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194 results for “Assetization”
H2020 Platone German Demonstrator Use Case 1 Asset and Topolgy Data
<p>This dataset asset and topology data of the field test setup. The dataset gives details about:</p> <p>- Low Voltage (LV) network</p> <p>- Tranformer located in the secondary substation</p> <p>- Community Battery Energy Storage System (CBES) connected to the LV-busbar in the 2nd. Substation</p> <p>- PV and number of households located in the energy community</p> <p>- PV installed generation power of the community</p> <p>- Domestic Storages and Inverter</p> <p> </p>
Figure data and model used in Stranded fossil-fuel assets translate to major losses for investors in advanced economies
<p>The package contains i) the figure code and underlying data to create all figures in the main paper and supplementary information of the journal article and ii) the network and imputation model used to calculate the shock calculation.</p>
Global scientific research commons under the Nagoya Protocol: Towards a collaborative economy model for the sharing of basic research assets
<p>This paper aims to get a better understanding of the motivational and transaction cost features of<br /> building global scientific research commons, with a view to contributing to the debate on the design of<br /> appropriate policy measures under the recently adopted Nagoya Protocol. For this purpose, the paper<br /> analyses the results of a world-wide survey of managers and users of microbial culture collections, which<br /> focused on the role of social and internalized motivations, organizational networks and external<br /> incentives in promoting the public availability of upstream research assets. Overall, the study confirms<br /> the hypotheses of the social production model of information and shareable goods, but it also shows the<br /> need to complete this model. For the sharing of materials, the underlying collaborative economy in<br /> excess capacity plays a key role in addition to the social production, while for data, competitive pressures<br /> amongst scientists tend to play a bigger role.</p>
COALMOD-World 2.0 data, results, figures for: Stranded assets and early closures in global coal mining under 1.5°C
<p>This dataset contains all COALMOD-World 2.0 data for Hauenstein (2023): Stranded assets and early closures in global coal mining under 1.5°C (doi.org/10.1088/1748-9326/acb0e5) </p> <p>With the input data files and the GAMS scenario file the model (https://doi.org/10.5281/zenodo.7077678) can be run to reproduce the model results.</p> <p>Furthermore, the output.zip folder contains the results file, the R code to compile the figures, and PDFs of the figures.</p>
Solar Asset Mapper: A continuously-updated global inventory of solar energy facilities built with satellite data and machine learning
<p><strong>TransitionZero’s Solar Asset Mapper is a global, satellite-derived dataset of utility-scale solar farms generated with a combination of machine learning and human annotation. Our Q1 2024 dataset contains the location and shape of 63,616 assets, along with estimated capacities. We estimate the construction date for over 80% of these assets. The dataset contains over 19,100 square kilometres of solar farms across 183 countries, with a total estimated capacity of 711 GW.</strong></p> <p>Download the dataset, read the explainer and explore our polygon browser UI at <a href="https://www.transitionzero.org/products/solar-asset-mapper" target="_blank" rel="noopener">TransitionZero.org.</a></p> <p><a href="https://blog.transitionzero.org/hubfs/Data%20Products/TZ-SAM/tz-sam-scientific-methodology-Q12024.pdf" target="_blank" rel="noopener">Download our methodology paper here </a></p> <h1><strong>1. Dataset Description</strong></h1> <p>We publish six files.</p> <ul> <li><em>analysis_polygons.gpkg:</em> our “analysis-ready” dataset containing geometries, capacity estimates and construction date estimates.</li> <li><em>analysis_polygons.csv:</em> a version of analysis_polygons.gpkg containing a central latitude and longitude in place of a geometry, to allow parsing without geospatial software.</li> <li><em>sources.csv</em>: a table mapping the IDs of our analysis-ready dataset to the raw geometries that make them up.</li> <li><em>raw_polygons.gpkg:</em> the raw geometries used to compose analysis_polygons.gpkg.</li> <li><em>TZ Solar Asset Mapper Q1 2024.xlsx</em>: an Excel formatted version of the analysis_polygons.csv file.</li> <li><em>tz-sam_scientific_data.pdf</em>: A pre-print aricle that explains the methodology in detail.</li> </ul> <h2><strong>1.1 Analysis-level datasets</strong></h2> <p>Our analysis-level dataset comprises our most complete view of global asset-level solar installations, incorporating our own detections as well as known solar farm geometries from other datasets.</p> <p>The geospatial dataset contains the following fields:</p> <ul> <li>id: unique ID for the asset</li> <li>geometry: Polygon or MultiPolygon defining the asset</li> <li>capacity_mw: estimated capacity of the asset in megawatts</li> <li>constructed_before: upper bound for construction date (estimated date of the image in which the solar plant was first seen in a constructed state)</li> <li>constructed_after: lower bound for construction date (estimated date of the image in which construction began for the solar plant)</li> </ul> <p>The CSV version replaces the Geometry column with:</p> <ul> <li>latitude: the latitude of the centroid of the asset</li> <li>longitude: the longitude of the centroid of the asset</li> <li>country: administrative country name</li> </ul> <h2><strong>1.2 Raw datasets and sources</strong></h2> <p>The analysis-level datasets hide some complexity in the underlying data that we expose in the <em>raw_polygons</em> and <em>sources</em> file.</p> <ul> <li>We produce new sets of polygons for each run. Often these overlap, sometimes in complicated ways.</li> <li>We cluster together overlapping and nearby geometries from both our detections and external sources. Currently these sources are:</li> <li>Large solar farms scraped from OpenStreetMap (OSM)</li> <li>Validated geometries from <a href="../records/5005868">Kruitwagen et. al., A global inventory of solar photovoltaic generating units</a>.</li> </ul> <p>Each cluster comprises one row in the analysis-level dataset. In order to enable tracking raw detections from run to run, as well as to provide detailed sourcing information, we provide all of these raw polygons, along with a source file that lists all of the raw polygons contained in each analysis-level polygon.</p> <p>raw_polygons.gpkg contains the following fields:</p> <ul> <li>id: ID of the raw source polygon</li> <li>geometry<strong>: </strong>Polygon or MultiPolygon defining the asset</li> <li>source: either “solar asset mapper”, “osm” or “2019_global_pv”.</li> <li>acquisition_date: for solar asset mapper polygons, this is the date of the inference run that produced the polygon; for OSM polygons it is the date that the polygon was scraped from OSM; for 2019_global_pv it is 2019-01-01, the approximate detection date of that dataset.</li> </ul> <p>Sources.csv contains the following fields:</p> <ul> <li>cluster_id: ID of the corresponding item in the analysis-level dataset</li> <li>source_id: ID of the raw source polygon</li> <li>source: either “solar asset mapper”, “osm” or “2019_global_pv”.</li> <li>acquisition_date: for solar asset mapper polygons, this is the date of the inference run that produced the polygon; for OSM polygons it is the date that the polygon was scraped from OSM; for 2019_global_pv it is 2019-01-01, the approximate detection date of that dataset.</li> </ul> <h2><strong>1.3 Caveats and limitations</strong></h2> <h3><strong>1.3.1 Capacity Estimates</strong></h3> <p>While we have made every effort to remove false positives from the published dataset, some will remain due to the difficulty of manually validating detections in 10-metre satellite imagery. To estimate false positive prevalence throughout the data a subset of approximately 2000 detections were selected at random from our positively labelled solar assets. Each of these were validated through a higher degree of scrutiny utilising high-resolution imagery. This analysis yielded an expected rate of false positives of around 1%.</p> <h3>1.3.2 Plant Shapes</h3> <p>Our plant outlines are not perfect. They will occasionally be much smaller or larger than the underlying plant. Our tests show that on average, these effects average out.</p> <h3>1.3.3 Capacity Updates</h3> <p>Our capacity estimation model should produce relatively unbiased country-level aggregates, since it is trained to learn the typical ground coverage ratio of plants by country. The model has no way to distinguish between a very dense and a very sparse (e.g. dual-axis-tracking) plant in the same country. Plants with unusually high or low ground coverage ratios will not have accurate capacity estimates.</p> <h3>1.3.4 Construction Date Estimates</h3> <p>We are not able to directly estimate the construction date of a plant. We estimate an upper bound (the date of the image in which the plant was first seen in constructed state) and a lower bound (the date of the image in which the plant was last seen in an unconstructed state). For plants that were constructed before the launch date of Sentinel-2 in 2017, we produce only an upper bound.</p> <p>We leave it to consumers of the data to interpret these bounds and/or estimate likely grid connection dates.</p> <p><strong>2. Attribution</strong></p> <p>TZ-SAM is made available under a Creative Commons Attribution Non-Commercial 4.0 International License (CC-BY-NC-4.0). Attribution to TransitionZero is required. You must also clearly indicate if you have made any changes to the TZ-SAM dataset and what these are. Please refer to the suggested citation formats:</p> <ul> <li>“TransitionZero Solar Asset Mapper, TransitionZero, May 2024 release.”</li> <li>“TZ-SAM, TransitionZero, May 2024 release.”</li> <li>“TransitionZero (2024) Solar Asset Mapper.”</li> </ul>
Support Data Assets for Figures and Tables in gmd-12-3449-2019 (Geosci. Model Dev.)
<p>Support datasets for "VISIR-1.b: ocean surface gravity waves and currents for energy-efficient navigation", by G. Mannarini and L. Carelli (Geosci. Model Dev. paper, https://doi.org/10.5194/gmd-12-3449-2019):</p><ul><li><strong> graphs_gmd-2018-292.tar.gz</strong></li></ul><p>Graphs for running VISIR computations </p><ul><li><strong>figTab_DATA_v3.tar.gz:</strong></li></ul><p>Source data for Figures and Tables</p>
Data for: Assets in Periphery, Agents in the Core: Mapping the Micro Structures of International Tax Planning
<p>Data for: Assets in Periphery, Agents in the Core: Mapping the Micro Structures of International Tax Planning</p> <p>See paper at: https://osf.io/preprints/socarxiv/hyc4p</p> <p>In the last two decades, tax avoidance has risen to the top of the agenda of policy makers and international organizations. The majority of political action and academic research has focused on the macro-level of states, pointing towards the responsibility of ‘tax havens’ or ‘offshore financial centers’. Research on the micro-level has demonstrated the importance of non-state actors who facilitate tax planning, but tax advisors have never been studied systematically with global data. In this paper, we connect the micro and macro levels. We map tax advisors geographically using a novel empirical approach based on LinkedIn. We show that tax advisors generally locate in large cities in the EU and OECD, rather than in places targeted as ‘tax havens’. We further consider what determines the locations of tax advisors. Using multiple regression analysis, we find that locations of tax advisors does not correlate with the location of corporate profits, financial secrecy, or economic activity. Rather, it correlates with the managerial and financial activity. Our results underscore the core-periphery structure in offshore finance. Effective regulation of tax avoidance should focus on tax advisors, not only on the destination of money flows, since the active facilitation does not occur in those places.</p>
COALMOD-World 2.0 data, results, figures for: Stranded Assets in the Coal Export Industry? The Case of the Australian Galilee Basin
<p>This dataset contains all COALMOD-World 2.0 data for Hauenstein et al. (2023): New coal mines in the Australian Galilee Basin are not economically viable and are prone to become stranded assets (doi.org/10.1016/j.oneear.2023.07.005).</p> <p>With the input data files and the GAMS scenario file the model (https://github.com/chauenstein/COALMOD-World_v2.0) can be run to reproduce the model results.</p> <p>Furthermore, the output.zip folder contains the results file, the R code to compile the figures, and PDFs of the figures.</p>
Compilation of open asset-level data, as of Dec 2022
<p>This dataset is a compilation of open asset-level data, which means the location of sites (e.g., operation, manufacturing, processing facilities of global supply chains), as of December 2022. This included data from 9 publicly available sources, that after data cleaning and harmonization, resulted in 189,075 data points. </p> <table> <tbody> <tr> <td><strong>Data source</strong></td> <td><strong>Number of data points</strong></td> </tr> <tr> <td><a href="https://opensupplyhub.org/">Open Supply Hub (former Open Apparel Registry)</a></td> <td>96,736</td> </tr> <tr> <td><a href="https://datasets.wri.org/dataset/globalpowerplantdatabase">Global Power Plant Database</a></td> <td>35,419</td> </tr> <tr> <td><a href="https://climatetrace.org/downloads">Climate trace</a></td> <td>19,945</td> </tr> <tr> <td><a href="https://www.fda.gov/drugs/drug-approvals-and-databases/drug-establishments-current-registration-site">FDA database</a></td> <td>12,898</td> </tr> <tr> <td><a href="https://www.globaldamwatch.org/database">Global Dam Watch</a></td> <td>11,017</td> </tr> <tr> <td><a href="https://www.ema.europa.eu/en/human-regulatory/research-development/compliance/good-manufacturing-practice/eudragmdp-database">EudraGMDP database</a></td> <td>5,181</td> </tr> <tr> <td><a href="https://www.cgfi.ac.uk/spatial-finance-initiative/geoasset-project/geoasset-databases/">Sustainable Finance Initiative GeoAsset Databases</a></td> <td>4,716</td> </tr> <tr> <td><a href="https://tailing.grida.no/disclosures">Global Tailings Portal</a></td> <td>1,956</td> </tr> <tr> <td><a href="https://www.fineprint.global/resources/mining-database/">Fine print Mining Database</a></td> <td>1,207</td> </tr> </tbody> </table> <p>This data was assigned with the industry in which the asset is. The summary table below shows the number of assets by industry.</p> <table> <tbody> <tr> <td><strong>Industry</strong></td> <td><strong>Number of assets </strong></td> </tr> <tr> <td>Textiles, Apparel & Luxury Good Production </td> <td>96,736</td> </tr> <tr> <td>Health Care, Pharma and Biotechnology</td> <td>18,079</td> </tr> <tr> <td>Energy - Solar, Wind</td> <td>16,282</td> </tr> <tr> <td>Energy - Hydropower</td> <td>14,515</td> </tr> <tr> <td>Energy - Geothermal or Combustion</td> <td>11,724</td> </tr> <tr> <td>Metals & Mining</td> <td>11,210</td> </tr> <tr> <td>Transportation Services</td> <td>4,872</td> </tr> <tr> <td>Construction Materials</td> <td>3,117</td> </tr> <tr> <td>Agriculture (animal products)</td> <td>2,388</td> </tr> <tr> <td>Agriculture (plant products)</td> <td>1,896</td> </tr> <tr> <td>Oil, Gas & Consumable Fuels</td> <td>1,194</td> </tr> <tr> <td>Water utilities / Water Service Providers</td> <td>892</td> </tr> <tr> <td>Hospitality Services</td> <td>294</td> </tr> <tr> <td>Fishing and aquaculture</td> <td>14</td> </tr> <tr> <td>Other</td> <td>5,862</td> </tr> </tbody> </table> <p><strong>Note that this compilation is based on an extensive search, however, we acknowledge that there is a significant discrepancy in data coverage/comprehensiveness among the different industries.</strong> The industry "Textiles, Apparel & Luxury Good Production" is by far the most complete, while other are clearly far from complete, for example, “Construction Materials”, "Agriculture (animal products)”, “Agriculture (plant products)”, “Oil, Gas & Consumable Fuels”, “Water utilities / Water Service Providers”, “Hospitality Services”, “Fishing and aquaculture”. <strong>Therefore, any comparison between industries should take this coverage/comprehensiveness bias into consideration.</strong></p> <p> </p>
Digital Assets for "Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey"
<p>These are morphological catalogs and trained <a href="https://github.com/aritraghsh09/GaMPEN">GaMPEN</a> models for Hyper Suprime-Cam galaxies. Please refer to <a href="https://gampen.readthedocs.io/en/latest/Public_data.html">https://gampen.readthedocs.io/en/latest/Public_data.html</a> and <a href="https://arxiv.org/abs/2212.00051">https://arxiv.org/abs/2212.00051</a> for more details about this data release. </p> <p> </p> <p><strong>Catalog Files</strong></p> <ol> <li>g_0_025_preds_summary.csv --> Structural parameter catalog for z < 0.25 HSC g-band galaxies </li> <li>r_025_050_preds_summary.csv --> Structural parameter catalog for 0.25 < z < 0.50 HSC r-band galaxies </li> <li>i_050_075_preds_summary.csv --> Structural parameter catalog for 0.50 < z < 0.75 HSC i-band galaxies </li> </ol> <p> </p> <p><strong>Trained PyTorch Model Files</strong></p> <ol> <li>g_0_025_real_data.pt --> Trained Model for z < 0.25 HSC g-band galaxies </li> <li>r_025_050_real_data.pt --> Trained Model for 0.25 < z < 0.50 HSC r-band galaxies </li> <li>i_050_075_real_data.pt --> Trained Model for 0.50 < z < 0.75 HSC i-band galaxies </li> <li>sim_g_0_025.pt --> Trained Model for Simulated z < 0.25 HSC g-band galaxies </li> <li>sim_r_025_050.pt --> Trained Model for Simulated 0.25 < z < 0.50 HSC r-band galaxies </li> <li>sim_i_050_075.pt --> Trained Model for Simulated 0.50 < z < 0.75 HSC i-band galaxies </li> </ol>
Dataset from autonomous assets collected during the SO-CHIC field campaign in the Southern Ocean
<p>These datasets are a part of the Southern Ocean Carbon and Heat Impact on Climate (SO-CHIC, https://www.sochic-h2020.eu/) project, and were collected during the field campaign in 2022.<strong><br></strong></p> <p><strong>Seaglider SG640 </strong><em>(SG640_20220110_SOCHIC.nc</em>)</p> <p>This dataset contains the temperature, salinity, dissolved oxygen, fluorescence, and backscatter profiles from a Seaglider (SG640), which was deployed at the Maud Rise seamount in the eastern part of the Weddell Sea from the 10th of January 2022, to the 7th of April 2022. These profiles are available at Level 2 (basic gridding) and Level 3 (despiked and interpolated).</p> <p><strong>Seaglider SG675</strong> (<em>SG675_20220123_SOCHIC.nc</em>)</p> <p>This dataset contains the temperature, salinity, dissolved oxygen, fluorescence, and backscatter profiles from a Seaglider (SG675), which was deployed at the Maud Rise seamount in the eastern part of the Weddell Sea from the 23rd of January 2022, to the 18th of June 2022. These profiles are available at Level 2 (basic gridding) and Level 3 (despiked and interpolated).</p> <p><strong>Sailbuoy Kringla </strong>(<em>SB_20220110_SOCHIC.nc</em>)<strong><br></strong></p> <p>This dataset contains atmospheric and oceanic observations of wind speed, air and sea temperature, salinity, and currents. The Sailbuoy was deployed at the Maud Rise seamount in the eastern part of the Weddell Sea from the 10th of January 2022, and recovered on the 17th of June 2022. There was a brief period in March where she was recovered and repaired by Polarstern and subsequently re-deployed, losing around three days of data. The ADCP data were processed using <a href="https://doi.org/10.5281/zenodo.7494998">Edholm, J. M., du Plessis, M., & Swart, S. (2022) - Sailbuoy processing (v1.0.0)</a></p> <p><strong>Saildrone 1067 </strong>(<em>SD-1067-NRT-data.nc</em>)<strong><br></strong></p> <p>This dataset contains atmospheric and oceanic observations of wind speed, air and sea temperature, relative humidity, short and longwave radiation, pCO2, dissolved oxygen, chlorophyll-a, salinity, and currents. The Saildrone was deployed in the Agulhas retroflection area in the Cape Cauldron from the 24th of August 2022 to the 22nd of November 2022.</p> <p> </p> <p> </p>
Community assets and relative rurality index
<p>The CARR (Community assets and relative rurality index) index is a continuous, multidimensional measure of rurality based on the concept of sustainable development that integrates measures of environmental, social, and economic resources. The index incorporates traditional rurality measures such as population density, distance from metropolitan areas, and developed area with measures of availability and accessibility of 70 different infrastructure types that support essential community services and community amenities. The index uses data last updated in 2019 and is available for census block-groups in the coterminous United States.</p> <p>This repository includes versions of CARR calculated for block groups (original) and versions of CARR aggregated to other conventional spatial units using and area-weighted averaging approach on the original block group data. Each zip folder includes the raw data, a metadata file, and a basic exploratory data analysis document in html format.</p> <p>The information for Version 2 of this repository, and additional information on CARR, can also be found at <a href="ruralityindex.com" target="_blank" rel="noopener">ruralityindex.com</a>.</p>
Data from: Building trust takes time: Limits to arbitrage for blockchain-based assets
<p><span>The dataset contains all historical order book snapshots and blockchain network information used to generate the results for the paper "Building Trust takes Time". </span></p> <p><span>A blockchain replaces central counterparties with time-consuming consensus pro</span><span>tocols to record the transfer of ownership.</span> <span>This settlement latency slows cross-</span><span>exchange trading, exposing arbitrageurs to price risk. Off-chain settlement, instead, </span><span>exposes arbitrageurs to costly default risk. We show with Bitcoin network and or</span><span>der book data that cross-exchange price differences coincide with periods of high </span><span>settlement latency, asset flows chase arbitrage opportunities, and price differences </span><span>across exchanges with low default risk are smaller. Blockchain-based trading thus </span><span>faces a dilemma: reliable consensus protocols require time-consuming settlement </span><span>latency, leading to arbitrage limits. Circumventing such arbitrage costs is possible </span><span>only</span> <span>by reinstalling trusted intermediation, which mitigates default risk.</span></p>
bioRxiv 10k with assets
<p>This dataset is a superset of the <a href="https://doi.org/10.5281/zenodo.3873702">bioRxiv 10k dataset</a>. It additionally includes the assets (usually images) that are linked by the XML files.</p> <p>The assets were retrieved from the <a href="https://www.biorxiv.org/tdm">bioRxiv's tdm bucket</a>. Assets that are not linked by the XML were omitted. Examples of those would be some large videos.</p> <p>This dataset serves a similar purpose as the <a href="https://doi.org/10.5281/zenodo.3873702">bioRxiv 10k dataset</a>, but for use-cases that require the assets. e.g. training and evaluation of figure image extraction.</p> <p>This dataset mirrors the exact same documents and structure as the <a href="https://doi.org/10.5281/zenodo.3873702">bioRxiv 10k dataset</a>. But rather than just containing the PDF and XML files, it also contains the linked assets (often images, but not necessarily).</p> <p>The dataset was created as part of <a href="https://elifesciences.org/">eLife</a>'s <a href="https://github.com/elifesciences/sciencebeam">ScienceBeam</a> project.</p>
ams-icdd-usecases: Use cases for employing ICDD containers for infrastructure asset management
<p>This repository provides two use cases for infrastructure asset management using information containers according to the <a href="https://www.iso.org/standard/74389.html">Information Container for linked Document Delivery standard (ISO 25197)</a>. Use Case 1 demonstrates the data preparation and result collection of bridge visual inspection with requirement- and delivery container. Use Case 2 demonstrates the pavement maintenance plan based on the existing condition data. Therefore, an additional connection to a relational database in the information container is provided in Use Case 2, which is registered within the container using an extension <a href="https://icdd.vm.rub.de/ontology/icdd/ExtendedDocument/">EXDOC:Extension for document types for the ISO 21597 ICDD Part 1 Container ontology</a>.</p> <p><strong>Full Changelog</strong>: <a href="https://github.com/RUB-Informatik-im-Bauwesen/ams-icdd-usecases/commits/v0.1">https://github.com/RUB-Informatik-im-Bauwesen/ams-icdd-usecases/commits/v0.1</a></p>
Dataset: Decentralized Cross-Blockchain Asset Transfers with Transfer Confirmation
<p>Dataset resulting from the experiments conducted in "Decentralized Cross-Blockchain Asset Transfers with Transfer Confirmation"</p>
CROSSBOW HLU3-UC1-TC1 Energy arbitrage revenues for storage asset
<p>In the process of energy arbitrage described in HLU3_UC3, storage assets buy energy from RES assets in valley price periods and later on sell this energy in peak price periods, thus earning some money for the shifting of the energy consumption to the periods were it more demanded (higher prices). The total benefits for the storage asset have some factors into account:</p> <ul> <li>The energy acquisition (charge) is only happening is there is possibility to sold (discharge) the energy in a near future with benefits (higher price)</li> <li>The energy is paid in the storage market at a price that compensate the RES owners for the low prices in the DA/ID markets. The energy is bought at the DA/ID market at the existing price</li> <li>Energy stored is sold in the most beneficial periods</li> </ul> <p>The dataset contains the revenues theoretically obtained in the DA/ID markets and the real revenues obtained considering the bilateral agreement with the storage asset. Fields:</p> <ul> <li>Time</li> <li>Payments: cost of the energy bought (including both markets)</li> <li>Revenues: earning for the energy sold</li> <li>Balance: revenues - payments</li> </ul>
Assets for 'Phase correlation on the edge for estimating cloud motion' submitted to Atmospheric Measurement Techniques
<p>1. CMV-26-07-2016_ARM-SGP.gif Sample cloud motion vectors from TSI camera images over the United States Atmospheric Radiation Measurement user facility’s Southern Great Plains site.</p> <p>2. raindrop_02-01-2017_ARM-SGP.gif Rotation of cloud motion vectors from raindrop contaminated TSI camera.</p>
Figure 1 in A review of nonlethal and lethal control tools for managing the damage of invasive birds to human assets and economic activities
Figure 1. The number of studies (i.e., field, lab, and modeling) using A) lethal methods to control populations at nesting, foraging, loafing, and roosting sites, B) nonlethal methods to control damage at urban nesting, foraging, loafing, and roosting sites, and C) nonlethal methods to control damage at agricultural foraging sites, including those conducted in the native or introduced ranges of the following birds considered invasive in the United States: rock doves (Columba livia; RODO), Eurasian collared doves (Streptopelia decaocto; EUCD), rose-ringed parakeets (Psittacula krameri; RRPA), monk parakeets (Myiopsitta monachus; MOPA), common mynas (Acridotheres tristis; COMY), European starlings (Sturnus vulgaris; EUST), and house sparrows (Passer domesticus; HOSP). Above each column on the left-hand side is the number of studies that were conducted in the field (i.e., not laboratory or modeling studies; if a study used multiple tools it was counted for each tool). The right-hand side is the subset of field studies for each category (D–F) that included damage assessments in the results.
Dataset: FlexShares Real Assets Allocation Index Fund (ASET) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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.