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128 results for “landscape mapping”
Regional landform and landscape digital maps for the Eastern Guiana Shield
<p>Archive containing digital <strong>maps of 'landform types' and 'landscape units' for French Guiana and the State of Amapa (Brazil).</strong> These maps accompany the paper 'Using textural analysis for regional landform and landscape mapping, Eastern Guiana Shield', <em>Geomorphology</em> (doi:10.1016/j.geomorph.2 018.03.017) and have been produced according to the methods presented therein.</p> <p><br> </p>
Data and supplementary material used for Soundscapes to Landscapes soundscape mapping
<p>This repository contains supporting data products to enable the soundscape mapping outlined in the associated publication (DOI forthcoming). Data were used to extract acoustic recording location environmental data for training random forest models to spatially predict 2021 ecoacoustic metrics. The accompanying code will be linked to the GitHub repository. Files include:</p> <p>Data:</p> <ul> <li>clustered_fold_k10.rsd: indices of the model data used if geoCV approach</li> <li>extracted_predictors_vif3.csv: site-specific predictor values extracted from predictors_annual_20230223.tif</li> <li>final_predictors_vif3.csv: a two column table summarizing the VIF selected predictors</li> <li>final_sites_2017-2021.csv: the list of 1,195 potential sites</li> <li>predictor_sprmn_corr.csv: correlation matrix for predictors in model data</li> <li>predictors_annual_20230223.tif: all predictors </li> <li>response_df_200623.csv: site level ecoacoustic metrics</li> </ul> <p>Results:</p> <ul> <li>map_correlations.tar: pairwise response map correlations</li> <li>pdps.tar: partial dependence plot data</li> <li>performance.tar: model performance summaries</li> <li>predictions_maps.tar: final median and IQR model prediction surfaces</li> <li>variable_importance.tar: summaries for variable importance analyses</li> </ul> <p>Contact Colin Quinn at cq73@nau.edu for questions related to this repository or the underlying work. Original wav recordings are expected to be made publicly available on the NASA DAACs in the near future. </p>
Map of 1992-2015 landscape change trajectories
<p>Map of 1992-2015 landscape change trajectories. Landscapes are colored depending on their change trajectories and a percentage of changed area; small < 10%, medium (10% to 30%), and large (> 30%).</p> <p>To access and visualize the map use: <a href="https://landgis.opengeohub.org/#/?base=OpenTopoMap&opacity=80&layer=ldg_landscape.degradation_sil.9km_c"><strong>https://landgis.opengeohub.org/#/?base=OpenTopoMap&opacity=80&layer=ldg_landscape.degradation_sil.9km_c</strong></a></p> <p>Creation of this map is explained in details at <a href="https://www.sciencedirect.com/science/article/pii/S0303243418305841">https://www.sciencedirect.com/science/article/pii/S0303243418305841</a> (preprint at <a href="https://eartharxiv.org/k3rmn/">https://eartharxiv.org/k3rmn/</a>).</p>
ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al. (2019)
<p><strong>ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)</strong></p> <p>**When using the GIS data included in these map packages, please cite all of the following:</p> <blockquote> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. <a href="http://doi.org/10.1371/journal.pone.0220457">http://doi.org/10.1371/journal.pone.0220457</a></p> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. <a href="https://doi.org/10.5281/zenodo.2572018">https://doi.org/10.5281/zenodo.2572018</a></p> </blockquote> <p><strong>OVERVIEW OF CONTENTS</strong></p> <p>This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:</p> <ul> <li>Raw DEM and Soils data <ul> <li>Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the <a href="https://viewer.nationalmap.gov/basic/">National Elevation Dataset</a>) <ul> <li><strong>DEM_Individual_Tiles</strong>: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.</li> <li><strong>DEMs_Merged</strong>: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM. </li> </ul> </li> <li> Soils Data (Map services and data available from <a href="https://data.nal.usda.gov/dataset/natural-resources-conservation-service-web-soil-survey">Natural Resources Conservation Service Web Soil Survey</a>, U.S. Department of Agriculture) <ul> <li><strong>Animas-Dolores_Area_Soils</strong>: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).</li> <li><strong>Cortez_Area_Soils</strong>: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).</li> </ul> </li> </ul> </li> <li>ArcGIS Map Packages <ul> <li><strong>Goodman_Watershed_Full_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).</li> <li><strong>Goodman_Watershed_Mesa-Only_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.</li> <li><strong>Crow_Canyon_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).</li> <li><strong>Coffey_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).</li> </ul> </li> </ul> <p>For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."</p> <p><strong>LICENSES</strong></p> <p>Code: <a href="http://opensource.org/licenses/MIT">MIT</a> year: 2019 <br> Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton</p> <p><strong>CONTACT</strong></p> <p><strong>Andrew Gillreath-Brown, PhD Candidate, RPA</strong><br> <a href="https://anthro.wsu.edu/">Department of Anthropology</a>, Washington State University<br> <a href="mailto:andrew.brown1234@gmail.com">andrew.brown1234@gmail.com</a> – Email<br> <a href="https://andrewgillreathbrown.wordpress.com/">andrewgillreathbrown.wordpress.com</a> – Web</p>
Data from: A new approach to map landscape variation in forest restoration success in tropical and temperate forest biomes
1. A high level of variation of biodiversity recovery within a landscape during forest restoration presents obstacles to ensure large scale, cost-effective, and long-lasting ecological restoration. There is an urgent need to predict landscape variation in forest restoration success at a global scale. 2. We conducted a meta-analysis comprising 135 study landscapes to predict and map landscape variation in forest restoration success in tropical and temperate forest biomes. Our analysis was based on the amount of forest cover within a landscape – a key driver of forest restoration success. We contrasted 17 generalized linear models measuring forest cover at different landscape sizes (with buffers varying from 5 to 200 km radii). We identified the most plausible model to predict and map landscape variation in forest restoration success. We then weighted landscape variation by the amount of potentially restorable areas (agriculture and pasture land areas) within the same landscape. Finally, we estimated restoration costs of implementing Bonn Challenge commitments in three specific temperate and tropical forest biome types in USA, Brazil and Uganda. 3. Landscape variation decreased exponentially as the amount of forest cover increased in the landscape, with stronger effects within a 5 km radius. Thirty-eight percent of forest biomes have landscapes with more than 27% of forest cover and showed levels of landscape variation below 10%. Landscapes with less than 6% of forest cover showed levels of variation in forest restoration success above 50%. 4. At the biome level, Tropical and Subtropical Moist Broadleaf Forests had the lowest (12.6%), while Tropical and Subtropical Dry Broadleaf Forests had the highest (22.9%) average of weighted landscape variation in forest restoration success. Our approach can lead to a reduction in implementation costs for each Bonn Challenge commitment between US$ 973 Mi and 9.9 Bi. 5. Policy implications. Our approach identifies landscape characteristics that increase the likelihood of biodiversity recovery during forest restoration – and potentially the chances of natural regeneration and long-term ecological sustainability and functionality. Identifying areas with low levels of landscape variation can help to reduce the risks and financial costs associated with implementing ambitious restoration commitments.
Рис. 1. Карта района иссΛеΑований. 1 – граница зон раститеΛьности; 2 – граница поΑзон раститеΛьности; 3 – места сбора материаΛа; I – южная тайга; II – среΑняя тайга; III – северная тайга; IV – крайнесеверная тайга; V – ΛесотунΑра; VI – южная тунΑра; VII – северная тунΑра. Fig. 1. Map of the studied area. 1 – boundary of vegetation zones; 2 – boundary of vegetation subzones; 3 – collection points; I – southern taiga; II – middle taiga; III – northern taiga; IV – extremely northern taiga; V – forest tundra; VI – southern tundra; VII – northern tundra. in Fauna and landscape-zonal distribution of Orthoptera in the Komi Republic (Russia)
Рис. 1. Карта района иссΛеΑований. 1 – граница зон раститеΛьности; 2 – граница поΑзон раститеΛьности; 3 – места сбора материаΛа; I – южная тайга; II – среΑняя тайга; III – северная тайга; IV – крайнесеверная тайга; V – ΛесотунΑра; VI – южная тунΑра; VII – северная тунΑра. Fig. 1. Map of the studied area. 1 – boundary of vegetation zones; 2 – boundary of vegetation subzones; 3 – collection points; I – southern taiga; II – middle taiga; III – northern taiga; IV – extremely northern taiga; V – forest tundra; VI – southern tundra; VII – northern tundra.
Organizing the fragmented landscape of multidisciplinary product development: A mapping of approaches, processes, methods and tools from the scientific literature - Searchable cartographies
<p>This document gathers cartographies for the development of mechatronic products, cyber-physical systems and smart products. The three cartographies presented are associated with an open-access article – see the citation box below – and differ from the ones provided in the article in that they are searchable, which makes it easier to pinpoint references, concepts and techniques. This document comprises a legend, the cartographies and a list of associated references. </p> <p>To contextualize the cartographies, the integration of digital and connectivity technologies in new products can invite companies to adapt their development. Organizing the fragmented landscape of multidisciplinary product development to help companies navigate the dense scientific literature corpus is a first step in supporting them in doing so. Multidisciplinary product development can be investigated by analyzing specific types of products that deal with both software and hardware development and can be referred to as cyber-physical systems, mechatronics, and smart products and systems in the literature. To support their development, 236 “concepts and techniques” (an expression that encompasses approaches, processes, methods and tools) were identified from 167 scientific papers through an extensive literature review and organized based on a four-level model paired with a decision tree. The mapping of the sorted concepts and techniques made it possible to generate graphical representations called “cartographies.” These cartographies represent a database of concepts and techniques for multidisciplinary product development and serve to support companies in their transformation from the product development perspective by providing them with a general overview of the related literature.</p>
Data accompanying manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'
<p>This upload contains the data which accompanies the manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'.</p> <p><strong>Metrics calculated for each of the 4269 50 km by 50 km regions</strong></p> <table> <tbody> <tr> <td>Filename (IFPA)</td> <td>Filename (Bedmachine)</td> <td>Filename (Bedmap3)</td> <td>Description</td> </tr> <tr> <td>x_ifpa.nc<br>y_ifpa.nc</td> <td> </td> <td> </td> <td>X and Y coordinates</td> </tr> <tr> <td>mean_ifpa.nc or ifpa_mean.nc</td> <td>bedmach_mean.nc</td> <td> </td> <td>Mean elevation (m)</td> </tr> <tr> <td> <p>ifpa_count.nc<br>ifpa_count_max_20.nc<br>ifpa_count_max_100.nc<br>ifpa_count_max_250.nc</p> </td> <td>bedmach_count.nc<br>bedmach_count_max_20.nc<br>bedmach_count_max_100.nc<br>bedmach_count_max_250.nc</td> <td> </td> <td> <p>The number of hills with a 50 m prominence within a 5 km neighbourhood <br>(or 20 m, 100 m, 250 m respectively)</p> </td> </tr> <tr> <td>ifpa_b1_5km.nc<br>ifpa_b1_thickness.nc</td> <td>bedmach_b1_5km.nc<br>bedmach_b1_thickness.nc</td> <td> </td> <td>The fourier fractal dimension for wavelengths greater than 5 km or the ice thickness respectively</td> </tr> <tr> <td>ifpa_std_deslope.nc<br>i_std_l.nc</td> <td>bedmach_std_deslope.nc<br>b_std_l.nc</td> <td> </td> <td>The standard deviation:<br>- with the best fit slope removed<br>- of some long wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_wav_max_power.nc</td> <td>bedmach_wav_max_power.nc</td> <td> </td> <td>The wavelength in the Fourier spectrum with the maximum power</td> </tr> <tr> <td>ifpa_rms_slope.nc<br>i_rms_slope_h.nc</td> <td>bedmach_rms_slope.nc<br>b_rms_slope_h.nc</td> <td> </td> <td>The RMS slope of:<br>- the bed elevation<br>- some short wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_rms_curvature.nc</td> <td>bedmach_rms_curvature.nc</td> <td> </td> <td>The RMS curvature of the bed elevation</td> </tr> <tr> <td> </td> <td>source.nc</td> <td> </td> <td>The method used to calculate the bed topography (Bedmachine only)</td> </tr> <tr> <td> </td> <td> </td> <td>mean_nearest.nc</td> <td>The mean distance from each IFPA grid point to the nearest Bedmap3 data point</td> </tr> <tr> <td> </td> <td> </td> <td>bedmap3_count.nc</td> <td>The number of Bedmap3 data points within the region</td> </tr> </tbody> </table> <p> </p> <p><strong>Datasets required for plotting</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>Groundingline_Antarctica_v2.shp</td> <td>Antarctic grounding line </td> </tr> <tr> <td>ECR_features.shp</td> <td>Outline of significant features within the example regions chosen</td> </tr> <tr> <td>IFPA_bed.nc</td> <td>OLD VERSION of IFPA bed topography map for Antarctica</td> </tr> <tr> <td> <p>IFPA_bed_C50.nc</p> </td> <td>IFPA bed topography map for Antarctica (without radar correction)</td> </tr> </tbody> </table> <p><strong>To plot the figures, you will either require the following datasets: </strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>IFPA_figures_data.zip</td> <td>Additionally data to plot figures 1,6,8 and 9 </td> </tr> <tr> <td> <p>HA_data.csv<br>HB_data.csv<br>RSB_data.csv</p> </td> <td>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected), IFPA (not radar corrected), Bedmachine v3, and ice-penetrating radar profiles</td> </tr> <tr> <td> <p>HA_data_ifpa.csv<br>HB_data_ifpa.csv<br>RSB_data_ifpa.csv</p> </td> <td> <p>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected) map for the region crossed by the ice-penetrating radar profile</p> </td> </tr> </tbody> </table> <p><strong>or, the figures can be regenerated using the following datasets, which are available at the listed DOIs</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> <td>Reference</td> <td>DOI</td> </tr> <tr> <td>GaplessREMA100.nc</td> <td>Gapless REMA Antarctica dataset at 100m resolution</td> <td>Dong et al. (2022)</td> <td>10.1016/j.isprsjprs.2022.01.024</td> </tr> <tr> <td>BedMachineAntarctica-v3.nc</td> <td>MEaSURES BedMachine Antarctica bed topography map version 3</td> <td>Morlighem et al. (2020)</td> <td>10.5067/FPSU0V1MWUB6</td> </tr> <tr> <td>antarctica_ice_velocity_450m_v2.nc</td> <td>ITSLIVE Antarctic velocity map</td> <td>Gardner et al. (2019)</td> <td>10.5067/6II6VW8LLWJ7</td> </tr> <tr> <td>antarctic_ice_vel_phase.nc</td> <td>MEaSURES Antarctic velocity map</td> <td>Mouginot et al. (2019)</td> <td>10.5067/PZ3NJ5RXRH10</td> </tr> <tr> <td>UTIG_2010_ICECAP_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the UTIG Icecap survey</td> <td>Wright et al. (2012)</td> <td>10.1029/2011JF002066</td> </tr> <tr> <td>BAS_2012_ICEGRAV_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the BAS Icegrav survey</td> <td>Forsberg et al. (2018)</td> <td>10.1144/SP461.17</td> </tr> </tbody> </table> <p><strong> </strong></p>
Mapping the DeFi Crime Landscape: An Evidence-based Picture
<p><br><strong>README - Crime Events Dataset</strong></p> <p>This document provides a detailed overview of the structure of the dataset for the paper: "Mapping the DeFi crime landscape: An Evidence-based Picture", published in the Journal of Cybersecurity, and available here: https://academic.oup.com/cybersecurity/article/11/1/tyae029/7962044</p> <p>The following fields are included, each representing different aspects of the events collected.</p> <p><strong>Data Fields</strong></p> <p><strong>1. unique_key</strong><br> <em>Description: </em>A unique number assigned to identify each event in the dataset.</p> <p><strong>2. Agregators </strong><br> <em>Description: </em>The sources where the event is listed. Aggregators include:<br> - De.Fi REKT<br> - SlowMist<br> - CryptoSec (rebranded to ChainSec as of February 2023)</p> <p><strong>3. DeFi actor involved </strong><br> <em>Description: </em>The name of the DeFi actor involved in the event (target, perpetrator, or intermediary).<br> Sources: <br> - On De.Fi REKT: Found as the "Title" of the event’s listing.<br> - On SlowMist: Found under the “Hacked target” title.<br> - On CryptoSec: Found in the "Title" of the event’s listing with the date.</p> <p><strong>4. REKT URL </strong><br> <em> Description: </em>The URL to the event's listing on De.Fi REKT.<br> <em>Process: </em>Found by searching for the DeFi actor involved in the REKT Database: https://de.fi/rekt-database</p> <p><strong>5. SlowMist URL </strong><br> <em>Description: </em>The URL to the event's listing on SlowMist.<br> <em>Process: </em>Available via https://hacked.slowmist.io/search/. Note that searching the actor's name will lead to the event but without an individualized URL.</p> <p><strong>6. CryptoSec URL </strong><br> <em>Description: </em>The URL to the event's listing on CryptoSec.<br> <em>Process: </em>Found at https://chainsec.io/defi-hacks/. Events are listed on a single page; use traditional keyboard search to locate specific events.</p> <p><strong>7. Aggregator Summary </strong><br> <em> Description</em>: A summary of the event provided by the aggregator.<br> <em>Sources: </em><br> - On De.Fi REKT: Found under "Quick Summary" and "Details of the Exploit".<br> - On SlowMist: Under "Description of the event".<br> - On CryptoSec: Below the title in quotation marks.</p> <p><strong>8. Aggregator sources URL </strong><br> <em>Description: </em>The URLs of references linked by the aggregator in the event’s listing.<br> <em>Sources:</em><br> - On De.Fi REKT: Found at the bottom by clicking "Source" or "Archived link".<br> - On SlowMist: Found by clicking "View Reference Sources".<br> - On CryptoSec: Available by clicking the source’s name at the end of the summary.</p> <p><strong>9. Event date </strong><br> <em>Description: </em>The date the event occurred.<br> <em>Sources:</em><br> - On De.Fi REKT: Listed under the "Date" field.<br> - On SlowMist: At the top right of the listing.<br> - On CryptoSec: Listed in parentheses behind the actor’s name.</p> <p><strong>10. Event year </strong><br> <em> Description: </em>The year the event occurred, extracted from the Event date.</p> <p><strong>11. Stolen amount USD </strong><br> <em>Description:</em> The total amount stolen, converted to USD.<br> <em>Sources:</em><br> - On De.Fi REKT: Found under "Funds lost".<br> - On SlowMist: Under the title “Amount of loss”.<br> - On CryptoSec: Behind the title "Amount stolen".<br> <em>Note: </em>If needed, conversions were manually performed using CoinMarketCap’s historical data as explained in the paper. </p> <p><strong>12. Implication of actor </strong><br> <em>Description: </em>Indicates whether the DeFi actor was a target, perpetrator, or intermediary in the event. Manually coded after reviewing the aggregator’s summary and linked sources.</p> <p><strong>13. Strategy </strong><br> <em>Description: </em>The main approach used to steal funds. Six categories are possible: Technical vulnerability, Human risks, Undetermined, Malicious use of contract, Misappropriation of funds, and Imitation. This was manually coded from the event summary and sources.</p> <p><strong>14. General tactic </strong><br> <em> Description: </em>The common techniques or methods used by malicious actors. Eleven categories are possible, defined in the appendix. Manually coded after reviewing the summary and linked sources.</p> <p><strong>15. Specific tactic </strong><br> <em> Description:</em> The precise technique used to commit the crime. Thirty-seven categories are possible, defined in the appendix. This was manually coded based on the event summary and sources.</p> <p><strong>16. Paper category </strong><br> <em>Description: </em>The main area of operation of the involved DeFi actor. Twelve categories are possible: Blockchain, Bridge, DApp, Derivatives, Exchange, Fungible Token (FT), Non-Fungible Token (NFT), Oracle, Yield, Staking, and Others. This was determined by the event summary and research on the actor.</p> <p><strong>17. Stack category </strong><br> <em>Description: </em>The technical layer of the DeFi Stack Reference (DSR) model corresponding to the paper category. Five categories are possible: DeFi Compositions (CP), DeFi Protocols (P), Cryptoassets (CA), Distributed Ledger Technology (DLT), and Interfaces (INT).</p> <p>---</p> <p>For more detailed information on the tactics, strategies, or categories used, please refer to the appendix of the dataset or the associated documentation.</p> <p>The previous version included duplicates. They have been removed in the second version.</p>
Рис. 1. Карта района иссΛеΑований. УсΛовные обозначения: красными кружками показаны базовые Λагеря; фиоΛетовой штриховкой — территория ΛанΑшафтного памятника прироΑы местного значения «ВΛасьевские торфяники»; синей штриховкой — акватория памятника прироΑы краевого значения «ЗаΛив Счастья с островами Кевор и Чаечный» Fig. 1. Map of the study area. Legend: red circles show base camps; purple shading — the territory of the landscape natural monument of local importance "Vlasyevsky Torfyaniky"; blue shading — the water area is a natural monument of regional significance "The Bay of Schastꞌе with the islands of Kevor and Chaechny" in New data on rare and insufficiently studied birds of the Shchastya Bay, the Sea of Okhotsk, and adjacent territories (Khabarovsk Krai)
Рис. 1. Карта района иссΛеΑований. УсΛовные обозначения: красными кружками показаны базовые Λагеря; фиоΛетовой штриховкой — территория ΛанΑшафтного памятника прироΑы местного значения «ВΛасьевские торфяники»; синей штриховкой — акватория памятника прироΑы краевого значения «ЗаΛив Счастья с островами Кевор и Чаечный» Fig. 1. Map of the study area. Legend: red circles show base camps; purple shading — the territory of the landscape natural monument of local importance "Vlasyevsky Torfyaniky"; blue shading — the water area is a natural monument of regional significance "The Bay of Schastꞌе with the islands of Kevor and Chaechny"
Constructing a high-density linkage map to infer the genomic landscape of recombination rate variation in European Aspen (Populus tremula)
<p>Data sets and files for linkage map construction and for inferring recombination rate variation in <em>Populus tremula</em>. Associated scripts for analyses can be found at <a href="https://github.com/parkingvarsson/Recombination_rate_variation">https://github.com/parkingvarsson/Recombination_rate_variation</a> </p>
Mapping the landscape of climate services
<p>Climate services are technology-intensive, science-based and user-tailored tools providing timely climate information to a wide set of users. They accelerate innovation, while contributing to societal adaptation. Research has explored the advancements of climate services in multiple fields, producing a wealth of interdisciplinary knowledge ranging from climatology to the social sciences. The aim of this paper is to map the global landscape of research on climate services and to identify patterns at individual, affiliation and country level and the structural properties of each community. We use a sample of 358 records published between 1974 and 2018 and quantitatively analyze them. We provide insights into the main characteristics of the community of climate services through Bibliometrics and complement these findings with Network Science. We have computed the centrality of each actor as derived from a Principal Component Analysis of 42 different measures. By exploring the structural properties of the networks of individuals, institutions and countries we derive implications on the most central agents. Furthermore, we detect brokers in the network, capable of facilitating the information flow and increasing the cohesion of the community. We finally analyze the abstracts of the sample via Content Analysis. We find a progressive shift towards climate adaptation and user-centric visions. Agriculture and Energy are the top mentioned sectors. Anglophone countries and institutions are quantitatively dominant, and they are also important in connecting different discipline of the network of scholars, by building on established partnerships. Finding that nodes facilitating the diffusion of information flows (<em>the brokers</em>) are not necessarily the most central, but have a high degree of interdisciplinarity facilitating interactions of different communities. <em>Social media abstract</em>. #WhoisWho in #climateservices? A comprehensive map of research in #Europe and beyond</p>
Mapping the Swiss Landscape of Diamond Open Access Journals. The PLATO Study on Scholar-Led Publishing. Dataset
<p><strong>Context</strong><br> From March to September 2022, the <a href="https://www.openscience.uzh.ch/en/openaccess/plato.html">«Platinum Open Access Funding» Project (PLATO)</a>, in collaboration with the Institute for Applied Data Science & Finance at the Bern University of Applied Sciences, undertook a bibliometric and empirical study of the Platinum/Diamond open access journal landscape in Switzerland. The PLATO project is an initiative of six Swiss universities – the University of Zurich, the University of Bern, the University of Geneva, the University of Neuchâtel, the Zurich University of the Arts and ETH Zurich –, dedicated to furthering community-led scholarly publishing in Switzerland. Diamond open access stands for a concept of equitable open access to and participation in scholarly publishing that is free for both authors and readers.</p> <p><strong>Presentation</strong><br> The main objective of the PLATO Study was to gain insight into the Platinum/Diamond open access publishing ecosystem in Switzerland through a mixed-method approach. The study consisted of three parts: First, bibliometric data were combined with inputs from Swiss open access publishers, institutional open access experts as well as information on journal websites to identify Swiss Diamond OA journals and their main characteristics. Second, seven semi-structured interviews with editors of select Diamond OA journals were conducted to generate a thorough understanding of their workflows, infrastructures, business models, challenges and opportunities. Third, based on the inputs from the interviews, three surveys were designed and sent to authors/reviewers, editors, and representatives of hosting and funding institutions of Swiss Diamond OA journals.</p> <p>The results of the study are published in the form of the following outputs:</p> <ul> <li>DOI Report: 10.5281/zenodo.7461728</li> <li>DOI Bibliometric List: <a href="https://zenodo.org/record/6992615#.YzK3ElJBw-Q">10.34914/olos:l2tys6tie5f63h35lqpqzzlx24</a></li> <li>DOI Data Set: 10.5281/zenodo.7461754</li> </ul> <p>The Data Set comprises the following files:</p> <ul> <li>Survey questionnaires</li> </ul> <p> SurveyQuestionnaire_Author.pdf<br> SurveyQuestionnaire_Editor.pdf<br> SurveyQuestionnaire_Publisher.pdf</p> <ul> <li>Data collected in three survey studies addressing journal editors, authors/reviewers as well as representatives of hosting and funding institutions:</li> </ul> <p> StudyData_Author.csv<br> StudyData_Editor.csv<br> StudyData_Publisher.csv</p> <ul> <li>Codebooks explaining the coding of the survey data files:</li> </ul> <p> StudyCodebook_Author.pdf<br> StudyCodebook_Editor.pdf<br> StudyCodebook_Publisher.pdf</p>
Data from: Latent generative landscapes as maps of functional diversity in protein sequence space
<p>Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.</p>
Data from: Latent generative landscapes as maps of functional diversity in protein sequence space
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A new approach to map landscape variation in forest restoration success in tropical and temperate forest biomes
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Spatial mapping of the hepatocellular carcinoma landscape identifies unique intratumoural perivascular-immune neighbourhoods
<p>The uploaded data includes results from imaging mass cytometry (IMC) data collected from hepatocellular carcinoma patients. The associated publication can be found <a href="https://journals.lww.com/hepcomm/fulltext/2024/11010/spatial_mapping_of_the_hcc_landscape_identifies.11.aspx">here</a> (Marsh-Wakefield <em>et al.</em>, 2024, <em>Hepatology Communications</em>).</p> <p>The CSV file contains segmented cells from IMC data. This includes the Patient, ROI, and Group each cell is assigned. Marker signal intensities underwent arcsine transformation and were rescaled. The “simprof_cluster” column contains the final iteration of clustering following initial X-shift clustering.</p> <p>Notes on additional columns:</p> <ul> <li>“Sample” is barcoded such that the first three digits are the ablation number, followed by the region on the TMA, the group, and the patient. I.e., “[ablation.number]_[TMA.location]_[group]_[patient]”.</li> <li>“x” and “y” refer to the coordinates of samples.</li> <li>“Group” refers to the tissue type. Included non-tumour (NT), invasive margin (IM), and tumour (T) regions.</li> <li>The area for each ROI has been calculated (µm^2 and mm^2).</li> <li>“Batch” refers to TMA.</li> <li>In most cases each area from each patient has three ablation sites. Three samples have an extra ablation site due to technical difficulties during the ablation, and hence have a “split” sample.</li> </ul> <p>The PDF file contains patient information associated with the IMC data.</p> <p>DOI of dataset:</p> <p>10.5281/zenodo.10622397</p> <p>Any further questions can be addressed to Felix Marsh-Wakefield felix.marsh-wakefield@sydney.edu.au</p>
Data from: Participatory mapping reveals biocultural and nature values in the shared landscape of a Nordic UNESCO Biosphere Reserve
<p>1. Making the right decisions for sustainable development requires sound knowledge of the values and spatial distribution of the services co-produced by ecosystems and people. UNESCO's Man and the Biosphere programme and associated Biosphere Reserves (BRs) are key learning sites or model regions for sustainable development providing key entry points for transdisciplinary work on sustainable development. However, there is limited research exploring spatial distribution of socio-cultural Ecosystem Service (ES) values in BRs and how those values vary according to the BR zonation.</p> <p>2. We used a transdisciplinary approach to design and implement a public participation geographic information systems (PPGIS) survey in a recently designated BR to (i) asses the spatial distribution of ES values in the different zones, (ii) identify hotspots of ES values, (iii) identify spatial bundles of ES values, and (iv) assess the social-ecological characteristics that determine the distribution of those values.</p> <p>3. We found that stakeholders identify high biocultural ES values, mapping predominantly places for outdoor recreation, biodiversity, agricultural products, and cultural heritage. Buffer zones had high agricultural and cultural heritage values while extractive values were largely absent from cores zones. We identified five spatial ES-value bundles highlighting distinct places important for ES values related to: 'multifunctional landscapes' located close to settlements, 'cultural landscapes' associated with agricultural land, 'wild animal resources' along the coastlines, 'outdoor recreation and biodiversity' and 'passive cultural values' widely distributed in high and moderately populated areas.</p> <p>4. Accessibility to nature was highly important for ES values and people highly value nature close to where they live. We show the importance of biocultural values in the region, and agricultural landscapes were highly valued for multiple ES values beyond agricultural products alone.</p> <p>5. We show that BRs have become places that link cultural heritage, agricultural, and biodiversity values in multifunctional landscapes. We put our findings into the local context and suggest how they can inform land-use planning and management through policies aimed at maintaining key agricultural landscapes that provide social-ecological resilience. Additionally, we discuss the value of our study for the wider BR network and how similar work can contribute to monitoring of BR implementation.</p>
MOH-AMM-SC-2024 ePoster ID 44: Mapping The Landscape of Precision Public Health: A Bibliometric Analysis of Influential Works and Key Contributors
<p>Supplementary Material for Poster Presentation</p> <p>MOH-AMM-SC-2024 ePoster ID 44: Mapping The Landscape of Precision Public Health: A Bibliometric Analysis of Influential Works and Key Contributors</p> <p>Conference: 15th MOH-AMM Scientific Meeting 2024 in conjunction with 25th NIH Scientific Conference</p>
High-resolution mapping of the period landscape reveals polymorphism in cell cycle frequency tuning
<p>Biological oscillators adapt to environmental changes with widely tunable frequencies, a property theoretical studies attributed to positive feedbacks. However, no experiments have tested this theory. Here, we created synthetic cells to independently tune the frequency and feedback strength of a cell-cycle oscillator, enabling continuous mapping of period landscape in response to network perturbations. We found that although inhibiting positive feedback of cyclin-dependent kinase (Cdk1) reduces the tunability, the reduction is not as significant as theoretically predicted, and the Cdk1-counteracting phosphatase, PP2A, provides additional machinery to ensure frequency regulation. Additionally, cells exhibit polymorphic responses to PP2A inhibition, showing a monomodal distribution of oscillatory cells at low or high PP2A inhibition or a bimodal distribution at both low and high inhibitions. We explained the polymorphism by a model of two interlinked bistable switches of Cdk1 and PP2A where cell-cycle oscillations exhibit two modes in the presence or absence of PP2A bistability.</p>
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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.