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17,036 results for “Nature”
Topological surface states in epitaxial (SnBi2Te4 )n (Bi2Te3)m natural van der Waals superlattices (data)
<p>This dataset contains the raw data files connected to the figures included in the paper "T<em>opological surface states in epitaxial (SnBi<sub>2</sub>Te<sub>4</sub> )<sub>n</sub> (Bi<sub>2</sub>Te<sub>3</sub>)<sub>m</sub> natural van der Waals superlattices</em>" by S. Fragkos et al., Phys. Rev. Materials <strong>5</strong>, 014203 (2021) <a href="https://doi.org/10.1103/PhysRevMaterials.5.014203">https://doi.org/10.1103/PhysRevMaterials.5.014203</a></p> <p>An Open Access version of the paper can be found here: <a href="https://zenodo.org/record/4562057#.YaDC4NBBxPY">https://zenodo.org/record/4563899#.YaDQ5NBBxPY</a></p>
Natural Products of ChemBioSys
<p>Documented collection of natural product structures that have been discovered, described or mentioned in a review by members of the CRC ChemBioSys (chembiosys.de)</p>
Data for 'Stakeholder Perspectives on Nature, People, and Sustainability at Mount Kilimanjaro'
<p>Title: Data for ‘Stakeholder Perspectives on Nature, People, and Sustainability at Mount Kilimanjaro’</p> <p>Recommended Citation: Masao CA, Prescott GW, Snethlage MA, Urbach D, Torre-Marin Rando A, Molina-Venegas R, Mollel NP, Hemp C, Hemp A, Fischer M (2022). People and Nature.</p> <p>Principal Investigator:<br> - Markus Fischer (markus.fischer@ips.unibe.ch)</p> <p>Authors:<br> * joint first-author<br> - Catherine A. Masao (ndeutz@yahoo.com, ORCID: 0000-0002-1242-9117) *<br> - Graham W. Prescott (graham.prescott.research@gmail.com, ORCID: 0000-0001-5123-514X) *<br> - Mark A. Snethlage (mark.snethlage@ips.unibe.ch, ORCID: 0000-0002-1398-8869) *<br> - Davnah Urbach (davnah.payne@ips.unibe.ch, ORCID: 0000-0001-9170-7834) *<br> - Amor Torre-Marin Rando (amor.torre@ips.unibe.ch)<br> - Rafael Molina Venegas (rafmolven@gmail.com, ORCID 0000-0001-5801-0736)<br> - Neduvoto P. Mollel (neduvotomollel@yahoo.com, ORCID: 0000-0002-4402-4667)<br> - Claudia Hemp (claudiahemp@yahoo.com, ORCID: 0000-0002-5369-2122)<br> - Andreas Hemp (andreas.hemp@uni-bayreuth.de, ORCID: 0000-0001-9170-7113)<br> - Markus Fischer (markus.fischer@ips.unibe.ch, ORCID: 0000-0002-5589-5900)</p> <p>Date of data collection: 2018-09<br> Location of data collection: Moshi, Kilimanjaro Region, Tanzania<br> Date of final file release: 2022-01-13</p> <p>Data Overview:</p> <p>We conducted a three-day stakeholder workshop in Moshi, Tanzania, in September 2018. The workshop was attended by 73 participants (16 women and 57 men), whom we invited to represent various sectors and local communities. We established the list of invitees through an extensive online search validated and complemented by key local informants. We divided registered participants into five groups based on their sectoral affiliation: 16 residents of local communities, including farmers (herein ‘Community’), 14 researchers and scientists (‘Research’), 16 professionals in conservation and management (‘Conservation’), 17 professionals in forestry, agriculture, and water management and governance (‘Resources’), and 10 other professionals mainly drawn from the tourism sector (‘Other’).</p> <p>We used two questionnaires—herein ‘habitat’ and ‘ecosystem services’— with open and closed questions. Closed questions were scored using a Likert-type scale.</p> <p>File overview:</p> <p>1. kilimanjaro_ipbes_workshop_habitat_questionnaire.csv</p> <p>Data from the ‘habitat’ questionnaire, entered by Catherine A. Masao and Mark A. Snethlage (finalised 2020-09-22). Individual perceptions about the state of and trends in habitats and species diversity and about the direct and indirect factors driving these trends. We invited participants to fill out separate questionnaires for each habitat of importance to their sector or for which they had knowledge, starting with the most important one.</p> <p>2. kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv</p> <p>Data from the ‘ecosystem services’ questionnaire, entered by Catherine A. Masao and Mark A. Snethlage (finalised 2020-01-09). The ‘ecosystem services’ questionnaire collected individual perceptions about the state of, trends in, and importance of NCP (Nature's Contributions to People), as well as about the factors driving observed changes in access and provision. With reference to the preliminary group discussion on NCP, we invited participants to fill out separate forms for each NCP they deemed important to their sector or had knowledge about and to indicate which habitat(s) provide(s) each of them.</p> <p>3. kilimanjaro_ipbes_workshop_ecosystem_services_access_change_codes.csv</p> <p>Adapted from the ecosytem services questionnaire data (kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv), coding the reasons for change in access to NCP.</p> <p>4. kilimanjaro_ipbes_workshop_spatial_scales_recommended_measures.csv</p> <p>Tally of recommended measures towards recorded from the carousel session, grouped by spatial scale and Conservation Measures Partnership (CMP) categories. See Table S7 for details.</p> <p><br> Code used for analysis:<br> R code used for the statistical analysis and to create the figures available from: https://github.com/grahamprescott/kilimanjaro.ipbes.workshop.paper</p> <p>File details:</p> <p>1. kilimanjaro_ipbes_workshop_habitat_questionnaire.csv</p> <p>143 observations of 73 variables</p> <p>Key Variables:<br> - Group<br> (categorical - stakeholder group to which participants were assigned. Blue = Community, Green = Research, Orange = Conservation, Red = Other, Yellow = Resources)<br> - Biome2<br> (categorical - standardised habitat categories used in the analysis, coded by Mark A. Snethlage)<br> - Habitat.area<br> (categorical - trends in habitat area over past 10 years (2008-2018); Decreased, Not Changed, Increased, No Answer)<br> - Habitat.condition<br> (categorical - trends in habitat condition over past 10 years (2008-2018); Deteriorated, Not Changed, Improved, No Answer)<br> - Habitat.area.will<br> (categorical - prediction for trend in habitat condition over next 10 years (2018-2028); Decrease Not Change, Increase, No Answer)<br> - Habitat.condition.will<br> (categorical - trends in habitat condition over past 10 years (2018-2028); Decrease, Not Change, Increase, No Answer)<br> Variables beginning with ES., DIR., IND., ACT. refer to ecosystem services (i.e. NCP), direct drivers, indirect drivers, and recommended actions associated with each habitat form. They are numerical and scored as 1 if that variable is mentioned (present) or 0 if not mentioned (absent). In a few cases where different ecosystem services listed by the participant are coded to the same variable the number is the number of times that ecosystem service is mentioned.</p> <p>Codes for ecosystem services (ES.): HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), OCE (Regulation of Ocean Acidification), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOL (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), PST (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), LRN (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), OPT (Maintenance of Options), WEB (Human Wellbeing), LIV (Livelihoods). Note: WEB and LIV are not traditionally included in NCP categories, but we created them as additional categories to capture responses that could not strictly be placed into the traditional 18 categories. </p> <p>Codes for direct drivers (DIR.): ACT = ‘Human Activities’, CC = Climate Change, IAS = Invasive Alien Species, LUC = Land-Use Change, OVR = Overexploitation, POL = Pollution.</p> <p>Codes for indirect drivers (IND.): CLT = Cultural, DEM = Demographic, ECO = Economic, GOV = Governance, S.T = Science and Technology.</p> <p>Codes for recommended actions (ACT.): AWR = Awareness Raising, ECO = Livelihood, Economic & Moral Incentives, EDU = Education & Training, ENF = Law Enforcement & Prosecution, INS = Institutional Development, LAN = Land / Water Management, LAW = Legal & Policy Frameworks, PRT = Conservation Designation & Planning, RSR = Research & Monitoring, SPC = Species Management.</p> <p>2. kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv</p> <p>144 observations of 38 variables</p> <p>Key variables:</p> <p>- Group<br> (categorical - stakeholder group to which participants were assigned. Blue = Community, Green = Research, Orange = Conservation, Red = Other, Yellow = Resources)<br> - Service.original (free text response to which ecosystem service the participant was filling out the form)<br> - ESCODE<br> (categorical - NCP category to which we assigned the free text response. Abbreviations: HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), OCE (Regulation of Ocean Acidification), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOL (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), PST (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), LRN (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), OPT (Maintenance of Options), WEB (Human Wellbeing), LIV (Livelihoods). Note: WEB and LIV are not traditionally included in NCP categories, but we created them as additional categories to capture responses that could not strictly be placed into the traditional 18 categories.)<br> - Biome<br> (categorical - which habitat provided the ecosystem service)<br> - Why.changed.provision<br> (free text response for why Provision changed)<br> - Why.changed.access<br> (free text response for why Access changed) [Note: although we theoretically expected a distinction between provision and access of each ecosystem service, we observed that this distinction was not strictly followed in practice and deemed the responses about access to be most accurate]<br> - Access<br> (categorical - changes in access to the ecosystem service over the last 10 years (2008-2018); Decreased, No Change, Increased, No Answer)<br> - Access.will<br> (categorical - predicted changes in access to the ecosystem service over the next 10 years (2018-2028); Deteriorate, Not Change, No Answer, Improve (note: no one responded ‘Improve’)) </p> <p><br> 3. kilimanjaro_ipbes_workshop_ecosystem_services_access_change_codes.csv</p> <p>144 observations of 7 variables</p> <p>We took the following variables from the ecosystem services questionnaire:<br> - ESCODE<br> (categorical - NCP category to which we assigned the free text response)<br> - Access<br> (whether access to this NCP increased or decreased between 2008-2018)<br> - Why.changed.access<br> (free text response for why Access changed)<br> And created a new variable to synthesise the drivers of change in NCP access:<br> - Why.changed.access.code</p> <p>Note: a challenge with the ‘Why.changed.access’ variable is that many drivers are listed in the same response. To process this, we duplicated the rows with multiple drivers so that there would be one row per driver. We did this using Microsoft Excel for Mac. We did this so that each link from a driver to an increase or decrease in a given NCP could be visualised. The individual links are not standardised by individual respondent or response. They represent every instance of a reported link between a driver of change and a change in access to a given NCP. Responses or respondents who listed multiple instances of NCP access change and/or multiple drivers have therefore contributed more to the Sankey figure (Figure 4). We chose this approach because the aim in this case was to document the complex web of drivers leading to changes in NCP access, drawing upon the collective expertise of the respondents, not to test for individual differences between groups or respondents. Graham W. Prescott and Mark A. Snethlage independently coded each of the drivers and reached a consensus on any disagreements. Graham W. Prescott edited the final file.</p> <p>4. kilimanjaro_ipbes_workshop_spatial_scales_recommended_measures.csv</p> <p>11 observations of 6 variables<br> <br> We also conducted a carousel session in which participants could suggest actions and actors that could contribute towards achieving a sustainable future for people and nature at Mt. Kilimanjaro. This file contains the tally of recommended measures arising from this carousel session, grouped by spatial scale and Conservation Measures Partnership (CMP) categories. For full list of measures, see Table S7.</p> <p> </p> <p> </p>
PEATCLSM_Trop: Integrating peat-specific land surface hydrology of natural and drained tropical peatlands in the GEOS CLSM framework
<p>The datasets archived here include simulation results shown in the peer-reviewed article “Tropical peatland hydrology simulated with a global land surface model“, published in the open access AGU Journal of Advances in Modeling Earth Systems (JAMES; Apers et al., 2022). The output was produced using the Catchment land surface model (CLSM), the land model component of the NASA Goddard Earth Observing System (GEOS) modeling framework, and various versions of peatland-specific adaptations of CLSM, i.e. PEATCLSM. Here, we provide netCDF files (*.nc or *.nc4c) for CLSM, the natural (PEATCLSM<sub>Trop,Nat</sub>), and drained (PEATCLSM<sub>Trop,Drain</sub>) tropical versions of PEATCLSM. The simulations are at a 9-km spatial resolution (EASEv2 grid) for the three major tropical peatland regions in Central and South America, the Congo Basin, and Southeast Asia, using a peat grid cell distribution that is a combination of the PEATMAP distribution from Xu et al. (2018) and the peat distribution from De Lannoy et al. (2014). Simulations with the northern version of PEATCLSM (PEATCLSM<sub>North,Nat</sub>) are not included in the archived dataset but can be obtained upon request. We provide three types of netCDF files:<br> • daily_images_*.nc4c: daily land states and fluxes for variables discussed in Apers et al., (2022; Table 1), provided as netCDF image-chunked image stack;<br> • daily_mean_*.nc: 20-year mean of the land states and fluxes (Table 1), provided as a single netCDF image;<br> • daily_std_*.nc: 20-year standard deviation of the land states and fluxes (Table 1), provided as a single netCDF image.</p> <p>The file content is described in the file PEATCLSM_Trop-Simulations.pdf.</p> <p>Please contact Sebastian Apers (sebastian.apers@kuleuven.be) or Michel Bechtold (michel.bechtold@kuleuven.be) for any questions.<br> <br> References:<br> Apers, S., De Lannoy, G. J. M., Baird, A. J., Cobb, A. R., Dargie, G. C., del Aguila Pasquel, J., … others (2022). Tropical peatland hydrology simulated with a global land surface model. <em>Journal of Advances in Modeling Earth Systems</em>. https://doi.org/10.1029/2021MS002784<br> Bechtold, M., De Lannoy, G. J. M., Koster, R. D., Reichle, R. H., Mahanama, S. P., Bleuten, W., ... others (2019). PEAT-CLSM: A specific treatment of peatland hydrology in the NASA Catchment Land Surface Model. <em>Journal of Advances in Modeling Earth Systems, 11</em>(7), 2130–2162. https://doi.org/10.1029/2018MS001574<br> De Lannoy, G. J. M., Koster, R. D., Reichle, R. H., Mahanama, S. P. P., & Liu, Q. (2014). An updated treatment of soil texture and associated hydraulic properties in a global land modeling system. <em>Journal of Advances in Modeling Earth Systems, 6</em>(4), 957– 979. https://doi.org/10.1002/2014MS000330<br> Xu, J., Morris, P. J., Liu, J., & Holden, J. (2018). PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. <em>Catena, 160</em>, 134–140. https://doi.org/10.1016/j.catena.2017.09.010</p>
Data and ancillary data for publication: Natural infrastructure and water erosion mitigation in the Andes
<p>The data contain information on the effectiveness of natural infrastructure to mitigate soil erosion. Data were compiled from 118 case studies from the Andean region, whereby information on natural infrastructure interventions, soil erosion and soil quality were tabulated and analysed.</p> <p>The data contains the following documents:<br> -Database with data on soil erosion, soil quality for different types of natural infrastructure (118 case studies)<br> -Metadata<br> -Summary of terms used in the systematic review of the literature (in Spanish and English)<br> -List of bibliographic data sources that were searched with the search terms<br> -Full bibliographic references of all 118 case studies</p> <p><strong>Full reference </strong></p> <p><em>Vanacker V, Molina A, Rosas-Barturen M, Bonnesoeur V, Román-Dañobeytia F, Ochoa-Tocachi B, Buytaert W (2022). The effect of natural infrastructure on water erosion mitigation in the Andes. </em></p> <p> </p> <p> </p>
Supplementary material for "High semi-natural vegetation cover and heterogeneity of field sizes promote bird beta-diversity at larger scales in Ethiopian Highlands"
<p><strong>Abstract</strong></p> <ol> <li>The intensification of farming practices exerts detrimental effects on biodiversity. Most research has focused on declines in species richness at local scales (alpha-diversity) although species loss is exacerbated by biotic homogenization that operates at larger scales (i.e., affecting beta-diversity). The majority of studies have been conducted in temperate, industrialized countries while tropical areas remain poorly studied. Agricultural landscapes of sub-Saharan Africa are still largely dominated by small-scale subsistence farming, but strenuous efforts to intensify farming practices are currently spreading to meet a growing food demand. It is therefore crucial to understand how these intensified practices affect biodiversity to mitigate their negative impacts. </li> <li>We investigated how farming system (small- vs large-scale farming) and landscape complexity (semi-natural vegetation cover) drive bird species composition, community turnover, and beta-diversity patterns in Ethiopian Highlands’ agroecosystems. We evaluated the following hypotheses: (1) large-scale farming homogenizes bird communities, (2) community turnover is higher in small-scale farms, (3) interactive effects between landscape complexity and farming systems shape avian communities, (4) heterogeneity of field sizes increases community turnover at larger scales. </li> <li>Bird communities underwent greater compositional changes along the landscape complexity than along the agricultural intensity gradient. Contrary to our expectations, beta-diversity was not significantly lower within large-scale farms (no biotic homogenization), and complex landscapes that still offer a high amount of semi-natural vegetation promoted community turnover in both farming systems. </li> <li>Semi-natural vegetation cover mediated how avian communities responded to agricultural intensification: the compositional differences between small- and large-scale farms increased with vegetation cover, further promoting avian community heterogeneity at the landscape level.</li> <li>The heterogeneity in field sizes also enhanced bird community turnover, suggesting that a combination of both small- and large-scale farming systems within a given landscape unit would promote beta-diversity at larger scales, provided large-scale farms do not become dominant.</li> <li>Synthesis and applications: Landscape complexity shaped avian communities to a stronger degree than farming intensity, emphasizing the importance of semi-natural vegetation and landscape heterogeneity for the maintenance of diverse bird communities and for achieving multifunctional landscapes promoting biodiversity and associated ecosystem services on the High Ethiopian plateaus. <br> </li> </ol>
The European Natural Gas Demand database (ENaGaD)
<p>The ``European Natural Gas Demand'' (ENaGaD) database is composed of daily time series of the national demand of natural gas for each of the 25 European Member States with a transmission system and some European Countries. The series are compiled and presented from 2015 to 2020 in energy unit of measurement. Values are mainly collected from the transparency platform of National Transmission System Operators in compliance to Regulation (EC) No 715/2009. Whenever possible, the daily demand is further divided in consumption by electricity and heat producers, consumption by industrial users and by households. The ENaGaD database is also available from the Joint Research Centre Data Catalogue at <a href="https://data.jrc.ec.europa.eu/">https://data.jrc.ec.europa.eu</a>.</p> <p>A newer public version of ENaGad (superseding version 1.1) is now available at <a href="https://data.jrc.ec.europa.eu/collection/id-00372">https://data.jrc.ec.europa.eu/collection/id-00372</a> .</p>
Data/ codes used in the the Natural Hazards and Earth System Sciences (NHESS) publication titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast" by Pranavam Ayyappan Pillai et al. (2022)
<p>The archive contains datasets and codes used in the manuscript titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast", and published in the journal <em>Natural Hazards and Earth System Sciences</em> (<em>NHESS</em>) by Pranavam Ayyappan Pillai et al., 2022.</p> <p>Pranavam Ayyappan Pillai, U., Pinardi, N., Federico, I., Causio, S., Trotta, F., Unguendoli, S., and Valentini, A.: Wind-Wave Characteristics and extremes along the Emilia-Romagna coast, Nat. Hazards Earth Syst. Sci. Discuss. https://doi.org/10.5194/nhess-2022-103, 2022.</p>
Assessing ambitious nature conservation strategies in a below 2-degree and food-secure world – supplementary spatial data
<p><strong>Assessing ambitious nature conservation strategies in a below 2-degree and food-secure world – supplementary spatial data</strong></p><p><strong>Authors: </strong>Marcel Kok, Johan Meijer, Willem-Jan van Zeist, Jelle Hilbers, Marco Immovilli, Jan Janse, Elke Stehfest, Michel Bakkenes, Andrzej Tabeau, Aafke Schipper, Rob Alkemade</p><p><strong>Point of contact:</strong> <a href="mailto:Marcel.Kok@pbl.nl">Marcel.Kok@pbl.nl</a></p><p><strong>Research paper summary:</strong> Global biodiversity is projected to further decline under a wide range of future socio-economic development pathways, even in sustainability-oriented scenarios. This raises the question how biodiversity can be put on a path to recovery, the core challenge for the implementation of the CBD Kunming-Montreal Global Biodiversity Framework. We designed two ambitious global conservation strategies, 'Half Earth' (HE) and 'Sharing the Planet' (SP), and evaluated their ability to restore terrestrial and freshwater biodiversity and to provide nature's contributions to people (NCP), while also limiting global warming below 2 degrees and ensuring food security. We applied the integrated assessment framework IMAGE with the GLOBIO biodiversity model, using the 'Middle of the Road' Shared Socio-economic Pathway (SSP2) with its projected human population growth as baseline. We found that the HE strategy performs generally better for terrestrial biodiversity (biodiversity intactness (MSA), Area of Habitat, Living Planet Index, Red List Index) in currently still natural regions. The SP strategy yields more improvements for biodiversity in human-used areas, for freshwater biodiversity and for regulating NCP (pest control, pollination, erosion control, water quality). However, both strategies were insufficient to restore biodiversity and corresponded with considerable increases in food security risks and global temperature. Only when we combined the conservation strategies with a portfolio of 'integrated sustainability measures', including climate change mitigation and reductions of food waste and animal product consumption, our scenarios resulted in a restoration of biodiversity and NCP while keeping global warming below two degrees and food security risks below the baseline projection.</p><p><strong>Contents:</strong> This repository contains the supplementary spatial data describing the specific prioritization of conservation areas under the Half Earth (HE) and Sharing the Planet (SP) scenarios, and the resulting scenario land use and MSA data sets for the year 2050, including also a baseline (BL) scenario. All spatial data is in geotiff format at a 10 arcsecond resolution in WGS84 coordinate system. Detailed description of the methodology is provided in the paper listed under "related identifiers".</p><p><strong>Keywords:</strong> Nature conservation, Half Earth, Sharing the Planet, Climate Change, Food Security, Solution-oriented scenarios, Biodiversity, Nature's Contribution to People, NCP</p>
OpenStreetMap+ Protected nature areas in continental Europe (IUCN status + Natura 2000)
<p>Twelve maps of continental Europe indicating the protected nature area status in 2019 according to <a href="https://ec.europa.eu/environment/nature/natura2000/index_en.htm">Natura 2000</a> and the <a href="https://www.iucn.org/">International Union for Conservation of Nature</a> (IUCN). The IUCN status was extracted from crowdsourced data obtained from OpenStreetMap through geofabrik.de.</p> <p>This dataset contains:</p> <ul> <li>3 raster maps representing Natura 2000 protection status (A, B and C), named <strong>Natura2000_[status].tif</strong></li> <li>8 raster maps representing OSM-derived IUCN protection status (1a, 1b, 2, 3, 4, 5, 6, and 'other'), named <strong>OSM_IUCN_[status].tif</strong></li> <li>1 aggregated map (<strong>adm_protected.area_natura2000.osm_p_30m_0..0cm_2019..2021_eumap_epsg3035_v0.1</strong>) where each of the 11 protection statuses, as well as pixels where multiple statuses apply, are assigned a unique value. This map can also be accessed interactively at <a href="https://maps.opendatascience.eu/?base=OpenStreetMap%20(grayscale)&layer=Natura2000-OSM%20Protected%20areas&zoom=4&eye=5000000&center=53.7139,17.0066&opacity=45">maps.opendatascience.eu</a>.</li> </ul> <p>All files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files for the aggregated raster are provided in both <strong><em>SLD</em></strong> and <strong><em>QML</em></strong> format.</p>
Analysis of the interacting residues between wild type SARS-CoV-2 spike protein and natural ligand hACE2, as well as three engineered alternative ligands
<p>The analysis of residue interactions between the SARS-CoV-2 spike protein and its natural (hACE2 <sup>1</sup>) and engineered binders P17 Fab <sup>2</sup>, Ty1 VHH <sup>3</sup> and LCB1 peptide <sup>4</sup> reveals that glutamine, serine and especially tyrosine residues on the ligand side are more frequent and influence spike binding efficiency, and that spike residues Glu484, Phe486, Tyr489 and Gln493 are more recurrent targets for interactions with ligands. The list of residues establishing contacts between the wild type structure of the SARS-CoV-2 spike protein and the binders defined above are described in Table 1. In Figure 1, the frequency and type of amino acids that interact with each spike residue is illustrated.</p>
Ethiopian archives of nature: photographs
<p>01. Hiking trail between Sankaber and Gich, Simien, 2013.</p> <p>02. The material shaping of nature: Village of Gich, Simien Mountains, September 2013 and January 2019.</p> <p>03. An archival collection in the environmental history of Ethiopia: EWCA warehouse, Lideta district, July 2016; EWCA offices, Yobek district, April 2021. (Photographs by Guillaume Blanc [2016] and Kidanemariam Woldegiorgis Ayalew [2021]).</p> <p>04. The “John Blower” collection: “JB” binders, Ethiopian Wildlife Conservation Authority Library, Addis Ababa, 2016.</p>
Predicted 13C NMR Chemical Shifts of Natural Products
<p>The Natural Product structures are those from the <a href="https://zenodo.org/record/5336220">COCONUTv5 database</a> .</p> <p>Predictions were obtained by means of the "Check Chemical Shifts" method from <a href="https://www.acdlabs.com/">ACD/Labs</a> C+H NMR Predictors and DB software, version 2020.1.0.</p> <p>The acd_coconut.zip archive contains a single file, acd_coconut.sdf, a collection of 2D structures from COCONUT supplemented by <sup>13</sup>C NMR chemical shifts values from ACD/Labs CNMR Predictor in verification mode.</p> <p>The file mol1.sdf describes the first compound in acd_coconut.sdf and indicates how chemical shift values are encoded.</p> <p>SDF tags related to NMR:</p> <ul> <li><CNMR_SHIFTS> for ACD/Labs DB software</li> <li><Predicted 13C shifts>, <Quaternaries>, <Tertiaries>, <Secondaries>, <Primaries> for <a href="https://sourceforge.net/projects/mixonat/">MixONat</a></li> <li><NMREDATA_ASSIGNMENT>, <NMREDATA_ORIGIN> in the style of <a href="https://nmredata.org/">NMReDATA</a></li> </ul> <p>The calculation workflow is based on tools developped <a href="https://github.com/nuzillard/KnapsackSearch/">here</a>.</p> <p>No attempt was made to change unlikely tautomers (like aliphatic iminols standing for aliphatic amides). Unlikely structures are likely associated to unlikely predicted chemical shift value sets.</p>
Data for manuscript "Creating boundaries along a synthetic frequency dimension" in Nature Communications
<p>Data for manuscript "Creating boundaries along a synthetic frequency dimension"</p> <p>https://www.nature.com/articles/s41467-022-31140-7</p> <p>https://arxiv.org/abs/2203.11296</p>
Numerical data analysed to produce Figure 3a of Nature Climate Change submission "Five challenges for subseasonal to decadal prediction research " by Merryfield et al.
<p>NetCDF4-formatted files containing daily sea ice concentration data from Environment and Climate Change Canada's CanSIPSv2 seasonal forecasting system described in Lin et al. (2020) https://doi.org/10.1175/WAF-D-19-0259.1 </p> <ul> <li>2 models, CanCM4i and GEM-NEMO</li> <li>10 ensemble members for each model, each in separate files as indicated by suffixes _1 to _10</li> <li>initialized May 1, 1980 to 2021</li> <li>840 files total (42 predicted years x 10 ensemble members x 2 models)</li> <li>model outputs interpolated to common 1-degree grid</li> </ul> <p>The calibrated probabilistic forecast map shown in Figure 3a is based on the nonhomogeneous censored Gaussian regression (NCGR) method described in Dirkson et al, (2021) https://doi.org/10.1175/WAF-D-20-0066.1 and produced using scripts available at https://github.com/adirkson/sea-ice-timing </p> <p>The procedure uses as inputs</p> <ul> <li>freeze-up dates calculated from the provided model outputs as described in Sigmond et al. (2016) https://doi.org/10.1002/2016GL071396</li> <li> <p>NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 3: https://nsidc.org/data/G02202/versions/3</p> </li> </ul>
The Natural History Museum's collection of Dalbergia, Pterocarpus and the Phaseolinae subtribe
<p>In 2018 the Natural History Museum (NHMUK, herbarium code: BM) undertook a pilot digitisation project together with the Royal Botanic Gardens Kew (project Lead) and the Royal Botanic Garden Edinburgh to collectively digitise non-type herbarium material of the subtribe <em>Phaseolinae</em> and the genera <em>Dalbergia </em>L.f. and <em>Pterocarpus</em> Jacq. (rosewoods and padauk), all from the economically important family of legumes (<em>Leguminosae</em> or <em>Fabaceae</em>). </p> <p>These taxonomic groups were chosen for two case studies using the herbarium collections to support the aims of the UK’s Department for Environment Food & Rural Affairs (DEFRA)-allocated, Official Development Assistance (ODA) funding: study 1 - to support the development of dry beans as a sustainable and resilient crop; study 2 - to aid conservation and sustainable use of rosewoods and padauk.</p> <p>We present the images and metadata for 11,222 NHMUK specimens. This includes label transcription and georeferencing, along with summary data on geographic, taxonomic, collector and temporal coverage. We also provide timings and the methodology for our transcription and georeferencing protocols. Approximately 35% of specimens digitised were collected in ODA-listed countries, in tropical Africa, but also in south east Asia and South America.</p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 2
<p>These figures are an integral part of Chapter 2 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 1
<p>These figures are an integral part of Chapter 1 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 3
<p>These figures are an integral part of Chapter 3 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 6
<p>These figures are an integral part of Chapter 6 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </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.