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Potential forest conservation value rasters for Denmark from Assmann et al. "LiDAR data fusion and machine learning identify temperate forests of high conservation value"
<p>Potential forest conservation value (high / low) rasters for Denmark based on a remote sensing data fusion approach. Please see manuscript (below) for a detailed description of the methods and data products. </p> <p><br>Jakob J. Assmann, Pil B. M. Pedersen, Jesper E. Moeslund, Cornelius Senf, Urs A. Treier, Derek Corcoran, Zsófia Koma, Thomas Nord-Larsen, Signe Normand. In prep. LiDAR data fusion and machine learning identify temperate forests of high conservation value.</p> <p><br>When using the data, please cite the above manuscript. </p> <p><br>Files description:</p> <ul> <li>Compressed and cloud optimised rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:3857 <ul> <li>forest_quality_ranger_biowide_10m_cog_epsg3857.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m_cog_epsg3857.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m_cog_epsg3857.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m_cog_epsg3857.tif GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul> <p> </p> <ul> <li>Aggregated rasters of potential forest conservation value projections for Denmark (100 m res.) in EPSG:25832 <ul> <li>forest_quality_ranger_biowide_100m.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_100m.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_100m.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_100m.tif GBM model projections based on SustainScapes stratification </li> </ul> </li> </ul> <p> </p> <ul> <li>Uncompressed and tiled rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:25832<br>Please note: the archives contain approx. 42k tiles, each 10 x 10 km, as well as a VRT file for covenient loading. <ul> <li>forest_quality_ranger_biowide_10m.zip RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m.zip RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m.zip GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m.zip GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul>
NICHE Flanders: reference values for the (a)biotic requirements of vegetation types in Flanders, Belgium
<p>This dataset contains site requirements/tolerance limits (or "reference values") for 28 vegetation types found in Flanders. It gives the lower and upper limits or the classes within which these vegetation types can occur, for 7 site factors that determine potential vegetation development. These reference values can be used to determine the potential distribution of the different vegetation types with the ecohydrological model NICHE Flanders (<a href="https://purews.inbo.be/ws/portalfiles/portal/5370206/Callebaut_etal_2007_NicheVlaanderen.pdf">Callebaut et al. 2007</a>, in Dutch).</p> <p>See the Technical info (available in English and Dutch) for more information.</p>
Dataset for "Fertilizer value of dairy processing waste materials and contributions of soil microorganisms towards phosphorus uptake in grasses."
<p>This file includes the dataset used for the analysis of the fertilizer value from dairy processing waste materials and the contribution of soil microorganisms towards P uptake. More information in the linked future publication</p>
Experimental CCS Values in PubChem
<p>The collection of experimental collision cross section (CCS) values from ion mobility experiments in PubChem, retrieved via the code developed <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/-/tree/master/annotations/CCS/CCS_retrieval">here</a>.</p> <ul> <li> "All_CCS_in_PubChem.csv" contains all CCS values extracted from PubChem (previous versions contained entries with no CIDs, current version has all entries mapped to CIDs).</li> <li>"All_CCS_in_PubChem_wInfo.csv" contains all CCS values, plus post-processing (splitting annotations, adducts and comments, adding chemical identifiers), but excludes all entries with no CID (and thus no structural information - not applicable in current version). </li> </ul> <p>Details how this file was produced are given <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/-/tree/master/annotations/CCS/CCS_retrieval">here</a>.</p>
Global MODIS-based snow cover monthly long-term (2000-2012) at 500 m, and aggregated monthly values (2000-2020) at 1 km
<p>The Global monthly snow cover repository contains multiple products (based on the MODIS/Terra MOD10A2):</p> <ol> <li>Global snow cover monthly long-term (2000–2012) P90 and standard deviation derived from the <a href="http://maps.elie.ucl.ac.be/CCI/viewer/index.php">ESA CCI snow cover weekly product</a>;</li> <li>Global snow cover monthly values P05, P50 and P95 for the period 2000–2020 derived using <a href="https://climate.esa.int/en/odp/#/project/snow">ESA snow cover fraction daily 1-km values</a>;</li> <li>Min and max geometric temperatures for the mid-month (dtm_temp.max_geom.*_m_1km_s0..0cm_xxxx_epsg4326_v1.tif);</li> </ol> <p>Quantiles (probability either 0.05, 0.5, 0.9 and/or 0.95) have been derived by matching dates in the filenames (daily or weekly values). After deriving quantiles, gaps were filled using temporal neighbors (e.g. missing values for year 2002 were filled using average of values between year 2001 and 2003). The gaps were especially large for months of November, December, January and February, northern Hemisphere. Important note: maps still contain some artifacts due to high reflections of white-sands e.g. Salar de Uyuni desert in Bolivia and similar. Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/snow.cover"><strong>here</strong></a>. Antarctica is not included.</p> <p>To access and visualize global datasets use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> </ul> <p>All files provided as Cloud-Optimized GeoTIFFs / internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>clm = theme: climate,</li> <li>snow.cover = variable: snow cover fractions,</li> <li>esa.modis = data source ESA snow product,</li> <li>p.90 = upper 90% quantile,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000..2012 = time reference aggregated: from 2000 to 2012,</li> <li>v1 = version number: 1,</li> </ul>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in the summary for policymakers
<p>These figures are an integral part of the Summary for policymakers 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 - Tables presented in Chapter 4
<p>These tables are an integral part of Chapter 4 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 - Tables presented in Chapter 3
<p>These tables 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 - Tables presented in Chapter 6
<p>These tables 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>
IPBES Assessment of the diverse values and valuation of nature - Tables presented in Chapter 5
<p>These tables are an integral part of Chapter 5 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>
Value chains under the framework of life cycle assessment indicators
<p>Tables included in the article "Monitoring the bioeconomy: value chains under the framework of life cycle assessment indicators"</p>
Forest condition anomaly index values covering Germany for 2016-2023
<p><strong>General description:</strong><br>In <a href="https://doi.org/10.1016/j.rse.2024.114323" target="_blank" rel="noopener">Lange et. al (2024)</a> we utilised <em>Sentinel-2</em> tree species-specific reflectance time series for extracting forest condition across Germany from 2016 to 2022. These time series' seasonal evolution - computed separately for seven natural regions - serves as reference when calculating a similarity metric – further called <em>forest condition anomaly index</em> (FCA). The FCA is computed between each single reflectance observation and the respective date within the reference time series, also considering the natural temporal deviations caused by phenology. FCA temporal aggregation allowed generating spatially comprehensive forest condition anomaly maps. FCA patterns in space and time are in line with dominant drivers like fires, storms and insect infestations and in agreement with state-of-the-art forest disturbance products using a threshold of FCA = −0.15 for forest loss. More information can be found in the <a href="https://doi.org/10.1016/j.rse.2024.114323" target="_blank" rel="noopener">related publication</a> and in the <a title="UFZ Forest condition monitor" href="https://web.app.ufz.de/forestconditionmonitor" target="_blank" rel="noopener">UFZ Forest condition monitor web-application</a>.</p> <p><br><strong>Data description:<br></strong>Data is provided in GeoTiff format (projection <a href="https://epsg.io/32632" target="_blank" rel="noopener">EPSG:32632</a>). Forest condition anomaly maps are available in a spatial resolution of 20 <em>m</em> for the years 2016 to 2023 as monthly (May to October), seasonal (spring, summer and fall) and yearly maps. Values are scaled by 10 000 to reduce the file size. Final FCA values are obtained by dividing the raw values by 10 000 and range from -1 to 1. A negative value generally indicates a poorer forest condition, for example, due to negative changes in chlorophyll or water content or due to crown defoliation. Through validation using forest surveys, data from the <em>Copernicus Emergency Management System</em> and other current maps of forest cover loss, it can be relatively accurate determined that a value below -0.15 indicates a heavily damaged or dead forest stand. Stronger damage (such as significant needle/leaf loss or tree mortality) is generally captured more precise than light damage (such as slight needle/leaf loss). Moderate forest condition values correspondingly show no anomaly and represent the expected normal condition for the respective tree species at the given time within the year. Positive forest condition values indicate a positive deviation from the expected state, which might stem from from positive chlorophyll or water content changes or from denser foliage or needle cover.</p> <p> </p> <p><strong>File descriptions</strong>: <br>Data is provided in zip archives containing maps in GeoTiff format (projection <a href="https://epsg.io/32632" target="_blank" rel="noopener">EPSG:32632</a>). 4 zip files are provided:</p> <ul> <li><em>FCA_v0007-0005_Germany_2016-2023_yearly_R20m.zip</em> contains 8 yearly FCA maps </li> <li><em>FCA_v0007-0005_Germany_2016-2023_seasonal_R20m.zip </em>contains 24 seasonal FCA maps (spring, summer and fall for 2016 to 2023)</li> <li><em>FCA_v0007-0005_Germany_2016-2019_monthly_R20m.zip</em> contains 24 monthly maps (May to October for 2016 to 2019)</li> <li><em>FCA_v0007-0005_Germany_2020-2023_monthly_R20m.zip</em> contains 24 monthly maps (May to October for 2020 to 2023)</li> </ul> <p> </p> <p><strong>Please note:</strong><br>Forest pixels were selected according to the tree species map from <a href="https://doi.org/10.1016/j.rse.2024.114069" target="_blank" rel="noopener">Blickensdörfer et al. (2024)</a>. </p>
IPI values of heart beats
<p>InterPuls Interval (IPI) is the time difference between two R-R peak of the heartbeat based on mili-second. Current dataset belongs to 4223 subjects and the IPI values are extracted from ECG datasets provided by PhysioNet. The main file IPI-All.txt contains the value of IPIs and each line represents a subject. In the rest of the files <em>n</em>bit.txt.7z, <em>n </em>is the number of bits in converting the IPI values to binary. For instance, if n=2, then after converting the IPI value to binary, only the least two significant bits were inserted in the 2bit.txt.7z. </p>
Table of associated Legendre functions of the first kind for values on the real axis
<p>This data set is a tabulation of associated Legendre functions of the first, sometimes called the regular solutions, computed for values on the real axis ranging from 0.0 to 10.0.</p> <p>It was created by a new program which has been implemented in C++ using template meta programming, to compute associated Legendre functions of the first and second kind of integer order (l) and degree (m) and complex argument (z). The mathematical equations, along with a short table of values, are given the book (see page 118 and following) </p> <p> Shanjie Zhang and Jianming Jin, Computation of Special Functions,<br> publishers: Wiley, 1996, ISBN: 0-471-11963-6,<br> LC: QA351.C45.</p> <p>The output from this new program has been verified against the tables printed in the book.</p> <p>This data set is provided because</p> <p> i) it covers a wider range of arguments than published in the tables in the book </p> <p> ii) it has many more l,m values than the tables in the book</p> <p>Note that this data set was computed using data type long double on an Intel CPU. </p>
Table of associated Legendre functions of the second kind for values on the real axis
<p>This dataset is a tabulation of associated Legendre functions of the second, sometimes called the irregular solutions, computed for values on the real axis ranging from 0.0 to 10.0.</p> <p>It was created by a new program which has been implemented in C++ using template meta programming, to compute associated Legendre functions of the first and second kind of integer order (l) and degree (m) and complex argument (z). The mathematical equations, along with a short table of values, are given the book (see page 118 and following) </p> <p> Shanjie Zhang and Jianming Jin, Computation of Special Functions,<br> publishers: Wiley, 1996, ISBN: 0-471-11963-6,<br> LC: QA351.C45.</p> <p>The output from this new program has been verified against the tables printed in the book.</p> <p>This dataset is provided because</p> <p> i) it covers a wider range of arguments than published in the tables in the book </p> <p> ii) it has many more l,m values than the tables in the book</p> <p>Note that this data set was computed using data type long double on an Intel CPU. </p> <p> </p>
A new method for approximating fractional derivatives/ integrals as a series of higher-integer-order derivatives - examples and results of applying the method to initial/boundary value problems
<p>The posted research data includes examples of the application of the author's fractional derivative/integral approximation method using the sum of higher integer derivatives. The attached text files contain the numerical solutions of the presented examples, recorded as a set of numerical values obtained from the performed computations.</p> <ul> <li>Example 4.1 <br> \(\begin{cases}<br> \displaystyle<br> ^{C}D^{\alpha}_{a+}\sin (x), \\<br> x \in \langle a, 3\pi \rangle \quad \hbox{and} \quad<br> \alpha = \{1.0,\ 0.8,\ 0.6,\ 0.4,\ 0.2\},<br> \end{cases}\)<br><br></li> <li>Example 4.2 <br>\( \begin{cases}<br> \displaystyle<br> I^{\alpha}_{0+} e^{-x}\cos 7x, \\<br> x \in \langle 0,1\rangle \quad \hbox{and} \quad<br> \alpha =\{1.0,\ 1.2,\ 1.4,\ 1.6,\ 1.8,\ 2.0 \},<br> \end{cases} \)<br><br></li> <li>Example 5.1 <br>\(\begin{cases}<br> ^{C} D_{0+}y(x)+2y(x)=x+ \frac{2x^{\alpha+1}}{\Gamma(\alpha+2)},\\<br> x\in\langle0,1\rangle, \\<br> y(0) = 0; \quad y(1) = \frac{1}{\Gamma(\alpha+2)}, \\<br> \alpha = \{1.2,\ 1.4,\ 1.6,\ 1.8,\ 2.0\}. <br>\end{cases}\)<br><br></li> <li>Example 5.2 <br>\(\begin{cases}<br> ^{C}D_{0+}^{\alpha}y(x)+1.8 y(x)=0,\\<br> x\in \langle 0,2\rangle \quad \hbox{and} \quad \alpha=\{1.0,\ 0.8,\ 0.6,\ 0.4,\ 0.2\},\\<br> y(0)=1.<br>\end{cases}\)</li> </ul>
Autochamber CH4 Fluxes and δ13C Values at Stordalen Mire
<p>Autochamber-based CH<sub>4</sub> fluxes and δ<sup>13</sup>C values measured with a Tunable Infrared Laser Direct Absorption Spectrometer (TILDAS, Aerodyne Research Inc.); and ancillary data, including CO<sub>2</sub> fluxes (measured with a LGR Greenhouse Gas Analyzer), temperatures, atmospheric pressure, and photosynthetically active radiation (PAR).</p> <p>In addition to the data published here, data from 2011 is also available in the supplementary files to <a href="https://doi.org/10.1038/nature13798"><strong>McCalley et al. (2014)</strong></a> under the <strong><a href="https://static-content.springer.com/esm/art%3A10.1038%2Fnature13798/MediaObjects/41586_2014_BFnature13798_MOESM61_ESM.xlsx">Source data to Fig. 1</a></strong> link.</p> <p> </p> <p>METHODS:</p> <p>Methane fluxes were measured using a system of 8 automatic gas-sampling chambers made of transparent Lexan (n=3 each in the palsa and bog habitats, and n=2 in the fen habitat). Chambers were initially installed in the three habitat types at Stordalen Mire in 2001 (Bäckstrand et al., 2008) and the chamber lids were replaced in 2011 with the current design, similar to that described by Bubier et al 2003. Chambers cover an area of 0.2 m<sup>2</sup> (45 cm x 45 cm), with a height ranging from 15-75 cm depending on habitat vegetation. At the Palsa and bog site the chamber base is flush with the ground and the chamber lid (15 cm in height) lifts clear of the base between closures. At the fen site the chamber base is raised 50–60 cm on lexon skirts to accommodate large stature vegetation. The chambers are instrumented with thermocouples measuring air and surface ground temperature, and water table depth and thaw depth are measured manually 3–5 times per week. The chambers are connected to the gas analysis system, located in an adjacent temperature-controlled cabin, by 3/8” Dekoron tubing through which air is circulated at approximately 2.5 L min<sup>-1</sup>. Each chamber lid is closed once every 3 hours for a period of 8 min, with a 5 min flush period before and after lid closure.</p> <p>We measured methane concentration using a Tunable Infrared Laser Direct Absorption Spectrometers (TILDAS, Aerodyne Research Inc.) connected to the main chamber circulation using ¼” Dekoron tubing (McCalley et al 2014). Calibrations were done every 90 min using 3 calibration gases spanning the observed concentration range (1.8–10 ppm). For each autochamber closure we calculated flux using a method consistent with that detailed by Bäckstrand et al 2008 for CO<sub>2</sub> and total hydrocarbons, using a linear regression of changing headspace CH<sub>4</sub> concentration over a period of 2.5 min. Eight 2.5 min regressions were calculated, staggered by 15 sec, and the most linear fit (highest r<sup>2</sup>) was then used to calculate flux. Daily average flux for each chamber was used to calculate daily flux and standard error for each cover type.</p> <p><em>References:</em></p> <p>Bäckstrand, K., Crill, P. M., Mastepanov, M., Christensen, T. R. & Bastviken, D. Total hydrocarbon flux dynamics at a subarctic mire in northern Sweden. <em>Journal of Geophysical Research</em> <strong>113</strong>, (2008).</p> <p>Bubier, J. L., Crill, P. M., Mosedale, A., Frolking, S. & Linder, E. Peatland responses to varying interannual moisture conditions as measured by automatic CO<sub>2</sub> chambers. <em>Global Biogeochemical Cycles</em> <strong>17</strong>, (2003).</p> <p>McCalley, C.K., B.J. Woodcroft, S.B. Hodgkins, R.A. Wehr, E-H. Kim, R. Mondav, P.M. Crill, J.P. Chanton, V.I. Rich, G.W. Tyson, S.R. Saleska (2014), Methane dynamics regulated by microbial community response to permafrost thaw, <em>Nature</em>, 514:478-481, doi:10.1038/nature13798.</p> <p> </p> <p>FILES:</p> <p>Files are named with the year or date range, followed by a suffix indicating data resolution:</p> <ul> <li>*<strong>_CH4output_clean_ckm.txt</strong> - Individual measurements of CH<sub>4</sub> fluxes (CH4Flux), CO<sub>2</sub> fluxes (CO2flux; for select years), and δ<sup>13</sup>C signature of emitted CH<sub>4</sub> (Flux13CH4) for each chamber closure. CH4FluxRsq is the R<sup>2</sup> value of the linear fit used to calculate CH<sub>4</sub> flux, CO2Rsq is the R<sup>2</sup> value of the linear fit used to calculate CO<sub>2</sub> flux, and Flux13CH4_stdev is the standard deviation of the δ<sup>13</sup>C signature (standard deviation of the intercept of the Keeling plot).</li> <li>*<strong>_DailyCH4output_ckm.txt</strong> - Daily average CH<sub>4</sub> fluxes (CH4Flux) and δ<sup>13</sup>C values (13CH4), grouped by site: Palsa, Bog, Fen, and Chamber 9 (bog/fen transition); along with standard deviations (stdev) and standard errors (se) of the flux or δ<sup>13</sup>C for each site type. For the Palsa, Bog, and Fen sites, these averages are calculated by chamber (n=3 for Palsa and Bog, n=2 for Fen), so each chamber's daily average is calculated, and then a daily average for that site is calculated as the average of the chambers. For Chamber 9 (bog/fen intermediate; n=1 chamber), averages are calculated by day as there are no chamber replicates.</li> </ul> <p>MEASUREMENT UNITS (same for both file types):</p> <ul> <li>CH<sub>4</sub> flux: mg CH<sub>4</sub> m<sup>−2</sup> hr<sup>−1</sup></li> <li>CO<sub>2</sub> flux: mg C m<sup>−2</sup> h<sup>−1</sup></li> <li>δ<sup>13</sup>C: ‰</li> <li>Temperature: °C</li> <li>Air pressure: mbar</li> <li>PAR: µmol photons m<sup>−2</sup> s<sup>−1</sup></li> </ul> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br>This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.<br>Autochamber measurements between 2013 and 2017 were supported by a grant from the US National Science Foundation MacroSystems program (NSF EF 1241037, PI Varner).</p>
From waste to value: Recovering critical raw materials from urban mines in the European Union and the United States
<p><strong>Submitted data was used to write an article: </strong>Jędrusiak, R., Bielowicz, B., Drobniak, A., 2023, From waste to value: Recovering critical raw materials from urban mines in the European Union and the United States, Mineral Resource Management 39 (3), 43-63. <a href="https://doi.org/10.24425/gsm.2023.147557">https://doi.org/10.24425/gsm.2023.147557</a></p> <p> </p> <p><strong>Funding acknowledgments: </strong>Agnieszka Drobniak contribution comes from the support of the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), and the National Science Center, Poland (2022/01/1/ST10/00024). This research was funded by the Ministry of Science and Higher Education of Poland (subsidies no. 16.16.140.315).</p> <p> </p> <p><strong>Article Abstract: </strong>Modern human consumption, rapid urbanization and further increases in the world’s population lead to the demand for more goods and materials. However, after utilization, only some of these materials are recovered or recycled, many are discarded due to a lack of implemented recovery technologies and regulations, or due to the content of contaminants. Moreover, many of the potentially recoverable materials are deposited in landfills or shipped to less developed countries for disposal where they can cause environmental contamination. The new approach to waste management follows the hierarchy of waste prevention. First, waste is prepared for reuse and repair without the need for treatment processes, or it is recycled. If this is not possible, the waste is incinerated with energy recovery, or failing that, it is disposed of in landfills. This waste hierarchy has become one of the key factors in the transformation of a linear economy into a circular economy. Particularly noteworthy is waste containing raw materials of significant economic importance, especially those of a high supply risk due to the level of concentration in another country and import dependence. These critical raw materials (CRM) are an inherent part of our modern, technology-driven life. They are essential to national security and the economic development of every country. Their use is drastically increasing, and with it, the need to assure their reliable and unrestricted access along with lowering the environmental impact from their production and extraction. Currently, scientists and industry direct a lot of effort into finding new supplies of these materials, not only from traditional sources in nature but also from new sources like anthropogenic waste. The purpose of this study is to present the raw material potential which remains mostly unused in residues from municipal waste incineration in regions with highly developed economies – the United States and the European Union. These economies have shortages of their own raw material extraction capacity due to high levels of consumption and insufficient amounts of raw-material content in natural resources.</p>
Values, beliefs, norms, and circular citizenship behaviours in a student sample
<p>Data set of 229 students from an online survey, run between March 7th 2024 and April 2nd 2024.</p> <p><strong>Gender</strong>: 43 men (18.8%), 181 women (79.0%), 3 non-binary persons (1.2%), 2 participants (0.9%) preferred not to indicate their gender.</p> <p><strong>Age</strong>: The age ranged from 17 to 29 years (M = 19.7, SD = 1.83).</p> <p><strong>Variables</strong>: Biospheric, altruistic, hedonic, and egoistic values, problem awareness, ascription of causal responsibility, self-efficacy, outcome efficacy, personal norms, circular citizenship behaviours</p> <p><strong>Data analysis </strong>by creating scales for the variables and running regressions/GLM.</p>
Data for 'The value of shifting cultivation for biodiversity in Northeast India'
Shifting cultivation is a widespread land-use in many tropical countries that also harbours significant levels of biodiversity. Increasing frequency of cultivation cycles and expansion into old-growth forests have intensified the impacts of shifting cultivation on biodiversity and carbon sequestration. We assessed how bird diversity responds to shifting cultivation and the potential for co-benefits for both biodiversity and carbon in such landscapes to inform carbon-based payments for ecosystem service (PES) schemes. We conducted this study in Nagaland, Northeast India. We surveyed above-ground carbon stocks and bird communities across various stages of a shifting cultivation system and old-growth forest using composite carbon sampling plots and repeated point counts directly overlaying the carbon plots in both summer and winter. We assessed species diversity using species accumulation and rarefaction curves based on Hill numbers. We fitted a linear mixed-effect model to assess the relationship between species richness and fallow age. We also examined possible co-benefits between carbon and biodiversity from fallow regeneration in terms of relative community similarity to old-growth forest across carbons stocks. Farmland and secondary forests regenerating on fallowed land had similar bird species richness to old-growth forests in summer and relatively higher species richness in winter. Within regenerating fallows, we did not find any strong evidence that fallow age influenced bird species richness. Bird community resemblance to old-growth forest increased with secondary forest maturity, correlating also with carbon stocks in summer. However, bird community assemblage did not show a strong association with habitat types and carbon stocks during winter. This study underscores the important role of traditional non-intensive shifting cultivation in providing refuges for biodiversity within heterogeneous habitat mosaics. Effectively managing these landscapes is crucial f
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.