ~125,000 km<sup>2</sup> covering the Institute, Möller and Foundation ice streams and the Bungenstock Ice Rise with a 1 km spatial resolution. </p> <p>In order to produce the bed elevation DEM, ice thickness DEM was formed from the available radio-echo sounding (RES) data using the 'Topo to Raster' function in ArcGIS. The RES data used in this study were compiled from four main sources which are (1) Scott Polar Research Institute (SPRI) survey collected during several campaigns in the 1970s (Drewry, 1983), (2) British Antarctic Survey (BAS) airborne radar survey conducted during the austal summer 2006/07 (GRADES/IMAGE) (Ashmore et al., 2014). , (3) BAS airborne survey accomplished during the Institute and Möller Antarctic Funding Initiative (IMAFI) in 2010/2011 (Ross et al., 2012) and (4) Center for the Remote Sensing of Ice Sheet (CReSIS) data during the NASA Operation IceBridge (OIB) programme in 2012, 2014 and 2016 (Gogineni, 2012). The ice thickness picks were gridded at a uniform 1-km spacing using the Nearest Neighbour interpolation within the Topo to Raster. The ice thickness DEM was later subtracted from the 1-km ice-sheet surface elevation derived from the combined European Remote Sensing Satellite-1 (ERS-1) radar and Ice, Cloud and land Elevation Satellite (ICESat) laser satellite altimetry DEM (Bamber et al., 2009), to produce the bed topography referenced to the Polar Stereographic projection (Snyder, 1987). </p>
Supplementary Data: Code, Input Data and Result Summaries: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the scripts to build the model, input data and result summaries for the model PyPSA-Eur-Sec-30 described in the above publication.</p> <p>The full results files (which include the post-processed input data) can be found in a <a href="https://zenodo.org/record/1146649">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p><strong>WARNING:</strong> A newer, improved version of this model, <a href="https://github.com/PyPSA/pypsa-eur-sec">PyPSA-Eur-Sec</a>, is under construction on GitHub.</p> <p><strong>Scripts</strong></p> <p>To use the scripts, you need the following free software Python libraries:</p> <ul> <li><a href="https://github.com/PyPSA/PyPSA">PyPSA</a> for the modelling framework</li> <li><a href="https://github.com/FRESNA/vresutils">vresutils</a> for various helper functions to build the model instance</li> <li><a href="https://github.com/FRESNA/atlite">atlite</a> to process weather data into power system data</li> <li><a href="https://snakemake.readthedocs.io/en/latest/">snakemake</a> to organise the execution of the software</li> </ul> <p>and other standard libraries from the <a href="https://pypi.python.org/pypi">Python Package Index</a> (PyPI), such as pandas, pyomo, countrycode, etc.</p> <p>snakemake requires that all code runs with Python version 3. The code setup is known to work with the following versions: PyPSA 0.12.0, pandas 0.21.1, numpy 0.14.0, scipy 0.19.1, pyomo 5.2. You may need to downgrade your libraries to these versions for the scripts to work. If you insist on using the latest versions, please be aware that you'll need to make at least the following changes:</p> <p>i) To accommodate changes in pandas versions 0.22 and higher, in scripts/prepare_network.py change "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby("technology").sum()" to "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby(level="technology").sum(min_count=1)".</p> <p>ii) In later versions of PyPSA the component groups like "pypsa.components.one_port_components" have become network-specific and are stored instead at "network.one_port_components".</p> <p>To solve the optimisation problem the scripts are coded to use the commercial solver <a href="http://www.gurobi.com/">Gurobi</a>. To solve the problems in a reasonable time, you will need <a href="http://www.gurobi.com/">Gurobi</a> or an equivalently fast solver such as <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a>. <a href="http://www.gurobi.com/">Gurobi</a> and <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a> both have cost-free licences for academic users.</p> <p>You will also need a computer with at least 64 GB of RAM, since pyomo and the solver are memory intensive.</p> <p>The Python scripts in this repository (in the directory scripts/) are released under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GNU General Public Licence Version 3.0</a> (GPL 3.0).</p> <p>The scripts build_*.py process all raw input data into a form where it can be used in the model.</p> <p>make_options.py prepares the options.yml file for each model run.</p> <p>prepare_network.py populates the PyPSA network for each model run with the input data.</p> <p>solve_network.py solves the optimisation problem with <a href="http://www.gurobi.com/">Gurobi</a> or the solver of your choice (this step takes several hours).</p> <p>make_summary.py aggregates the results into CSV files in the directory results/ (also provided in this repository).</p> <p>The scripts plot_*.py and paper_graphics*.py prepare graphical output.</p> <p>All scripts are managed with the <a href="http://snakemake.readthedocs.io/en/latest/">snakemake</a> workflow management tool.</p> <p>To run the scripts, adjust the parameters in config.yaml and cluster.yaml to your local configuration. Then simply execute</p> <pre><code>snakemake</code></pre> <p>for the rule you want to run.</p> <p>Since the jobs are computationally intensive you may want to run them on a cluster. To run the jobs on a cluster with <a href="https://slurm.schedmd.com/">Slurm</a>, then execute e.g.</p> <pre><code>./snakemake_cluster --jobs 6</code></pre> <p>The cluster is configured in cluster.yaml. You will need to create the directory for the logs, i.e. logs/cluster/, before running the script.</p> <p><strong>Data</strong></p> <p>All input data (in the directory scripts/) and results summaries (in the directory results/) are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0), except those where explicit sources and licences are mentioned in the data folders.</p> <p>The input data include:</p> <ul> <li>Electricity sector data, which largely follows the <a href="https://doi.org/10.5281/zenodo.804337">Zenodo repository</a> for <strong><a href="https://doi.org/10.1016/j.energy.2017.06.004">The Benefits of Cooperation in a Highly Renewable European Electricity Network</a></strong>, except the current repository uses the <a href="https://data.open-power-system-data.org/time_series/2017-07-09/">Open Power System Data Time Series Data Package</a> for load data and <a href="http://renewables.ninja/">Renewables.ninja</a> for solar time series.</li> <li>Heating time series based on the degree-day approximation, constructed with the library <a href="https://github.com/FRESNA/atlite">atlite</a>.</li> <li>Hourly traffic statistics for a week from the German Federal Highway Research Institute (BASt).</li> <li>Yearly energy per country per sector from the <a href="http://www.indicators.odyssee-mure.eu/energy-efficiency-database.html">Odyssee database</a> and <a href="http://ec.europa.eu/eurostat/web/energy/data/energy-balances">Eurostat</a>.</li> <li>A cost database with literature sources.</li> </ul>
Testing novel climate accounting identifies animal agriculture as the leading emissions sector
<p>Data and spreadsheet supporting the paper <span>"Testing novel climate accounting identifies animal agriculture as the leading emissions sector" G Wedderburn-Bisshop, 2024</span></p>
Emission Scenarios used for: Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020
<p>CH<sub>4</sub> emission scenarios, based on bottom-up emission estimates (Chandra et al., CEE, 2024; Fig 2) for simulating the long-term trends and latitudinal gradients of CH<sub>4</sub> and <em>δ</em><sup>13</sup>C-CH<sub>4</sub>. The details can be found at </p> <p>Chandra, N., Patra, P.K., Fujita, R. <em>et al.</em> Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020. <em>Commun Earth Environ</em> <strong>5</strong>, 147 (2024). https://doi.org/10.1038/s43247-024-01286-x</p>
Simulated datasets for dihiggs to (bb)(γγ) with higgs-sector SMEFT effects
<p>Analysis dataset used for the report "Deriving Constraints on di-Higgs SMEFT couplings with Neural Simulation-based Inference" (<a href="https://arxiv.org/abs/2405.15847">arXiv link</a>). We provide dihiggs signal events <em>gg -> hh -> (bb)(γγ)</em> and QCD background events <em>pp -> bbγγ </em>at both a 14 TeV and 100 TeV collider setup. All events are generated with MadGraph 3.5.1, Pythia 8.306, and Delphes 3.5.0.</p> <p>For the hard process (<em>pp -> hh</em>) for the signal events, we use the <a href="https://feynrules.irmp.ucl.ac.be/wiki/SMEFTatNLO" target="_blank" rel="noopener">SMEFT@NLO</a> MadGraph model to incorporate the effects of three dimension-6 SMEFT operators (c_p, c_{dp}, c_{tp}). We select 10 values ("morphing basis points") for the vector (c_p, c_{dp}, c_{tp}) (see Table IV of the accompanying report). At each morphing basis point, we generate a number of signal events; for each event, we also calculate the weights to the other 9 basis points. All of this data is stored in the .h5 files. We use MadSpin to carry out the higgs decays.</p> <p>These datasets can be used as input into the <a href="https://github.com/madminer-tool/madminer/blob/b7f7916dde5b315cecaafd113e88a37b9464c420/madminer/sampling/sampleaugmenter.py#L16" target="_blank" rel="noopener">MadMiner SampleAugmenter</a> function, which uses the weights associated with the signal events to generate detector-level events at arbitrary (c_p, c_{dp}, c_{tp}) points.</p> <p>All scripts used to generate and process this data are available at <a href="https://github.com/rmastand/nsbi_for_dihiggs" target="_blank" rel="noopener">this repository</a>. Please refer to the accompanying paper for more details on the analysis. </p> <p> </p> <p> </p> <p> </p>
Per capita sectoral emissions for European countries 1990 - 2022
<p>The data set contains per capita emissions (t CO2eq/capita) for European countries from 1990 to 2022 (for some countries values starting in 1985 are contained). Emission data is taken from the <a href="https://doi.org/10.2909/6331f651-8863-4656-a911-669f2a332a1e" target="_blank" rel="noopener">European Environment Agency</a> [1]. Population data is taken from <a href="https://doi.org/10.2908/DEMO_GIND" target="_blank" rel="noopener">Eurostat </a>(population at January 1th in each year) [2]. Emissions are reported per sector, with the sectoral categories according to <a href="https://www.bmuv.de/fileadmin/Daten_BMU/Download_PDF/Gesetze/191118_ksg_lesefassung_bf.pdf" target="_blank" rel="noopener">Annex I in the German Federal Climate Change Act</a> [3]:</p> <p>Energy: 1.A.1, 1.A.3.e, 1.B<br>Industry: 1.A.2, 2, 1.C<br>Buildings: 1.A.4.a, 1.A.4.b, 1.A.5<br>Transport: 1.A.3.a, 1.A.3.b, 1.A.3.c, 1.A.3.d<br>Agriculture: 5,6<br>Land use, land-use change and forestry: 4</p> <p>The data set contains the data as a .csv and as a .xlsx file. Additional, the code for processing the raw data (available from the European Environment Agency and from Eurostat) is contained as a jupyter notebook.</p> <p>Please communicate any questions, corrections or comments to mirko.schaefer [at] inatech.uni-freiburg.de</p> <p>[1] National emissions reported to the UNFCCC and to the EU Greenhouse Gas Monitoring Mechanism, April 2024. European Environment Agency (2024)<br>[2] Demographische Veränderung - absoluter und relativer Bevölkerungsstand auf nationaler Ebene. Eurostat (2024)<br>[3] German Federal Climate Change Act (2021)</p> <p> </p> <p> </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.