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708 results for “Global dataset”
NWEI: A global Nested Watershed dataset considering Endorheic basins and Islands
<h3><strong>contact</strong></h3> <p>Junzhi Liu (liujunzhi@lzu.edu.cn), Bin Zhang (zhangbin2023@lzu.edu.cn)</p> <h3><strong>description</strong></h3> <p>A global nested watershed dataset named NWEI (Nested Watershed dataset considering Endorheic basins and Islands) was developed based on the 3-arc-second-resolution hydrography dataset MERIT Hydro v1.0.1 automatically (<em>Yamazaki et al.</em>, 2019). Compared with the state-of-the-art HydroBASINS dataset, NWEI has more detailed and accurate watershed boundaries. </p> <h3><strong>Data organization</strong></h3> <p>Global data is stored by region.</p> <h3><strong>Filename</strong></h3> <p> "Basin_XXX.gdb.zip", XXX represents the region.</p>
Dataset (Global C-responses for CSES and CSES+Swarm+Obs database) presented in the recently submitted AGU manuscript "Electrical conductivity of mantle transition zone and water content revealed by the magnetic data of China Seismo-electromagnetic Satellite".
<p>Dataset (Global C-responses for CSES and CSES+Swarm+Obs database) presented in the recently submitted AGU manuscript "Electrical conductivity of mantle transition zone and water content revealed by the magnetic data of China Seismo-electromagnetic Satellite".</p>
Plant diversity darkspots for global collection priorities: time-to-event datasets per botanical country as defined by the World Geographical Scheme for Recording Plant Distributions (WGSRPD).
<p>Datasets used to predict the number of plant species remaining to be described and/or geolocated within a botanical country, which represents the third level of subdivision (generally equating to a political country) used by WGSRPD for recording plant distributions. The folder is composed of two subfolders <em>has_coords</em> and <em>has_no_coords</em> containing the time-to-event data for species with valid and no (invalidated) occurrence records within a given botanical country respectively<em>.</em></p> <ul> <li>Each folder contains a<strong> </strong>list of 361 botanical countries with the following 16 fields:</li> </ul> <pre><strong>species:</strong> species name<br><strong>time_ofdescription:</strong> year of the (first) description<br><strong>time_ofcollection:</strong> year of the collection of the earliest record<br><strong>family:</strong> species family name<br><strong>lifeform_description: </strong>the life form categorised into 4 classes <br><strong>CHELSA_bio_1: </strong>annual mean temperature (°C)<br><strong>CHELSA_bio_12:</strong> annual precipiation (mm)<br><strong>CHELSA_bio_15</strong>: temperature seasonality (-)<br><strong>CHELSA_bio_4</strong>: precipitation seasonality (-)<br><strong>elevation</strong>: elevation (m)<br><strong>range_size_area:</strong> total area of the botanical countries encompassing the species' native range <br>according to the World Checklist of Vascular Plants (WCVP) (km^2) <br><strong>taxo_activity</strong>: taxonomic activity calculated as the number of named authors in the World Checklist of Vascular Plants<br>describing species from the same family during the year of description of the species,<br>divided by the number of species described within the given family that year. <br><strong>num_records_per_year:</strong> geographic activity calculated as the number of occurrence records<br>collected within the native range of the species, divided by the number of years between <br>the earliest and the lastest (first) record collected within this range.<br><strong>num_uses</strong>: number of human uses<br><strong>time_todescription:</strong> number of years between the (first) description and 1753<br><strong>time_tocollection:</strong> number of years between the (first) description and the collection of the first record of the species<br><br></pre> <p> </p>
Global dataset of areas under cropland expansion pressure
<p>To reconcile global sustainability goals, such as protecting biodiversity and the climate, with agricultural production, a spatial understanding of potential future cropland expansion and potentially resulting trade-offs is required. Assuming that the globally most profitable land for cropland expansion is also under the highest pressure to be converted into cropland, we provide a global dataset on the areas under globally highest expansion pressure until 2030 considering future socio-economic and environmental conditions.</p> <p>Using the integrative land-use change model iLANCE (integrative land-allocation sequencer), the relative profitability of cropland expansion is assessed globally at 0.5° spatial resolution. Thereby, future environmental conditions for crop growth (under SSP585) are considered by the crop model PROMET. Socio-economic drivers of land-use change, regional economic conditions and global trade are taken into account by the Computable General Equilibrium model DART-BIO. Thereon based, the areas under the globally highest expansion pressure up to a global cropland increase of +30% (as an upper benchmark) are identified. The data on the area under expansion pressure at each pixel is provided in km² at 0.5° spatial resolution. Based on the relative profitability ranking, we provide additional spatial data at 0.5° spatial resolution indicating the percentage of global cropland expansion under which each pixel is among the globally most profitable ones (from 1% to 30% global cropland expansion). Accordingly, by overlaying both datasets, various scenarios of an increase in future cropland extent from 1% to 30% global cropland expansion can be investigated.</p> <p>The areas under highest expansion pressure are assessed without any restrictions on cropland expansion (EXP scenario) and under a conservation policy scenario that prohibits cropland expansion into forests, wetlands and strictly protected areas (CON scenario), thereby reflecting key aims of the Sustainable Development goals and recent efforts to stop deforestation, protect the climate and preserve biodiversity.</p> <p>Additionally, information on the area under expansion pressure under both scenarios, EXP and CON, is provided in km² at country level for different global cropland expansion scenarios from 1% to 30% (in 1% increments).</p> <p>The provided data could be used in integrated assessment models or impact studies to investigate various potential effects of different future cropland expansion scenarios, for example regarding biodiversity, climate, hydrology, local or regional agricultural production or socio-economic effects. In the study associated with this dataset, potential impacts on agricultural markets, biodiversity intactness and carbon storage are assessed.</p> <p>Information on the spatial patterns of future expansion pressure and resulting trade-offs as well as co-benefits could contribute to improving the spatial planning of conservation measures and to creating more efficient conservation policies.</p> <p> </p> <p><strong>Further information:</strong></p> <p>A detailed description on the methods and underlying data is available in:</p> <p>Schneider, J.M., Delzeit, R., Neumann, C., Heimann, T., Seppelt, R., Schuenemann, F., Söder, M., Mauser, W., Zabel, F. (2024): Effects of profit-driven cropland expansion and conservation policies. Nature Sustainability. </p> <p><a href="https://www.nature.com/articles/s41893-024-01410-x">https://doi.org/10.1038/s41893-024-01410-x</a></p> <p> </p> <p><strong>Contact</strong>:</p> <p>Please contact: Julia M. Schneider (<a href="mailto:Schneider.ju@lmu.de">Schneider.ju@lmu.de</a>), Department of Geography, Ludwig-Maximilians-Universität München (LMU), Munich, Germany.</p> <p>or</p> <p>Florian Zabel (<a href="mailto:florian.zabel@unibas.ch">florian.zabel@unibas.ch</a>), Departement of Environmental Sciences, University of Basel, Basel, Switzerland.</p> <p> </p> <p><strong>Funding</strong>:</p> <p>This project was supported by the German Federal Ministry of Education and Research (grant 031B0230B and grant 031B0788B).</p>
Global Marine Environment Dataset (GMED)
<p>The Global Marine Environment Datasets (GMED) is a compilation of publicly available climatic, biological and geophysical environmental layers featuring present, past and future environmental conditions. GMED covers the widest available range of environmental layers from a variety of sources and depths from the surface to the deepest part of the ocean. It has a uniform spatial extent, high-resolution land mask (to eliminate land areas in the marine regions), and high spatial resolution (5 arc-minute, c. 9.2 km near equator). The free online availability of GMED enables rapid map overlay of species of interest (e.g. endangered or invasive) against different environmental conditions of the past, present and the future, and expedites mapping distribution ranges of species using popular SDM algorithms. This archive features a snapshot of GMED dataset in 2018 as a single archive which is associated with the Earth Systems Data Science Manuscript.</p>
Datasets associated with: Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography
<p>Data associated with the paper 'Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography' by Lembrechts JJ et al., published in Global Ecology and Biogeography.</p> <p>Contains a dataset containing all extracted and measured temperature variables for all 106 measurement plots (climatedata), as well as the climate and species data used in the Species Distribution Models (SDMs). </p> <p>For details on the content of the table, see the readme-file, for details on methodology, see the original paper. </p>
Global River Ice Dataset - validation dataset
<p><strong>Documentation for nws_breakup_nogeo.csv and nws_freezeup_nogeo.csv</strong></p> <p>Alaskan river ice records from National Weather Service (NWS), including <strong>nws_breakup_nogeo.csv</strong> containing location (description) and dates of ice breakup and related conditions and <strong>nws_freezeup_nogeo.csv </strong>containing location (description) and dates of ice freeze-up and related conditions. Note that both dataset do not contain exact geolocations of the observation. We thank Dr. Scott Lindsey at the Alaska-Pacific River Forecast Center for providing these datasets.</p> <p><strong>Documentation for landsat_river_ice_validation.csv</strong></p> <p>This file contains 20,687 same-day river ice condition from Landsat and from in situ, and consists of the following associated properties for each comparison:</p> <ol> <li>date: The date on which both the Landsat river ice (length) fraction and in situ river ice condition were observed (data type: string; format: "YYYY-MM-DD").</li> <li>ice_in_situ: The ice condition on rivers observed in situ. For records from NWS, we assumed river has been ice covered between the date of "first_ice" to the date of "breakup" in the following year and ice-free between the date of "breakup" and the following "first_ice" date. For records from Water Survey of Canada, river was treated as ice-covered whenever the daily "Flow" data were flagged with "B"–meaning backwater effect (data type: integer; range: 0 (ice-free) or 1 (ice-covered)).</li> <li>ice_landsat: The river ice length fraction derived from Landsat image (data type: float; range: [0, 1]).</li> <li>cloud_landsat: The cloud fraction derived from Landsat image (data type: float; range: [0, 0.25]).</li> <li>LANDSAT_SCENE_ID: The unique Landsat TOA image identifier (data type: string).</li> <li>site_id: The ID of the site in its original dataset.</li> <li>dat_source: The source of the in situ river ice record (data type: string; values: ("National Weather Service (Alaska)", "Water Survey of Canada").</li> <li>longitude: The longitude of the site (data type: float, format: decimal degree).</li> <li>latitude: The latitude of the site (data type: float, format: decimal degree).</li> </ol> <p>A subset (N = 18,930) of this dataset was used in the evaluation of the river ice classification. This subset was calculated by applying the following two constraints on the full dataset in the <strong>landsat_river_ice_validation.csv</strong>:</p> <ol> <li><span class="math-tex">\(cloud\_landsat ≤ 0.05\)</span></li> <li><span class="math-tex">\(site\_id \neq 10BE013\)</span> & <span class="math-tex">\(site\_id \neq 08KE016\)</span></li> </ol> <p>The second constraint exclude two Canadian sites from the evaluation as via manual inspection, we found that the Landsat-derived ice fraction for this two sites came from river reaches that were different from where the in situ records were observed.</p>
Global River Ice Dataset
<p>This dataset is public for a manuscript under review. The dataset consists of one csv file containing estimated river ice (length) fraction based on images from USGS Landsat satellite missions. </p> <p><strong>Documentation for the Global River Ice Dataset</strong></p> <p>This csv file contains 840,187 records (rows) of river ice length fraction derived from Landsat satellite images.</p> <p>For each river ice fraction, we have the following associated properties:<br> 1. date: The date on which the image was captured (format: YYYY-MM-DD; data type: string)<br> 2. river_ice_fraction: The length fraction of ice cover (format: decimal number; data type: float; range: [0, 1])<br> 3. cloud_fraction: The length fraction of cloud cover (format: decimal number; data type: float; range: [0, 1])<br> 4. topo_shadow: Average topographic shadow along river centerline (format: decimal number; data type: float; range: [0, 1] with 0 meaning fully in shadow)<br> 5. LANDSAT_SCENE_ID: The unique Landsat TOA image identifier (data type: string)<br> 6. PATH: The path of the Landsat image (under <a href="https://landsat.gsfc.nasa.gov/the-worldwide-reference-system/">WRS-2</a>), roughly related to the longitudinal position of the image (data type: integer)<br> 7. ROW: The row of the Landsat image (under <a href="https://landsat.gsfc.nasa.gov/the-worldwide-reference-system/">WRS-2</a>), roughly related to the latitudinal position of the image (data type: integer)<br> 8. N_river_pixel: Number of total centerline points intersecting with the Landsat image (data type: integer)<br> 9. N_clear_river_pixel: Number of centerline points free from cloud or cloud shadow cover that intersected with the Landsat image (data type: integer)</p> <p>The dataset used for analysis is a subset (No. of records: 407,542) of this dataset with implementing the following three constraints:</p> <ol> <li> <span class="math-tex">\(N\_clear\_river\_pixel ≥ 333\)</span></li> <li><span class="math-tex">\(topo\_shadow ≥ 0.95\)</span></li> <li><span class="math-tex">\(cloud\_fraction ≤0.25\)</span></li> </ol>
Eulerian modelling of the three-dimensional distribution of seven popular microplastic types in the global ocean dataset
<p>Dataset for the paper "Eulerian modelling of the three-dimensional distribution of seven popular microplastic types in the global ocean" by A. S. Mountford and M. A. Morales Maqueda.</p> <p>ORCA2_5d_00010101_00011231_ptrc_T_con.nc<a href="https://zenodo.org/api/files/c6c9cbac-d5db-452a-aade-fd852db07351/ORCA2_5d_00010101_00011231_ptrc_T_con.nc"> </a> - control experiment (year 50)</p> <p>ORCA2_5d_00010101_00011231_ptrc_T_30m.nc - 30 m year<sup>-1</sup> piston velocity sensitivity experiment (year 50)</p> <p>ORCA2_5d_00010101_00011231_ptrc_T_90m.nc - 90 m year<sup>-1</sup> piston velocity sensitivity experiment (year 50)</p> <p>ORCA2_5d_00010101_00011231_ptrc_T_50.nc - neutrally buoyant sensitivity simulation (year 50)</p> <p>plastic_1_ts.nc - positively buoyant time series</p> <p>plastic_2_ts.nc - neutrally buoyant time series</p> <p>plastic_3_ts.nc - negatively buoyant time series</p> <p>plastic_input_ORCA2.nc - plastic input data file</p> <p>ORCA2_5d_00010101_00011231_grid_U.nc & ORCA2_5d_00010101_00011231_grid_V.nc - ocean velocity files</p>
Dataset: Global Electricity Sector Decarbonization Modelling
<p>This dataset contains the supporting materials for the PhD thesis "Global Electricity Sector Decarbonization Modelling". The following list of data packages and files are compiled from this research outputs.</p> <p><strong>Data Package 1: The main output datasets</strong><br> File 1: Definition of the zones in this study<br> File 2: Statistics on land cover pixels updated from OSM<br> File 3: Validation of the eligibility masks with existing sites<br> File 4: Validation of the estimated CF profile for EU countries<br> File 5: Summary of the available area, technical potential and capacity potential in this study<br> File 6: Aggregation of the estimated 10-year hourly CF into 288 month-hour profile<br> File 7: The projection of electricity demand profile for each zone in 288 month-hour profile<br> File 8: The overall aggregated output covariance matrices for each country</p> <p><strong>Data Package 2: The estimated 10-years hourly capacity factor of all available technologies and capacity factor tranches in every zone in the world</strong></p> <p><strong>Data Package 3: The solutions of optimal climate-related energy mix with different objectives in global electricity systems</strong></p> <p><strong>Data Package 4: The modelling results of electricity sector decarbonization pathways by country/district</strong></p> <p><strong>Data Package 5: Python scripts of the electricity sector decarbonization modelling framework</strong></p>
Development of a global 30-m impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform
<p>An accurate global impervious surface map at a resolution of 30-m for 2015 by combining Landsat-8 OLI optical images, Sentinel-1 SAR images and VIIRS NTL images based on the Google Earth Engine (GEE) platform.</p>
Dataset for: Navigating Ecosystem Services Trade-offs: A Global Comprehensive Review
<p><strong>Methods</strong></p> <p>The dataset is the output of a comprehensive literature-based search that aims to collate all the evidence on where ES relationships have been mentioned and addressed. We applied systematic mapping which is based on the “Guidelines for Systematic Review in Environmental Management” developed by the Centre for Evidence-Based Conservation at Bangor University (Pullin and Stewart 2006).</p> <p>The methodological framework followed the standard stages outlined for systematic mapping in environmental sciences (James et al. 2016). Briefly, we defined the scope and objectives:</p> <p>· We comprehensively review and further explore the global evidence of ES trade-offs and synergies focusing on all systems including terrestrial, freshwater, and marine.</p> <p>· We compiled the evidence on trade-offs and synergies among multiple ES interacting across various ecosystems.</p> <p>· We performed a geographical and temporal trend analysis exploring the distribution of studies across the world examining how the focus on various ecosystem types and ES categories has evolved to highlight gaps and biases.</p> <p>Then we set the criteria for study inclusion (Table 1), searched the evidence, coded, and produced the database. Extracted article information including the specific criteria is detailed in Table 1.</p> <p>The first step was to search the ISI Web of Knowledge core collection (http://apps.webofknowledge.com) database, targeting the search on the ecosystem services literature and studies dealing with trade-offs/synergies, win-win outcomes or bundles when managing different ecosystem services in the landscape/seascape. All peer-reviewed journal articles written in English and Spanish have been considered for review.</p> <p>The <em>peer-reviewed </em>literature from 2005 to 2021 was reviewed identifying relevant studies according to specific search terms. The relevant search terms and descriptive words derived from (Howe et al. 2014) adding “bundles” and “co-benefits”. Boolean nomenclatures ‘*’ = all letters were allowed after the *, were used on the root of words where several different endings applied (Figure 1). Search terms used were:</p> <p>(“*ecosystem service*” OR “environment* service*” OR “ecosystem* approach*” OR “ecosystem good*” OR “environment* good*”)</p> <p>AND</p> <p>(“*trade-off*” OR “tradeoff*” OR “synerg*” OR “win-win*” OR “bundle*” OR “cost*and benefit*” OR “co-benefit*”) n=5194</p> <p>Papers were preliminarily coded with a semantic analysis using the R package Bibliometrix (<a href="http://www.bibliometrix.org">http://www.bibliometrix.org</a>).</p> <p>In the second step (Figure 1) papers were preliminarily coded with a semantic analysis using the R package Bibliometrix (http://www.bibliometrix.org). Papers were classified according to three systems: terrestrial, marine, and freshwater (Table 1). Papers with multiple systems, transitional habitats or those that could not be classified were classified as “other” (Mazor et al. 2018). Articles were classified based on the occurrence of the most frequent system words in their title, keywords, and abstract (Mazor et al. 2018). The set of system-specific words was determined by extracting the 250 most frequently used keywords from all considered articles and assigning each word to either system (articles could fall into just one of the four categories). Using this technique, we managed to classify 100% of the papers. To further enrich the dataset and make it a useful repository for science and policy, an additional sub-classification was performed, categorizing papers into the following categories: Coastal, Urban, Wetlands, Forest, Mountain, Freshwater, Agroecosystems, and Others that mainly represented multiple ecosystems (Table S1). This comprehensive classification approach enhances the dataset’s utility for various scientific and policy-making applications.</p> <p>In the third step (Figure 1), applying the same technique, we classified the papers into four ES categories: habitat (supporting biodiversity related), provisioning, regulating, and cultural services (De Groot et al. 2010; MEA 2005; Sukhdev 2010; Wallace 2007). For the classification into ES categories, articles could fall into one or more of the four categories (see Table 1 for example the keywords used to classify ecosystems, ES categories, and countries). Applying this technique, we excluded 2149 papers that weren’t classified in any of the ecosystem services types categories resulting in 3629 papers (see Figure 1).</p> <p>In the fourth step (Figure 1), an initial screening was conducted to identify papers that did not align with the review objectives of assessing ecosystem services trade-offs and synergies to inform policy and management decisions. We manually reviewed the titles of each paper in the dataset, excluding those that were from other fields or did not align with the review objectives. In this initial assessment, we excluded 347 papers, leaving a total of 3,286 papers for further review. A descriptive analysis of this 3286 article dataset was performed to examine the distribution of ES categories within each ecosystem type over the specified period. This analysis allowed us to conclude the prevalence of each ecosystem service category in different ecosystem types, identifying temporal trends and patterns. The number of occurrences was calculated for each ES category within each ecosystem type, expressed as counts. This allowed for the comparison of ecosystem service distributions across the selected ecosystem types.</p> <p>In the fifth step (Figure 1), we employed an approach to visually represent the geographical distribution and focus of ES studies across the world. With the classification of studies in ES categories and the types of ecosystems, the papers were coded according to the country where the study was performed. It was possible to assign a specific country to 2636 studies, removing 650 studies that did not specify the country of study. From these 2636 papers classified, a proportion were global studies that consider several countries under study (499 global studies).</p> <p>We developed global maps (Figure 1), each offering a unique perspective on the ES research landscape. The first map presents the total number of ES trade-off studies conducted worldwide, illustrating the geographical spread and concentration of research efforts to provide a clear overview of regions that have been extensively studied and those that may require more attention in future research. Additionally, we calculated two key metrics to assess research productivity more comprehensively: the number of research papers per capita and the number of research papers relative to Gross Domestic Product (GDP). For population and GDP, we used the most recent available data from the World Bank (https://data.worldbank.org). These alternative metrics normalize the data based on economic output and population size, providing a more balanced view of research activity across different countries (Figures S3).</p> <p>Detailed maps were created featuring pie charts that highlight the different categories of ES and ecosystem types addressed for each country. These charts offer an understanding of how various ES categories and ecosystems are represented in different parts of the world. Finally, we assessed ES trade-off studies to world regions (Africa, Antarctica, Asia, Australasia, Europe, Latin America, and North America) looking at the relationships between the categories of ES. We considered papers that evaluated more than one category of ES and the papers that considered only one category of ES. This country-level analysis offers insights into regional research trends and priorities, contributing to a more localized understanding of ES studies.</p> <p>In the sixth step (Figure 1), each publication in this review was critically appraised to evaluate the quality of the papers included in the review. The foundation for our critical appraisal stems from the comprehensive and multidimensional approach of Belcher et al. (2016) that is framed to evaluate research quality, which aligns well with the interdisciplinary nature of our study. Belcher et al. (2016) developed a robust framework that incorporates essential principles and criteria for assessing the quality of transdisciplinary research. This is particularly relevant for ecosystem services science and our review that contributes to advancing current knowledge by systematically synthesizing evidence on relationships among various ES across these diverse systems.</p> <p>The Belcher et al. (2016) framework emphasizes four main principles: relevance, credibility (which we have adapted as methodological transparency), legitimacy (generalizability in our context), and effectiveness (significance). A continuous scoring system (ranging from 0 to 1) was applied for the four main criteria to maintain simplicity and consistency across the large number of studies. In this system, a value closer to 0 indicates that the criteria are not met, while a value closer to 1 indicates that the criteria are more closely met. This scoring method was a useful indicator of the overall quality of the paper and how well the article met the review's goals overall.</p> <p>Methodological Transparency was assessed based on the clarity and completeness of methodological descriptions, including data availability, the rigor of statistical analyses, methodological detail, and reproducibility of the findings. This criterion assesses the transparency and rigor of the study's methodology, including data collection, analysis, and reporting (Belcher et al. 2016). Relevance was evaluated by the study's alignment with the review's objectives, its importance to the field, and its practical applicability. This includes the extent to which the study addresses pertinent research questions of the study (Belcher et al. 2016). Significance was determined by the novelty of the study, its theoretical contributions, and practical implications. This includes evaluating whether the study presents new ideas, concepts, or frameworks that advance the field of ES relationships (Belcher et al. 2016). Generalizability was judged based on the contextual applicability and transferability of the study's findings to other settings. This includes assessing whether the study's results can be applied broadly and whether the context is sufficiently described to understand its applicability (Belcher et al. 2016).</p> <p>In the final seventh step (Figure 1) a detailed assessment was performed incorporating sub-categories within each main critical appraisal criteria to allow for a more comprehensive analysis (Table S2). To this end, a random sample of 20% of the studies was selected (574 papers) and a thorough revision by subcategories for each main criteria was performed (see Table S2). For the random selection of 20% of the studies from the dataset, we loaded the dataset into R and set a random seed to ensure reproducibility. We then calculated the sample size as 20% of the total dataset and used the sample function to select this subset of studies randomly. This method ensured an unbiased and representative sample. The selected subset was assessed for a further detailed critical appraisal adding sub-categories to each criterion (Table S2). A qualitative assessment sub-category was also inclded to explain the rationale behind each assigned score in the dataset.</p> <p>We finally created a heatmap to visualize average scores by continent across the four criteria. Utilizing R programming (R Core Team 2023) for data aggregation with the <em>dplyr </em>package, the study computed mean scores for each criterion by continent. The aggregated data was visualized using a heatmap created with the ggplot2 package, where the color intensity of each cell represented the mean scores, facilitating a visual comparison across continents. This analysis offers a quantitative foundation for identifying areas of strength, evaluating performance by continent and potential for improvement, and further elucidating the different quality and focus of research within the ES trade-offs research.</p> <p><strong>References:</strong></p> <p>Belcher, B. M., Rasmussen, K. E., Kemshaw, M. R., & Zornes, D. A. (2016). Defining and assessing research quality in a transdisciplinary context. Research Evaluation, 25(1), 1-17.</p> <p>De Groot, R. S., Alkemade, R., Braat, L., Hein, L., & Willemen, L. (2010). Challenges in integrating the concept of ecosystem services and values in landscape planning, management, and decision-making. Ecological Complexity, 7, 260-272.</p> <p>Howe, C., Suich, H., Vira, B., Mace, G.M., 2014. Creating win-wins from trade-offs? Ecosystem services for human well-being: A meta-analysis of ecosystem service trade-offs and synergies in the real world. Global Environmental Change 28, 263-275.</p> <p>Mazor, T.A.-O., Doropoulos, C.A.-O., Schwarzmueller, F.A.-O., Gladish, D.A.-O.X., Kumaran, N.A.-O., Merkel, K.A.-O.X., Di Marco, M., Gagic, V.A.-O. (2018). Global mismatch of policy and research on drivers of biodiversity loss. Nature Ecology & Evolution 2, 1071-1074.</p> <p>Millennium Ecosystem Assessment (MEA).(2005). Ecosystems and Human Well-Being: Synthesis. Island Press, Washington DC.</p> <p>Pullin, A. S., & Stewart, G. B. (2006). Guidelines for systematic review in conservation and environmental management. Conservation biology, 20(6), 1647-1656.</p> <p>Sukhdev, P. (2010). The economics of ecosystems & biodiversity: mainstreaming the economics of nature: a synthesis of the approach, conclusions, and recommendations of TEEB. UNEP.</p> <p>R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/</p> <p>Wallace K.J. (2007). Classification of ecosystem services: Problems and solutions. Biological Conservation 139, 235-246.</p> <p><strong>Dataset</strong></p> <p>Name of the file: MMartinez-Harms-Dataset-ESTrade-offs_Zenodofinal.xlsx</p> <p>Description: Complete database of the 3286 studies reviewed in this synthesis coded by:</p> <table> <tbody> <tr> <td><strong>N</strong></td> <td>Number of papers</td> </tr> <tr> <td><strong>AU</strong></td> <td>Authors</td> </tr> <tr> <td><strong>TI</strong></td> <td>Title</td> </tr> <tr> <td><strong>SO</strong></td> <td>Publication name</td> </tr> <tr> <td><strong>Included</strong></td> <td>identify papers that did alignd (yes) or did not (No) align with the review objectives.</td> </tr> <tr> <td><strong>JI</strong></td> <td>Scientific Journal</td> </tr> <tr> <td><strong>AB</strong></td> <td>Abstract</td> </tr> <tr> <td><strong>DE</strong></td> <td>Authors’ Keywords</td> </tr> <tr> <td><strong>ID</strong></td> <td>Keywords associated by ISI database</td> </tr> <tr> <td><strong>LA</strong></td> <td>Language</td> </tr> <tr> <td><strong>DT</strong></td> <td>Document Type (article, review, editorial, book chapter, letter, meeting abstract)</td> </tr> <tr> <td><strong>TC</strong></td> <td>Times Cited</td> </tr> <tr> <td><strong>PY</strong></td> <td>Publication Year</td> </tr> <tr> <td><strong>SC</strong></td> <td> Subject Categories</td> </tr> <tr> <td><strong>WC</strong></td> <td>Web of Science categories</td> </tr> <tr> <td><strong>System</strong></td> <td>Freshwater, terrestrial, marine, and other</td> </tr> <tr> <td><strong>Habitat</strong></td> <td>Habitat ES category</td> </tr> <tr> <td><strong>Regulating</strong></td> <td>Regulating ES category</td> </tr> <tr> <td><strong>Cultural</strong></td> <td>Cultural ES category</td> </tr> <tr> <td><strong>Provisionning</strong></td> <td>Provisionning ES category</td> </tr> <tr> <td><strong>Country_Classification</strong></td> <td>Country of study</td> </tr> <tr> <td><strong>Continent</strong></td> <td>Continent of study</td> </tr> <tr> <td><strong>Synergies_Trade-offs</strong></td> <td>The term trade-off involves losing one quality or aspect of something in return for gaining another quality or aspect. Synergy is a situation where the use of one ES directly increases the benefits supplied by another ES or a win-win situation</td> </tr> <tr> <td><strong>Stakeholder_Participation</strong></td> <td>The study interacts with stakeholders at any moment of the study or policy decisions. </td> </tr> <tr> <td><strong>Critical_Appraisal</strong></td> <td>Articles are appraised to ensure that they are adequate for answering the research question</td> </tr> <tr> <td><strong>Relevance</strong></td> <td>Relevance to the review question (0 no relevance, 1 relevance)</td> </tr> <tr> <td><strong>Methodological_Transparency</strong></td> <td>Assessed based on the clarity and completeness of methodological descriptions</td> </tr> <tr> <td><strong>Significance</strong></td> <td>Significance of the contribution (are new ideas offered?)</td> </tr> <tr> <td><strong>Generalizability</strong></td> <td> Is the context specified, and do the ideas apply in other contexts?</td> </tr> <tr> <td><strong>Total_Score</strong></td> <td>The sum total score across all critical appraisal criteria </td> </tr> <tr> <td><strong>Category</strong></td> <td>Coastal, Urban, Wetlands, Forest, Mountain, Freshwater, Agroecosystems, and Others mainly represented multiple ecosystems</td> </tr> <tr> <td><strong>Uses_INVEST</strong></td> <td>This paper uses the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) tool from the Natural Capital Project to assess ecosystem services (Yes) or Not (No)</td> </tr> </tbody> </table>
The Koo Dataset: An Indian Microblogging Platform With Global Ambitions
<p>This is the dataset released with the <a href="https://arxiv.org/abs/2401.07599">paper</a> titled "The Koo Dataset: An Indian Microblogging Platform With Global Ambitions". </p> <p>The dataset contains 43 JSON files containing the posts made on the platform, 34 JSON files for the comments, the shares and the likes. It also contains a JSON file for the user profiles. The metadata included in each data type is described in the paper.</p> <p>If you use our dataset, please cite the arXiv version:</p> <p><code>@misc{mekacher2024koo,</code><br><code> title={The Koo Dataset: An Indian Microblogging Platform With Global Ambitions}, </code><br><code> author={Amin Mekacher and Max Falkenberg and Andrea Baronchelli},</code><br><code> year={2024},</code><br><code> eprint={2401.07599},</code><br><code> archivePrefix={arXiv},</code><br><code> primaryClass={cs.SI}</code><br><code>}</code></p>
Software sustainability of global impact models (Dataset and analysis script)
<p><strong><em>slocount.py</em></strong>: This script calculates the number of comment lines, total lines of code (TLOC) and source lines of code (SLOC). It uses a code line counter developed by Ben Boyter, which must be installed (https://github.com/boyter/scc.). The source code links to the global impact models (GIMs) can be found in the 'ISIMIP_models.xlsx' file.</p> <p><strong><em>active_dev.py</em></strong>: This script plots the number of active developers for each GIM across 10 sectors. It utilizes data from the 'active_dev.csv' file, which lists the GIMs and their respective number of developers.</p> <p><strong><em>cocomo.py</em></strong>: This script estimates the effort required for software development using the methodology proposed by Sachan et al. 2016 (https://doi.org/10.1016/j.procs.2016.06.107). It also generates plots for these estimates.</p> <p><strong><em>comment_density_modularity.py</em></strong>: This script calculates the comment density and evaluates the modularity of the modules. It also produces plots for these metrics.</p> <p><strong><em>code_standard.py</em></strong>: This script uses Pylint (<a href="https://pylint.readthedocs.io/en/latest/user_guide/usage/output.html">https://pylint.readthedocs.io/en/latest/user_guide/usage/output.html</a>) to check if the source code, either in part or in its entirety, adheres to the PEP8 coding standard. It also generates lint scores for the source code.</p> <p><strong><em>line_count.zip</em></strong>: This file contains the results of counting the number of comment lines, TLOC and SLOC for each GIM.</p> <p><strong><em>lint_score.zip</em></strong>: This file contains the results of running pylint on GIMs that include Python in their source code. Results also include lint score per GIM</p>
Dataset for manuscript "Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model"
<p>Datasets and Jupyterlab python script for plotting all figures relevant to the mansucript "Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model" by Herbert et al.</p> <p>https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1538/</p> <p>Data needs to be unzipped and paths (input and output) updated in the jupyterlab python script.</p> <p> </p>
A global dataset of freshwater fish trophic interactions
<p>Dataset associated with Ridgway and Wesner. 2024. A global dataset of freshwater fish trophic interactions. Scientific Data.</p>
Plot-Rice v1.0: A global plot-based rice benchmark dataset with spatiotemporal heterogeneity for scientific deep learning
<p>This dataset (Plot-Rice v1.0) offers a global rice benchmark dataset for scientific deep learning at a 10-meter resolution for the year 2023. Plot-Rice v1.0 is constructed based on Sentinel-1 and Sentinel-2 images, encompassing plot-level rice labels and corresponding multi-source feature time series from 20 countries worldwide. It fully considers the spatiotemporal heterogeneity of rice and supports continuous updates, providing a data benchmark for performance comparisons of deep learning models.</p>
SynRS3D : A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery
<h1><strong>SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery</strong></h1> <h3><strong>Neural Information Processing Systems (Spotlight), 2024</strong></h3> <p>For more details, please refer to our <a href="https://arxiv.org/pdf/2406.18151">paper</a> and visit our <a href="https://github.com/JTRNEO/SynRS3D">GitHub repository</a>.</p> <h2><strong>Overview</strong></h2> <p><strong>TL;DR:</strong><br>SynRS3D is a comprehensive synthetic remote sensing dataset designed to improve global 3D semantic understanding from monocular high-resolution imagery. It includes data for three key tasks:</p> <ul> <li>Height estimation</li> <li>Land cover mapping</li> <li>Building change detection</li> </ul> <h2><strong>Dataset Structure</strong></h2> <p>The dataset consists of 17 folders and includes a total of 69,667 images at a resolution of 512x512. After downloading and extracting the files, ensure the directory structure follows this format:</p> <p>${DATASET_ROOT} # Example: /home/username/project/SynRS3D/data/grid_g05_mid_v1<br>├── opt # RGB images (.tif), also used as post-event images for building change detection<br>├── pre_opt # RGB images (.tif), used as pre-event images for building change detection<br>├── gt_nDSM # Normalized Digital Surface Model (nDSM) images (.tif)<br>├── gt_ss_mask # Land cover mapping labels (.tif)<br>├── gt_cd_mask # Building change detection masks (.tif, 0 = no change, 255 = change area)<br>└── train.txt # List of training data filenames</p> <p>The land cover mapping labels (`gt_ss_mask`) are mapped to the following categories:</p> <ul> <li>Bareland: 1</li> <li>Rangeland: 2</li> <li>Developed Space: 3</li> <li>Road: 4</li> <li>Trees: 5</li> <li>Water: 6</li> <li>Agriculture land: 7</li> <li>Buildings: 8</li> </ul> <h2><strong>Image Breakdown by Folder</strong></h2> <p>The dataset is organized into grid-like and irregular terrain. It includes a range of ground sampling distances (GSDs) and variations in building heights. The folder naming convention indicates these characteristics: <br>- `grid` = grid-like terrain <br>- `terrain` = irregular terrain <br>- `g005`, `g05`, `g1` = GSD ranges (0.05m–0.3m, 0.3m–0.6m, and 0.6m–1m, respectively) <br>- `low`, `mid`, `high` = building height variations</p> <p>The dataset includes the following image counts:</p> <p>- 1,430 images – `terrain_g05_mid_v1`<br>- 10,000 images – `grid_g05_mid_v2`<br>- 2,354 images – `terrain_g05_low_v1`<br>- 3,707 images – `terrain_g05_high_v1`<br>- 880 images – `terrain_g005_mid_v1`<br>- 2,127 images – `terrain_g005_low_v1`<br>- 11,325 images – `grid_g005_mid_v2`<br>- 1,212 images – `terrain_g005_high_v1`<br>- 348 images – `terrain_g1_mid_v1`<br>- 4,285 images – `terrain_g1_low_v1`<br>- 904 images – `terrain_g1_high_v1`<br>- 3,000 images – `grid_g005_mid_v1`<br>- 2,997 images – `grid_g005_low_v1`<br>- 4,000 images – `grid_g005_high_v1`<br>- 7,000 images – `grid_g05_mid_v1`<br>- 7,098 images – `grid_g05_low_v1`<br>- 7,000 images – `grid_g05_high_v1`</p> <h2><strong>Citation</strong></h2> <p>If you find SynRS3D useful in your research, please consider citing:</p> <div> <div>@article{song2024synrs3d,</div> <div>title={SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery},</div> <div>author={Song, Jian and Chen, Hongruixuan and Xuan, Weihao and Xia, Junshi and Yokoya, Naoto},</div> <div>journal={arXiv preprint arXiv:2406.18151},</div> <div>year={2024}</div> <div>}</div> </div> <h2><strong>Contact</strong></h2> <p>For any questions or feedback, feel free to reach out via email: <strong> song@ms.k.u-tokyo.ac.jp</strong>.</p> <p>Enjoy using SynRS3D!</p>
Global tropical cyclone size and intensity reconstruction dataset for 1959–2022 based on IBTrACS and ERA5 data
<p>A global long-term tropical cyclone (TC) size and intensity reconstruction dataset is generated, covering a time period from 1959 to 2022, with a 3-hour temporal resolution. The machine learning model was established by taking ERA5-derived 10 m azimuthal mean azimuthal wind profiles in six basins for which TCs were generated as input, while the maximum sustained wind speed and radius of maximum wind from the International Best Track Archive for Climate Stewardship (IBTrACS) was used as the learning target. An empirical wind–pressure relationship and six wind profile models were employed to estimate the minimum central pressure and outer sizes (radial distances from the cyclone center to locations where sustained wind speeds of 34, 50 and 64 knots are observed on surface) of the TCs, respectively. Compared to the IBTrACS dataset, the reconsturction dataset contains approximately 3–4 times more data points per characteristic.</p> <p>Over all, this dataset is in terms of both coverage and good accuracy.</p>
ASM-SS: The First Quasi-Global High Spatial Resolution Coastal Storm Surge Dataset Reconstructed from Tide Gauge Records
<p>The ASM-SS dataset is a high spatial resolution (every 10 km per node along the coastline), long-term (over 80 years from 1940 to 2020), quasi-global (within 45°S-45°N), hourly data-driven storm surge dataset. Each NetCDF file includes five parameters: longitude, latitude, nodes, time, and surge level. Longitude and latitude are the location information of nodes in degree; the unit of time is accumulated hours since 1900-01-01 00:00:00; surge levels are given in meters. Users can use longitude, latitude, and time as keywords to select surge levels at nodes of interest within a target period. </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.