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31 results for “SDM”
Baseline data for SDM of the SIM4NEXUS case of the Netherlands
<p>This dataset consists of the baseline data for the SDM of the SIM4NEXUS case of the Netherlands. Most data is based on year data that has been equally distributed over 12 months per year. For optional extension to monthly data, the data display already monthly data starting at December 2009 until December 2050 i.e. 493 points in time. The data include time series for socio-economic indicators, land use, food production, energy use, climate and water. The socio-economic system includes data on population and gross domestic product (GDP) per capita. The land system includes data on four main land uses, namely built-up areas, agriculture (with areas for food, energy crops and fodder crops production) , nature areas (non-forest, forest not for biomass production, forest for biomass production) , and areas for renewable energy production like wind mills and solar power fields. The food system includes plant-based (food crops like cereals and vegetables and fruit) and animal-based food production (based on the herds of cattle, pigs and poultry) in terms of protein. In addition, the animal- and plant-based protein requirements of Dutch consumers are also estimated. The energy system has an energy production and an energy demand part. Energy demand is determined for the domestic sector (i.e. households) based upon population and households’ demands for renewable and non-renewable energy. For the other economic sectors (agriculture, manufacturing industry, transportation and services sector) the demands for renewable and non-renewable energy are determined by GDP per sector and the energy intensity of the sectors. Energy supply is divided into non-renewable energy production and renewable energy production. Non-renewable energy sources include energy from coal, natural gas, oil and nuclear. The renewable energy consist of energy from wind (onshore and offshore), solar (on buildings and solar power fields), biomass and other sources (innovations like hydrogen or geothermic power). The energy of biomass there are 7 sources of biomass: energy crops, crop residues, manure, organic household waste, organic waste from public areas, waste water and timber residues. Timber residues are largely imported for large-scale use of co-firing in coal power plants and bio-based activities in the manufacturing industries. The water system has two parts: water quality which are the agricultural emissions of nitrogen and phosphorus to water, and water quantity i.e. agricultural water demand for irrigation and livestock drinking water. Finally, the climate system is divided into non-agricultural GHG emissions, and agricultural emissions. The non-agricultural GHG emissions are based on the GHG emissions from non-renewable energy production and non-energy related GHG emissions per economic sector except for agriculture. The agricultural GHG emissions relate to GHG emissions from livestock production, crop production and wetlands.</p>
Data from: Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management
<p><span>1. Species' ranges are changing at accelerating rates. Species distribution models (SDMs) are powerful tools that help rangers and decision-makers prepare for reintroductions, range shifts, reductions, and/or expansions by predicting habitat suitability across landscapes. Yet, range-expanding or -shifting species in particular face other challenges that traditional SDM procedures cannot quantify, due to large differences between a species' currently-occupied range and potential future range. The realism of SDMs is thus lost and not as useful for conservation management in practice. Here, we address these challenges with an extended assessment of habitat suitability through an <i>integrated SDM database (iSDMdb)</i>.</span></p> <p><span>2. The<i> iSDMdb</i> is a spatial database of predicted sites in a species' prediction range, derived from SDM results, and is a single spatial feature that contains additional, user-friendly data fields that synthesise and summarise SDM predictions and uncertainty, human impacts, restoration features, novel preferences in novel spaces, and management priorities. To illustrate its utility<i>,</i> we used the endangered New Zealand sea lion (<i>Phocarctos hookeri</i>). We consulted with wildlife rangers, decision-makers, and sea lion experts to supplement SDM predictions with additional, more realistic, and applicable information for management. </span></p> <p><span>3. Almost half the data fields included in this database resulted from engaging with these end-users during our study. The SDM found 395 predicted sites. However, the <i>iSDMdb</i>'s additional assessments showed that the actual suitability of most sites (90%) was questionable due to human impacts. >50% of sites contained unnatural barriers (fences, grazing grasslands), and 75% of sites had roads located within the species' range of inland movement. Just 5% of the predicted sites were mostly (>80%) protected.</span></p> <p><span>4. Integrating SDM results with supplemental assessments provides a way to address SDM limitations, especially for range-expanding or -shifting species. SDM products for conservation applications have been critiqued for lacking transparency and interpretation support, and ineffectively communicating uncertainty. The <i>iSDMdb</i> addresses these issues and enhances the practical relevance and utility of SDMs for stakeholders, rangers, and decision-makers. We exemplify how to build an <i>iSDMdb</i> using open-source tools, and how to make diverse, complex assessments more accessible for end-users.</span></p>
SDM env predictor comparison dataset
<p class="MsoNormal">Identifying the environmental drivers of the global distribution of succulent plants using the crassulacean acid metabolism pathway of photosynthesis has previously been investigated through ensemble-modelling of species delimiting the realised niche of the natural succulent biome. An alternative approach, which may provide further insight into the fundamental niche of succulent plants in the absence of dispersal limitation, is to model the distribution of selected species that are globally widespread and have become naturalised far beyond their native habitats.<span> </span>This could be of interest, for example, in defining areas that may be suitable for cultivation of alternative crops resilient to future climate change. We therefore explored the performance of climate-only species distribution models in predicting the drivers and distribution of two widespread CAM plants, <em>Opuntia ficus-indica </em>and <em>Euphorbia tirucalli</em>. <span> </span>Using two different algorithms and five predictor sets, we created distribution models for these examplar species and produced an updated map of global inter-annual rainfall predictability. <span>No single predictor set produced markedly more accurate models, with the basic bioclim-only predictor set marginally out-performing combinations with additional predictors. Minimum temperature of the coldest month was the single most important variable in determining spatial distribution, but additional predictors such as precipitation and inter-annual precipitation variability were also important in explaining the differences in spatial predictions between SDMs. When compared against previous projections, an </span><em><span>a posteriori</span></em><span> approach correctly does not predict distributions in areas of ecophysiological tolerance yet known absence (e.g. due to biotic competition). </span>An updated map of inter-annual rainfall predictability has successfully identified regions known to be depauperate in succulent plants. <span>High model performance metrics suggest that the majority of potentially suitable regions for these species are predicted by these models with a limited number of climate predictors, and there is no benefit in expanding model complexity and increasing the potential for overfitting.</span></p>
Data from: Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management
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SDM env predictor comparison dataset
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HKI, Buku Konsep Urgensi Disiplin Untuk produktifitas SDM, 2021
<p>Hak atas Kekayaan Intelektual (HaKI) : Pengertian dan Jenisnya</p> <p>Posted on <a href="https://lp2m.uma.ac.id/2021/11/25/hak-atas-kekayaan-intelektual-haki-pengertian-dan-jenisnya/">November 25, 2021</a> by <a href="https://lp2m.uma.ac.id/author/admin/">adminlp2m</a></p> <p><a href="https://lp2m.uma.ac.id/2021/11/25/hak-atas-kekayaan-intelektual-haki-pengertian-dan-jenisnya/#respond">0</a></p> <p>Sudahkah anda familiar dengan Hak atas Kekayaan Intelektual atau HaKI? HaKI mempunyai fungsi utama untuk memajukan kreatifitas dan inovasi yang bermanfaat bagi masyarakat secara luas. Tapi sejauh mana anda mengetahui Hak Kekayaan Intelektual? Yuk bahas bersama-sama.</p> <p>Istilah HaKI atau Hak atas Kekayaan Intelektual merupakan terjemahan dari Intellectual Property Right (IPR), sebagaimana diatur dalam undang-undang No. 7 Tahun 1994 tentang pengesahan WTO (Agreement Establishing The World Trade Organization). Pengertian Intellectual Property Right sendiri adalah pemahaman mengenai hak atas kekayaan yang timbul dari kemampuan intelektual manusia, yang mempunyai hubungan dengan hak seseorang secara pribadi yaitu <a href="http://uma.ac.id/">hak asasi manusia (human right).</a></p> <p>HaKI atau Hak atas Kekayaan Intelektual adalah hak eksklusif yang diberikan suatu hukum atau peraturan kepada seseorang atau sekelompok orang atas karya ciptanya. Pada intinya HaKI adalah hak untuk menikmati secara ekonomis hasil dari <a href="http://lp2m.uma.ac.id/">suatu kreativitas intelektual</a>. Objek yang diatur dalam HaKI adalah karya-karya yang timbul atau lahir karena kemampuan intelektual manusia.</p> <p>Setiap hak yang digolongkan ke dalam HaKI harus mendapat kekuatan hukum atas karya atau ciptannya. Untuk itu diperlukan tujuan penerapan HaKI. Tujuan dari penerapan HaKI yang Pertama, antisipasi kemungkinan melanggar HaKI milik pihak lain, Kedua meningkatkan daya kompetisi dan pangsa pasar dalam komersialisasi kekayaan intelektual, Ketiga dapat dijadikan sebagai bahan pertimbangan dalam penentuan strategi penelitian, usaha dan industri di Indonesia.</p> <p>Pentingnya HAKI</p> <p>Lalu bagaimana apabila karya kita atau milik orang lain tidak dilindungi? Sudah pasti dipastikan akan terkena pembajakan. Sebegai contoh untuk di dunia pendidikan saat ini marak adanya pembajakan buku. Pembajakan buku ini makin marak terjadi di masyarakat, banyak faktor yang menyebabkan terjadinya pembajakan buku, salah satunya adalah kurangnya penegakan hukum, ketidaktahuan masyarakat terhadap perlindungan hak cipta buku, dan kondisi ekonomi masyarakat.</p> <p>Sudah banyak pelaku terjaring oleh aparat, dan masih banyak pula yang masih berkeliaran dan tumbuh, seiring tingginya permintaan oleh masyarakat. Untuk itu butuh kesadaran dari masyarakat untuk mengetahui HaKI agar karyanya tidak diambil oleh orang lain. Berikut ini terdapat macam-macam HaKI</p> <p>Macam-macam HaKI (Hak atas Kekayaan Intelektual)</p> <p>1. Hak Cipta</p> <p>Hak Cipta adalah hak khusus bagi pencipta untuk mengumumkan atau memperbanyak ciptaannya. Termasuk ciptaan yang dilindungi adalah ciptaan dalam bidang ilmu pengetahuan, sastra dan seni.</p> <p>Hak cipta diberikan terhadap ciptaan dalam ruang lingkup bidang ilmu pengetahuan, kesenian, dan kesusasteraan. Hak cipta hanya diberikan secara eksklusif kepada pencipta, yaitu “seorang atau beberapa orang secara bersama-sama yang atas inspirasinya lahir suatu ciptaan berdasarkan pikiran, imajinasi, kecekatan, keterampilan atau keahlian yang dituangkan dalam bentuk yang khas dan bersifat pribadi.</p> <p>2. Hak Kekayaan Industri yang Meliputi</p> <p>A. Paten</p> <p>Berdasarkan Undang-Undang Nomor 14 Tahun 2001 Pasal 1 Ayat 1, Paten adalah hak eksklusif yang diberikan oleh Negara kepada Inventor atas hasil invensinya di bidang teknologi, yang untuk selama waktu tertentu melaksanakan sendiri invensinya tersebut atau memberikan persetujuannya kepada pihak lain untuk melaksanakannya.</p> <p>Paten hanya diberikan negara kepada penemu yang telah menemukan suatu penemuan (baru) di bidang teknologi. Yang dimaksud dengan penemuan adalah kegiatan pemecahan masalah tertentu di bidang teknologi yang berupa : Proses, hasil produksi, penyempurnaan dan pengembangan proses, penyempurnaan dan pengembangan hasil produksi.</p> <p>B. Merek</p> <p>Berdasarkan Undang-Undang Nomor 15 Tahun 2001 Pasal 1 Ayat 1 Merek adalah tanda yang berupa gambar, nama, kata, huruf-huruf, angka-angka, susunan warna, atau kombinasi dari unsur-unsur tersebut yang memiliki daya pembeda dan digunakan dalam kegiatan perdagangan barang atau jasa.</p> <p>Jadi merek merupakan tanda yang digunakan untuk membedakan produk (barang dan atau jasa) tertentu dengan yang lainnya dalam rangka memperlancar perdagangan, menjaga kualitas, dan melindungi produsen dan konsumen.</p> <p>Terdapat beberapa istilah merek yang biasa digunakan, yang pertama merek dagang adalah merek yang digunakan pada barang yang diperdagangkan oleh seseorang atau beberapa orang secara bersama-sama atau badan hukum untuk membedakan dengan barang-barang sejenis lainnya.</p> <p>Merek jasa yaitu merek yang digunakan pada jasa yang diperdagangkan oleh seseorang atau beberapa orang secara bersama-sama atau badan hukum untuk membedakan dengan jasa-jasa sejenis lainnya.</p> <p>Merek kolektif adalah merek yang digunakan pada barang atau jasa dengan karakteristik yang sama yang diperdagangkan oleh beberapa orang atau badan hukum secara bersama-sama untuk membedakan dengan barang atau jasa sejenis lainnya.</p> <p>Hak atas merek adalah hak khusus yang diberikan negara kepada pemilik merek yang terdaftar dalam Daftar Umum Merek untuk jangka waktu tertentu, menggunakan sendiri merek tersebut atau memberi izin kepada seseorang atau beberapa orang secara bersama-sama atau badan hukum untuk menggunakannya.</p> <p>C. Desain Industri</p> <p>Berdasarkan Undang-Undang Nomor 31 Tahun 2000 Pasal 1 Ayat 1 Tentang Desain Industri, bahwa desain industri adalah suatu kreasi tentang bentuk, konfigurasi, atau komposisi garis atau warna, atau garis dan warna, atau gabungan daripadanya yang berbentuk tiga dimensi atau dua dimensi yang memberikan kesan estetis dan dapat diwujudkan dalam pola tiga dimensi atau dua dimensi serta dapat dipakai untuk menghasilkan suatu produk, barang, komoditas industri, atau kerajinan tangan.</p> <p>D. Desain Tata Letak Sirkuit Terpadu</p> <p>Berdasarkan Undang-Undang Nomor 32 Tahun 2000 Pasal 1 Ayat 1 Tentang Desain Tata Letak Sirkuit Terpadu bahwa, Sirkuit Terpadu adalah suatu produk dalam bentuk jadi atau setengah jadi, yang di dalamnya terdapat berbagai elemen dan sekurang-kurangnya satu dari elemen tersebut adalah elemen aktif, yang sebagian atau seluruhnya saling berkaitan serta dibentuk secara terpadu di dalam sebuah bahan semikonduktor yang dimaksudkan untuk menghasilkan fungsi elektronik.</p> <p>E. Rahasia Dagang</p> <p>Menurut Undang-Undang Nomor 30 Tahun 2000 Tentang Rahasia Dagang bahwa, Rahasia Dagang adalah informasi yang tidak diketahui oleh umum di bidang teknologi dan/atau bisnis, mempunyai nilai ekonomi karena berguna dalam kegiatan usaha, dan dijaga kerahasiaannya oleh pemilik Rahasia Dagang.</p> <p>F. Indikasi Geografis</p> <p>Berdasarkan Undang-Undang No. 15 Tahun 2001 Pasal 56 Ayat 1 Tentang Merek bahwa, Indikasi-geografis dilindungi sebagai suatu tanda yang menunjukkan daerah asal suatu barang yang karena faktor lingkungan geografis termasuk faktor alam, faktor manusia, atau kombinasi dari kedua faktor tersebut, memberikan ciri dan kualitas tertentu pada barang yang dihasilkan.</p> <p><strong>Prinsip-Prinsip HaKI atau Hak atas Kekayaan Intelektual</strong></p> <p><strong>Prinsip-prinsip Hak atas Kekayaan Intelektual (HaKI) adalah sebagai berikut :</strong></p> <p><strong>1. Prinsip Ekonomi</strong><br> Dalam prinsip ekonomi, hak intelektual berasal dari kegiatan kreatif dari daya pikir manusia yang memiliki manfaat serta nilai ekonomi yang akan member keuntungan kepada pemilik hak cipta.</p> <p><strong>2. Prinsip Keadilan</strong><br> Prinsip keadilan merupakan suatu perlindungan hukum bagi pemilik suatu hasil dari kemampuan intelektual, sehingga memiliki kekuasaan dalam penggunaan hak atas kekayaan intelektual terhadap karyanya.</p> <p><strong>3. Prinsip Kebudayaan</strong><br> Prinsip kebudayaan merupakan pengembangan dari ilmu pengetahuan, sastra dan seni guna meningkatkan taraf kehidupan serta akan memberikan keuntungan bagi masyarakat, bangsa dan Negara.</p> <p><strong>4. Prinsip Sosial</strong><br> Prinsip sosial mengatur kepentingan manusia sebagai warga Negara, sehingga hak yang telah diberikan oleh hukum atas suatu karya merupakan satu kesatuan yang diberikan perlindungan berdasarkan keseimbangan antara kepentingan individu dan masyarakat/lingkungan.</p>
A compilation of environmental geographic rasters for SDM covering France
<p>This dataset is a compilation of geographic rasters from multiple environmental data sources. It aims at making the life of SDM users easier. All rasters cover the metropolitan French territory, but have varying resolutions and projections. Each directory inside the main directory "<strong>0_mydata</strong>" contain a single environmental raster. Punctual extraction of raster values can be easily done for large sets of WGS84-(longitude,latitude) points coordinates and for multiple rasters at the same time through the R function <strong>get_variables</strong> of script <a href="https://github.com/ChrisBotella/SamplingEffort/blob/master/_functions.R">_functions.R</a> from Github repository: <a href="https://github.com/ChrisBotella/SamplingEffort">https://github.com/ChrisBotella/SamplingEffort</a>. All data sources are accessible on the web and free of use, at least for scientific purpose. They have various conditions of citations. Anyone diffusing a work using the present data must reference along with the present DOI, the original source data employed. Those source data are described in the paragraphs below. We provide the articles to cite, when required, and webpages for access.</p> <p><strong>Pedologic Descriptors of the ESDB v2: 1 km × 1 km Raster Library :</strong> The library contains multiple soil pedology (physico-chemical properties of the soil) descriptors raster layers covering Eurasia at a resolution of 1 km. We selected 11 descriptors from the library. They come from the PTRDB. The PTRDB variables have been directly derived from the initial soil classification of the Soil Geographical Data Base of Europe (SGDBE) using expert rules. For more details, see [1, 2] and [3]. The data is maintained and distributed freely for scientific use by the European Soil Data Centre (ESDAC) at <a href="http://eusoils.jrc.ec.europa.eu/content/european-soil-databasev2-raster">http://eusoils.jrc.ec.europa.eu/content/european-soil-databasev2-raster</a>. The 11 rasters are in the directories <strong>"awc_top", "bs_top", "cec_top", "dimp", "crusting", "erodi", "dgh", "text", "vs", "oc_top", "pd_top"</strong>.</p> <p><strong>Corine Land Cover 2012, Version 18.5.1, 12/2016 :</strong> It is a raster layer describing soil occupation with 48 categories across Europe (25 countries) at a resolution of 100 m. This data base of the European Union is freely accessible online for all use at <a href="http://land.copernicus.eu/pan-european/corine-land-cover/clc-2012">http://land.copernicus.eu/pan-european/corine-land-cover/clc-2012</a>. The raster of this variable is in the directory "<strong>clc</strong>".</p> <p><strong>Hydrographic Descriptor of BD Carthage v3: </strong>BD Carthage is a spatial relational database holding many informations on the structure and nature of the french metropolitan hydrological network. For the purpose of plants ecological niche, we focus on the geometric segments representing watercourses, and polygons representing hydrographic fresh surfaces. The data has been produced by the Institut National de l’information Géographique et forestière (IGN) from an interpretation of the BD Ortho IGN. It is maintained by the SANDRE under free license for non-profit use and downloadable at:<br> <a href="http://services.sandre.eaufrance.fr/telechargement/geo/ETH/BDCarthage/FX">http://services.sandre.eaufrance.fr/telechargement/geo/ETH/BDCarthage/FX</a><br> From this shapefile, we derived a raster containing the binary value raster proxi_eau_fast, i.e. proximity to fresh water, all over France.We used qgis to rasterize to a 12.5m resolution, with a buffer of 50m, the shapefile COURS_D_EAU.shp on<br> one hand, and the polygons of SURFACES_HYDROGRAPHIQUES.shp with attribute NATURE=“Eau douce<br> permanente” on the other hand.We then created the maximum raster of the previous ones (So the value of 1 correspond to an approximate distance of less than 50m to a watercourse or hydrographic surface of fresh water). The raster is in the directory named "<strong>proxi_eau_fast</strong>".</p> <p><strong>USGS Digital Elevation Data :</strong> The Shuttle Radar Topography Mission achieved in 2010 by Endeavour shuttle measured elevation at three arc second resolution over most of the earth surface. Raw measures have been post-processed by NASA and NGA in order to correct detection anomalies. The data is available from the U.S. Geological Survey, and downloadable on the Earthexplorer (<a href="https://earthexplorer.usgs.gov/">https://earthexplorer.usgs.gov/</a>). One may refer to <a href="https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-void?qt-science_center_objects=0#qt-science_center_objects">https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-void?qt-science_center_objects=0#qt-science_center_objects</a> for more informations. the elevation raster is in the directory named "<strong>alti</strong>".</p> <p><strong>Potential Evapotranspiration of CGIAR-CSI ETP : </strong>The CGIAR-CSI distributes this worldwide monthly potential-evapotranspiration raster data. It is pulled from a model developed by Antonio Trabucco [4, 5]. Those are estimated by the Hargreaves formula, using mean monthly surface temperatures and standard deviation from WorldClim 1:4 (<a href="http://www.cgiar-csi.org/data/global-aridity-and-pet-database#description">http://www.worldclim. org/</a>), and radiation on top of atmosphere. The raster is at a 1km resolution, and is<br> freely downloadable for a nonprofit use at: <a href="http://www.cgiar-csi.org/data/global-aridity-and-pet-database#description">http://www.cgiar-csi.org/data/global-aridity-and-pet-database#description</a>. This raster is in the directory "<strong>etp</strong>".</p> <p><strong>Bioclimatic Descriptors of Chelsea Climate Data 1.1:</strong> Those are raster data with worldwide coverage and 1 km resolution. A mechanistical climatic model is used to make spatial predictions of monthly mean-max-min temperatures, mean precipitations and 19 bioclimatic variables, which are downscaled with statistical models integrating historical measures of meteorologic stations from 1979 to today. The exact method is explained in the reference papers [6] and [7]. The data is under Creative Commons Attribution 4.0 International License and downloadable at (<a href="http://chelsa-climate.org/downloads/">http://chelsa-climate.org/downloads/</a>). The 19 bioclimatic rasters are located in the directories named "<strong>chbio_X</strong>".</p> <p><strong>ROUTE500 1.1:</strong> This database register classified road linkages between cities (highways, national roads, and departmental roads) in France in shapefile format, representing approxi-mately 500,000 km of roads. It is produced under free license (all uses) by the IGN. Data are available online at <a href="http://osm13.openstreetmap.fr/~cquest/route500/">http://osm13.openstreetmap.fr/~cquest/route500/</a>. For deriving the variable “<strong>droute_fast</strong>”, the distance to the main roads networks, we computed with qGis the distance raster to the union of all elements of the shapefile ROUTES.shp (segments).</p> <p><strong>References : </strong></p> <p>[1] Panagos, P. (2006). The European soil database. GEO: connexion, 5(7), 32–33.</p> <p>[2] Panagos, P., Van Liedekerke, M., Jones, A., Montanarella, L. (2012). European Soil Data<br> Centre: Response to European policy support and public data requirements. Land Use Policy,<br> 29(2),329–338.</p> <p>[3] Van Liedekerke, M. Jones, A. & Panagos, P. (2006). ESDBv2 Raster Library-a set of rasters<br> derived from the European Soil Database distribution v2. 0. European Commission and the<br> European Soil Bureau Network, CDROM, EUR, 19945.</p> <p>[4] Zomer, R., Bossio, D., Trabucco, A., Yuanjie, L., Gupta, D. & Singh, V. (2007). Trees and<br> water: smallholder agroforestry on irrigated lands in Northern India.</p> <p>[5] Zomer, R., Trabucco, A., Bossio, D. & Verchot, L. (2008). Climate change mitigation: A<br> spatial analysis of global land suitability for clean development mechanism afforestation and<br> reforestation. Agriculture, ecosystems & environment, 126(1), 67–80.</p> <p>[6] Karger, D. N., Conrad, O., Bohner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W. & Kessler,<br> M. (2016). Climatologies at high resolution for the earth’s land surface areas. arXiv preprint<br> arXiv:1607.00217.</p> <p>[7] Karger, D. N., Conrad, O., Bohner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W. & Kessler, M.<br> (2016). CHELSEA climatologies at high resolution for the earth’s land surface areas (Version<br> 1.1).</p>
SDM current and future projections for alternate CAM-based cultivation in SSA
<p class="MsoNormal">Globally we are facing an emerging climate crisis, with impacts to be notably felt in semi-arid regions across the world. Cultivation of drought-adapted succulent plants has been suggested as a nature-based solution that could: (i) reduce land degradation, (ii) increase agricultural diversification and provide both economic and environmentally sustainable income through derived bioproducts and bioenergy, (iii) help mitigate atmospheric CO<sub>2</sub> emissions, and (iv) increase soil sequestration of CO<sub>2</sub>. Identifying where succulents can grow and thrive is an important pre-requisite for the advent of a sustainable alternative 'bio-economy'. Here we first explore the viability of succulent cultivation in Africa under future climate projections to 2100 using species distribution modelling to identify climatic parameters of greatest importance and regions of environmental suitability. Minimum temperatures and temperature variability are shown to be key controls in defining the theoretical distribution of three succulent species explored, and <span>under both current and future SSP5 8.5 projections, the conditions required for growth of at least one of the species is met in most parts of sub-Saharan Africa. </span>These results are supplemented with an analysis of potentially <em>available</em> land for alternative succulent crop cultivation. <span>In total</span>, <span>up to 1.5 billion hectares could be considered ecophysiologically suitable and available for succulent cultivation due to projected declines in rangeland biomass and yields of traditional crops. These </span>findings may serve to highlight new opportunities for farmers, governments, and key stakeholders in the agriculture and energy sectors to invest in sustainable bioeconomic alternatives that deliver on environmental, social and economic goals.<span> </span></p>
Microsatellite genotypes of Japanese abies species: Insights from population genetics and SDM
<p><span>Range shifts during the Pleistocene shaped the unique phylogeographical structures of many species. Pleistocene range shifts gave currently allopatric species opportunities to occur in sympatry, likely resulting in ancient introgressions between related taxa. In our study, we investigate the range shifts and introgression patterns of three Japanese <em>Abies </em>species (<em>A. firma, A. homolepis, and A. veitchii</em>) by employing an extensive survey of 43 populations. This survey includes comprehensive analysis of both mitochondrial (mtDNA) and nuclear (18 microsatellites) genomes, in combination with species distribution modeling (SDM). It is important to note that these two types of markers provide distinct and complementary information, as they have different modes of inheritance and mutation rates. Bayesian clustering analysis indicates that the three species were clearly separated, with the exception of the <em>A. homolepis </em>var. <em>umbellata</em> population, which is considered a natural hybrid between <em>A. homolepis</em> and <em>A. firma</em>. However, mtDNA haplotypes of the four northern populations of <em>A. firma</em> were entirely replaced by two major haplotypes of <em>A. homolepis </em>and <em>A. veitchii.</em> The results of Neighbor-net, NewHybrids, STRUCTURE analyses, and SDM suggest that historical introgression between species occurred in each geographic region, with mtDNA capture being the likely mechanism. However, contrary to these findings, the ABC coalescent analysis did not support an ancient introgression. Therefore, further validation with genome-wide level data is needed to clarify this issue. Our conclusion is that climate-induced range shifts during the Pleistocene/Holocene likely played a crucial role in the observed patterns of introgression in these species.</span></p>
SDM for Stroke Prevention in Atrial Fibrillation
ClinicalTrials.gov study NCT02905032. IPD Sharing: NO. Countries: 1. Publications: 6.
SDM POSSIBLE: A Breast Cancer Treatment Decision Aid for Women 70+ With Low-Risk Stage I Breast Cancers
ClinicalTrials.gov study NCT06896474. IPD Sharing: YES. Countries: 1. Publications: 51.
SDM current and future projections for alternate CAM-based cultivation in SSA
Open the record for dataset details and reuse information.
Microsatellite genotypes of Japanese abies species: Insights from population genetics and SDM
Open the record for dataset details and reuse information.
Data from: Low incidence of choroidal neovascularization following subthreshold diode micropulse laser (SDM) in high-risk AMD
Purpose: To determine the incidence of new choroidal neovascularization (CNV) in eyes with dry age-related macular degeneration (AMD) following subthreshold diode micropulse laser (SDM). Method: In an observational retrospective cohort study, the records of all patients active in the electronic medical records database were reviewed to identify eyes with dry AMD treated with SDM. Identified eyes were classified by simplified AREDS categories, and analyzed for the primary endpoint of new CNV after treatment. Results: The EMR revealed SDM was offered to 373/392 (95%) patients with dry AMD and elected by 363/373 (97%) between 2008-2017. Follow up was available for 354/363 patients (547 eyes, 98%) (range 6-108 mos., avg. 22). CNV risk factors included age (median 84 years, 67% > 80); reticular pseudodrusen (214 eyes, 39%); AREDS category (78% category 3 and 4); and fellow eye CNV (128 eyes, 23%). New CNV developed in 9/547 eyes (1.6%, annualized rate 0.87%). Visual acuity was unchanged. There were no adverse treatment effects. Summary: In a review of a large group of eyes with exceptionally high-risk AMD, SDM was followed by a very low incidence of new CNV. If confirmed by further study, SDM would offer a new and highly effective treatment to reduce the risk of vision loss from AMD.
Fig. 5. Termite SDM predictions for species located within Southern Australia for A in Utilization of Community Science Data to Explore Habitat Suitability of Basal Termite Genera
Fig. 5. Termite SDM predictions for species located within Southern Australia for A. Porotermes adamsoni (Stolotermitidae)(left) and B. Stolotermes victoriensis (right), and C. Mastotermes darwiniensis (Mastotermitidae). Final model predictions were generated using our thinned occurrence dataset and final set of uncorrelated environmental variables for each species, with 10 bootstrap replicates with 'cloglog' outputs in which raw values are converted to a range of 0 - 1 to approximate a probability of occurrence (Cobos et al. 2018). Brighter colors indicate areas of higher suitability (higher probability of occurrence), while darker colors indicate areas of lower suitability (lower probability of occurrence)..
Fig. 4. Termite SDM predictions for species located within Eastern United States and Canada for A in Utilization of Community Science Data to Explore Habitat Suitability of Basal Termite Genera
Fig. 4. Termite SDM predictions for species located within Eastern United States and Canada for A. Zootermopsis nevadensis (Stolotermitidae)(left) B. Zootermopsis angusticollis (right), and C. Zootermopsis laticeps (bottom). Final model predictions were generated using our thinned occurrence dataset and final set of uncorrelated environmental variables for each species, with 10 bootstrap replicates with 'cloglog' outputs in which raw values are converted to a range of 0 - 1 to approximate a probability of occurrence (Cobos et al. 2019). Brighter colors indicate areas of higher suitability (higher probability of occurrence), while darker colors indicate areas of lower suitability (lower probability of occurrence).
SDM results for 10,590 tree species from "Regional uniqueness of tree species composition and response to forest loss and climate change"
<p>Output from species distribution models (SDMs) with geographic constraints to estimate the spatial distribution of tree species at the global level at a 30-arc second resolution, presented in the publication "Regional uniqueness of tree species composition and response to forest loss and climate change". </p> <h2>Data</h2> <p>This file contains the results for 10,590 tree species. The results for each species are contained in a directory with the species name connected by an underscore. For most species, the directory contains several .tif files that make up the tiles of the distribution maps for that species and a metadata file. The .tif files can be merged with the gdal_merge.py function to obtain a single .tif file per species (see example below). For some species, the directory contains a single .tif file which does not require merging. In all cases, the .tif files contain 9 bands that correspond to the predicted species distribution using climatic variables corresponding to various climate projections from Chelsa 2.1.</p> <h3>Band order</h3> <ol> <li>covariates_1981_2010: average of historical climate measurements from 1981 to 2010</li> <li>covariates_2011_2040_ssp126: average future climate projection for 2011-2040 under shared socioeconomic pathway (SSP) 1.26</li> <li>covariates_2011_2040_ssp370: average future climate projection for 2011-2040 under SSP 3.70</li> <li>covariates_2011_2040_ssp585: average future climate projection for 2011-2040 under SSP 5.85</li> <li>covariates_2041_2070_ssp126: average future climate projection for 2041-2070 under SSP 1.26</li> <li>covariates_2041_2070_ssp370: average future climate projection for 2041-2070 under SSP 3.70</li> <li>covariates_2041_2070_ssp585: average future climate projection for 2041-2070 under SSP 5.85</li> <li>covariates_2071_2100_ssp126: average future climate projection for 2071-2100 under SSP 1.26</li> <li>covariates_2071_2100_ssp370: average future climate projection for 2071-2100 under SSP 3.70</li> <li>covariates_2071_2100_ssp585: average future climate projection for 2071-2100 under SSP 5.85</li> </ol> <h3>Metadata</h3> <p>The metadata contains more information about the bands, as well as the following species-level properties:</p> <ul> <li>nobs: number of spatially distinct occurrence records used in model training</li> <li>precision: precision of binarised model output computed through 3-fold cross-validation</li> <li>threshold: threshold used to binarise probabilistic model output, determined as the threshold maximizing the true skill statistic (TSS) during 3-fold cross-validation</li> <li>f1: F1 score of binarised model output computed through 3-fold cross-validation</li> <li>auc: area under the ROC curve (AUC) of model output computed through 3-fold cross-validation</li> <li>prevalence: prevalence of presences (ie. occurrences records) throughout the training data which consisted of occurrence records and pseudo-absences</li> <li>tss: TSS of binarised model output computed through 3-fold cross-validation</li> <li>recall: recall of binarised model output computed through 3-fold cross-validation</li> <li>nativeness_info: indicates whether reported native countries were available for this species (possible values: "yes" or "no", should be "yes" for all species included)</li> <li>npa: number of pseudo-absences used in model training</li> <li>system:index: species name </li> </ul> <h3>Merging example</h3> <p>For example, the directory Abarema_barbouriana contains files Abarema_barbouriana_0.tif, Abarema_barbouriana_2.tif, ..., Abarema_barbouriana_9.tif and metadata.json. The tiles can be merged with the command "gdal_merge.py -o Abarema_barbouriana_merged.tif Abarema_barbouriana/Abarema_barbouriana_*.tif".</p>
American Beech SDM DATA
<p><strong>Project:</strong> Anticipating Shifts in American Beech Distribution in a Changing Climate</p> <p><strong>Description:</strong></p> <p>This dataset supports a study on species distribution modeling (SDM) of American Beech (<em>Fagus grandifolia</em>) to anticipate its distribution shifts under climate change scenarios. The data includes a stratified random sampling of presence points based on the USFS basal area grid, variance inflation factor (VIF) analysis, correlation analysis, environmental data (with comparisons between POLARIS and SoilGrids), species occurrence points, random forest variable importance analysis, ensemble forecasting across various current and future climate scenarios (SSP1, SSP2, SSP3), and projections of future habitat suitability. The dataset is used in both current-only and future scenario modeling, covering SSP1, SSP2, and SSP3 climate scenarios for 2011–2040, 2041–2070, and 2071–2100. It also includes model evaluation metrics, range size comparisons, and ensemble modeling outputs.</p> <p>Additionally, Multivariate Environmental Similarity Surface (MESS) analyses are conducted to assess the similarity between current environmental conditions and future climate projections. MESS results for SSP1, SSP2, and SSP3 scenarios are included in the dataset for all three periods (2011–2040, 2041–2070, 2071–2100), providing insight into areas where environmental conditions deviate from the present climate envelope.</p> <p><strong>Keywords:</strong> American Beech, Species Distribution Modeling, Climate Change, SSP Scenarios, Random Forest, Ensemble Modeling, Future Projections, MESS Analysis.</p>
RCT Regarding SDM Online Training and Face-to-face SDM Training
ClinicalTrials.gov study NCT02674360. IPD Sharing: NO. Countries: 1. Publications: 9.
SDM Laser for Non Central Diabetic CSME
ClinicalTrials.gov study NCT03226951. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
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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.