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1,542 results for “Degradation”
Lattice Boltzmann simulation of liquid water transport in gas diffusion layers of proton exchange membrane fuel cells: Impact of gas diffusion layer and microporous layer degradation on effective transport properties
<p><span>Underlying data to publication Sarkezi-Selsky et al., <em>J. Pow. Sour.</em> 556 (2023) 232415,<span> https://doi.org/10.1016/j.jpowsour.2022.232415</span> <br><br>Polymer Electrolyte Membrane Fuel Cells (PEMFCs) represent a promising technology for clean drivetrain solutions, in particular for heavy-duty applications. However, lifetime requirements demand high durability of each cell component.<br></span><span>In this work, transport of liquid water through pristine and degraded gas diffusion layers (GDL) was simulated with a 3D Color-Gradient Lattice Boltzmann model. The GDL microstructure was reconstructed </span><span>from high-resolution X-ray micro-computed tomography (</span><span>μ</span><span>-CT) of an impregnated Freudenberg H14. The </span><span>effect of a microporous layer (MPL) was considered by reconstruction of an impregnated and MPL-coated H14. Aged microstructures were generated artificially, assuming loss of polytetrafluoroethylene (PTFE) within the GDL and increase of MPL macroporosity as main degradation mechanisms. Liquid water transport within aged microstructures was simulated by imposing a liquid phase flow rate until breakthrough was reached. Subsequently, the GDL microstructures were analyzed for their breakthrough characteristics by means of saturation and effective gas transport properties. When the MPL was pristine, no distinct GDL degradation effect was observable, this was attributed to the MPL dominating capillary transport. MPL aging, however, led to increased saturations and thus to a deterioration of the effective gas transport. With a partially degraded MPL, aging of the GDL then appeared to affect the breakthrough characteristics.</span></p>
Figure 6. (a1), (a2), (a3), (a4), (a5), (a6), (a7) and (a8) watermarked image is degraded respectively through JPEG2000 compression, JPEG compression, median filtering, adding Salt&Pepper noise, rotating, center cropping, surrounding cropping and scaling. (b1), (b2), (b3), (b4), (b5), (b6), (b7) and (b8) The corresponding extracted watermarks.-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>This paper has described a scheme for digital watermarking of still images based on discrete<br> wavelet transform. In the proposed method, the embedded logo watermark can be extracted without<br> access to the original image. It has been confirmed that the proposed watermarking method is able<br> to extract the embedded logo watermark from the watermarked images that have degraded through<br> compression, filtering, cropping and scaling. Although this algorithm is not robust against rotation,<br> it can completely extract the watermark from watermarked images that lose about 35% of their<br> areas by cropping attack.</p>
Quantitative susceptibility mapping of articular cartilage: ex vivo findings at multiple orientations and following different degradation treatments
<p>This dataset contains all the raw source data and MATLAB analysis functions that comprise the study:</p> <p><br> Quantitative susceptibility mapping of articular cartilage: Ex vivo findings at multiple orientations and following different degradation treatments</p> <p>Magnetic Resonance in Medicine | DOI: 10.1002/mrm.27216</p> <p>Nykänen Olli(1*), Rieppo Lassi(2,3), Töyräs Juha(1,4), Kolehmainen Ville(1), Saarakkala Simo(2,3,5), Shmueli Karin(6) and Nissi Mikko Johannes(1)</p> <p>(1) Department of Applied Physics, University of Eastern Finland, POB 1627, FI-70211 Kuopio, Finland<br> (2) Research Unit of Medical Imaging, Physics and Technology, University of Oulu, POB 5000, FI-90014 Oulu, Finland<br> (3) Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland<br> (4) Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland<br> (5) Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland<br> (6) Department of Medical Physics & Biomedical Engineering, University College London(UCL), London, United Kingdom</p> <p><br> *Corresponding author:<br> Olli Nykänen<br> Department of Applied Physics,<br> University of Eastern Finland<br> POB 1627<br> FI-70211, Kuopio, Finland<br> olli.nykanen@uef.fi<br> +358-50-5556357</p> <p><br> Keywords: cartilage, collagen matrix, quantitative susceptibility mapping, MRI, osteoarthritis</p> <p><br> Included folders and files are:<br> - article_figures: all figures published in the manuscript<br> - data: all MRI, histological, and PLM data used in this article<br> - matlab_functions: matlab functions used in data analysis with subfolders:<br> - aedes_plugins: plugins for aedes (http://aedes.uef.fi) for calculation of QS- and T2* maps<br> - fitting_functions: functions for fitting relaxation times or TKD-method for QSM, used by the functions in above folder<br> - miscellaneous_functions: small helper functions for a number of small tasks utilized by the other scripts and functions<br> - ReadMe.txt: this file</p> <p><br> Notes for setting up Aedes correctly for this dataset:<br> Run Aedes -> Tools -> Edit VNMR Defaults:<br> - Return: FT + K-space<br> - DC: off<br> - Zeropadding: off<br> - Sorting & fastread: on<br> - Precision: single<br> - Read_fcn: readvnmr<br> - Orient: no</p> <p>See more info in separate readme files included in each folder.</p> <p><br> (Olli Nykänen, Apr 17, 2018)</p>
Summary for policymakers of the assessment report on land degradation and restoration of the Intergovernmental SciencePolicy Platform on Biodiversity and Ecosystem Services: Figure SPM.1
<p>The purpose of Figure SPM.1 is to support the statement that land degradation occurs just about everywhere in the world (i.e. it is ‘pervasive’), takes many forms, and that examples of successful restoration are also widespread. The figure consists of a backdrop map of the world from a multiple land degradation perspective, showing the level of uncertainty between studies, overlaid with dots representing all the places specifically mentioned in the eight chapters of the main Assessment Report on Land Degradation and Restoration, including case studies of both degradation and restoration. Around the map are brief notes regarding the main forms of degradation encountered.</p>
Impacts of rainforest degradation on the diets of the insectivorous bats of Sabah
<b>Description: </b><p>The work was carried out within Sabah, at the SAFE project, Danum Valley and Maliau basin. Bats were captured by deploying 6 harp traps per night, during field seasons taking place in 2015, 2016 and 2017. Bat guano samples were collected by placing individual bats into cloth bags, and then releasing them after 12 hours. Any guano in the bottom of the bag was then transferred into 95% ethanol and stored at -20. DNA was extracted from the faecal samples using a Qiagen Stool Mini kit, and then amplified using the ZBJ-ArtF1c ZBJ-ArtR2c primers, and sequencing the DNA on an Illumina MiSeq.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/182"><b>Impacts of rainforest degradation on the diets of the insectivorous bats of Sabah</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Standard grant, NE/K016407/1)</li><li>Royal Society (Standard grant, RG130793)</li><li>Bat Conservation International (Standard grant)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.4 (46))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3247465">here</a></p><p><b>Files: </b>This dataset consists of 3 files: Bat_SAFE_data_metadata.xlsx, interaction_network.csv, sequences_95.fasta</p><p><b>Bat_SAFE_data_metadata.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>data</b> (described in worksheet Data)</p><p>Description: measurements collected</p><p>Number of fields: 21</p><p>Number of data rows: 3292</p><p>Fields: </p><ul><li><b>TrapName</b>: The trap ID which the bats were captured in (Field type: ID)</li><li><b>Lat</b>: Latitude of trap (Field type: Latitude)</li><li><b>Long</b>: Longitude of trap (Field type: Longitude)</li><li><b>Elevation</b>: Elevation of trap (Field type: Numeric)</li><li><b>Bat_no</b>: Bat ID (Field type: ID)</li><li><b>Date</b>: Date of capture (Field type: Date)</li><li><b>Faeces_no1</b>: Tube number used to store faecal sample. Pairs up with column names of interaction matrix (Field type: ID)</li><li><b>Faeces_no2</b>: Number of any additional faeces (Field type: ID)</li><li><b>Biopsy_Dave</b>: Tube used to store wing biopsy (Field type: ID)</li><li><b>Block</b>: If sampling occurred within the SAFE landscape, this is the block it occurred within (Field type: ID)</li><li><b>Fragment</b>: If sampling occurred within the SAFE landscape, this is the fragment size it occurred within (Field type: ID)</li><li><b>Site</b>: The site within Sabah sampling occurred at (Field type: ID)</li><li><b>Species</b>: The bat species ID (Field type: Taxa)</li><li><b>Sex</b>: Male or Female (Field type: Categorical trait)</li><li><b>Age</b>: Was the bat an adult or juvenile (Field type: Categorical trait)</li><li><b>Forearm</b>: The forearm length of the bat (Field type: Numeric trait)</li><li><b>Weight</b>: The weight of the bat (Field type: Numeric Trait)</li><li><b>Reproductive_condition</b>: If a female bat, if the bat was Non-Reproductive, PRegnant, LActating or Post-Lactating (Field type: Categorical trait)</li><li><b>Parasite</b>: Tube used to store any ectoparasites obtained (Field type: ID)</li><li><b>Time</b>: If the bat was captured in evening or morning (Field type: Categorical)</li><li><b>Tag</b>: Band ID, if used (Field type: ID)</li></ul></li></ol><p><b>interaction_network.csv</b></p><p>Description: A network of operational taxonomic units found within the guano of bats captured in Sabah. The column names refer to the bat guano id, as found in the columns 'Faeces_no1' and 'Faeces_no2' in the fieldwork data, and the rownames refer to the OTU of the prey, which is paired to the names of the OTUs in the fasta file.</p><p><b>sequences_95.fasta</b></p><p>Description: A fasta file of prey OTUs found in bat guano, generated using 95% similarity for clustering. The sequence names correspond with the rownames of the file interaction_network.csv</p><p><b>Date range: </b>2015-02-16 to 2017-07-21</p><p><b>Latitudinal extent: </b>4.5000 to 5.0933</p><p><b>Longitudinal extent: </b>116.7500 to 117.8380</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Chordata<br> -  - Mammalia<br> -  -  - Chiroptera<br> -  -  -  - Emballonuridae<br> -  -  -  -  - <i>Emballonura</i><br> -  -  -  -  -  - <i>Emballonura alecto</i><br> -  -  -  -  -  - <i>Emballonura monticola</i><br> -  -  -  - Hipposideridae<br> -  -  -  -  - <i>Hipposideros</i><br> -  -  -  -  -  - <i>Hipposideros ater</i><br> -  -  -  -  -  - <i>Hipposideros bicolor</i><br> -  -  -  -  -  - <i>Hipposideros cervinus</i><br> -  -  -  -  -  - <i>Hipposideros diadema</i><br> -  -  -  -  -  - <i>Hipposideros doriae</i><br> -  -  -  -  -  - <i>Hipposideros dyacorum</i><br> -  -  -  -  -  - <i>Hipposideros galeritus</i><br> -  -  -  -  -  - <i>Hipposideros ridleyi</i><br> -  -  -  - Megadermatidae<br> -  -  -  -  - <i>Megaderma</i><br> -  -  -  -  -  - <i>Megaderma spasma</i><br> -  -  -  - Nycteridae<br> -  -  -  -  - <i>Nycteris</i><br> -  -  -  -  -  - <i>Nycteris tragata</i><br> -  -  -  - Pteropodidae<br> -  -  -  -  - <i>Balionycteris</i><br> -  -  -  -  -  - <i>Balionycteris maculata</i><br> -  -  -  -  - <i>Macroglossus</i><br> -  -  -  -  -  - <i>Macroglossus minimus</i><br> -  -  -  -  - <i>Megaerops</i><br> -  -  -  -  -  - <i>Megaerops wetmorei</i><br> -  -  -  - Rhinolophidae<br> -  -  -  -  - <i>Rhinolophus</i><br> -  -  -  -  -  - <i>Rhinolophus acuminatus</i><br> -  -  -  -  -  - <i>Rhinolophus affinis</i><br> -  -  -  -  -  - <i>Rhinolophus borneensis</i><br> -  -  -  -  -  - <i>Rhinolophus creaghi</i><br> -  -  -  -  -  - <i>Rhinolophus luctus</i><br> -  -  -  -  -  - <i>Rhinolophus sedulus</i><br> -  -  -  -  -  - <i>Rhinolophus trifoliatus</i><br> -  -  -  - Vespertilionidae<br> -  -  -  -  - <i>Harpiocephalus</i><br> -  -  -  -  -  - <i>Harpiocephalus harpia</i><br> -  -  -  -  - <i>Hesperoptenus</i><br> -  -  -  -  -  - <i>Hesperoptenus blanfordi</i><br> -  -  -  -  - <i>Kerivoula</i><br> -  -  -  -  -  - <i>Kerivoula hardwickii</i><br> -  -  -  -  -  - <i>Kerivoula intermedia</i><br> -  -  -  -  -  - <i>Kerivoula lenis</i><br> -  -  -  -  -  - <i>Kerivoula minuta</i><br> -  -  -  -  -  - <i>Kerivoula papillosa</i><br> -  -  -  -  -  - <i>Kerivoula pellucida</i><br> -  -  -  -  -  - <i>Kerivoula whiteheadi</i><br> -  -  -  -  - <i>Murina</i><br> -  -  -  -  -  - <i>Murina aenea</i><br> -  -  -  -  -  - <i>Murina cyclotis</i><br> -  -  -  -  -  - <i>Murina rozendaali</i><br> -  -  -  -  -  - <i>Murina suilla</i><br> -  -  -  -  - <i>Myotis</i><br> -  -  -  -  -  - <i>Myotis muricola</i><br> -  -  -  -  -  - <i>Myotis ridleyi</i><br> -  -  -  -  - <i>Phoniscus</i><br> -  -  -  -  -  - <i>Phoniscus atrox</i><br> -  -  -  -  - <i>Pipistrellus</i><br> -  -  -  -  -  - <i>Pipistrellus javanicus</i><br> -  -  -  -  -  - <i>Pipistrellus tenuis</i><br> -  -  -  -  - <i>Scotophilus</i><br> -  -  -  -  -  - <i>Scotophilus kuhlii</i><br></div><p></p>
Enrichment and characterisation of a mixed-source ethanologenic community degrading the organic fraction of municipal solid waste under minimal environmental control
<p>The spread sheets in this workbook correspond to the datasets evaluating different inocula sources for ethanol (EtOH) production from the organic fraction of municipal solid waste (OMSW) at initially acid and neutral pH, and initially aerobic and anaerobic conditions.</p>
Figure 5 in Assessing a ReviTec Measure to Combat Soil Degradation by studying Acari and Collembola from Ngaoundéré, Adamawa, Cameroon
Figure 5. Temporal variation of total Acari and Collembola in the ReviTec plots (ctrl1: ReviTec control; cpmy: compost + mycorrhiza; cpbcbo: compost + biochar + bokashi). Details as in Fig. 3.
Figure 3 in Assessing a ReviTec Measure to Combat Soil Degradation by studying Acari and Collembola from Ngaoundéré, Adamawa, Cameroon
Figure 3. Temporal variation of total Acari and Collembola in control plots (sav: savanna; ctrl1: ReviTec control plot; tsd. ind./m2, 0–10 cm). Significant differences between 2017 sampling campains are marked by different letters.(n = 5); Asterisk: Difference to sav significant (n = 5, p <0.05). (Kruskal-Wallis test with subsequent Mann-Whitney U-test to test pairs [p ˂ 0.05]). Error bars: standard error.
Figure 2 in Impacts of anthropogenic activities and habitat degradation on breeding waterbirds
Figure 2. Distribution and observation frequency of urbanization, industrialization, pollution, overgrazing, disturbance, and illegal reed cutting and burning threats in 2002.
Figure 4 in River degradation impacts fish assemblages in Kosovo's Ibër basin
Figure 4. Left: CCA results between physico-chemical variables and species densities. Right: CCA results between anthropogenic pressures and species densities.
Figure 1 in River degradation impacts fish assemblages in Kosovo's Ibër basin
Figure 1. Site locations on the Ibër and its tributaries in northern Kosovo; inset map shows locations on the Ibër's main western stem, upstream of the city of Mitrovica. Minor inset map (upper left) shows the position of Kosovo in the Western Balkans (data about sampling sites presented in Table 2).
Figure 3 in River degradation impacts fish assemblages in Kosovo's Ibër basin
Figure 3. Left: MDS for sampling sites of Ibër river, regarding the physico-chemical variables. Red square: very polluted; Brown triangle: Polluted; Orange triangle: Less polluted. Right: MDS for sampling sites of Ibër river, regarding the anthropogenic pressures as recorded in the on-site protocol assessment.
Figure 2 in River degradation impacts fish assemblages in Kosovo's Ibër basin
Figure 2. Fish specimens photographed in the field aquarium during the survey: A. Squalius cephalus, B. Cobitis elongatoides, C. Phoxinus sp., D. Gobio obtusirostris, E. Rutilus rutilus, F. Salmo cf. trutta, G. Sabanejewia balcanica, H. Chondrostoma nasus, I. Romanogobio uranoscopus, and J. Barbus balcanicus. Photographs by Stamatis Zogaris.
Raw data for Machine learning approach for photocatalysis: An experimentally validated case study of photocatalytic dye degradation
<p>Specification of affiliations:</p> <ul> <li>Hassan Ali - Centre of Polymer Systems</li> <li>Muhammad Yasir - Centre of Polymer Systems</li> <li>Hamza Ul Haq - Laboratory of Alternative Fuel and Sustainability, School of Chemical and Materials Engineering,</li> <li>Ali Can Guler - Centre of Polymer Systems</li> <li>Milan Masar - Centre of Polymer Systems</li> <li>Muhammad Nouman Aslam Khan - Laboratory of Alternative Fuel and Sustainability, School of Chemical and Materials Engineering,</li> <li>Michal Machovsky - Centre of Polymer Systems</li> <li>Vladimir Sedlarik - Centre of Polymer Systems</li> <li>Ivo Kuritka - Centre of Polymer Systems</li> </ul> <p> </p> <p>Raw data for the research paper. Information on the data collection are described in the manuscript. </p>
Supplementary data for manuscript: "Characterizing dynamic heterogeneities during nanogel degradation"
<p>Contains data files and code (python Jupyter notebook) to reconstruct plots for manuscript: "Characterizing dynamic heterogeneities during nanogel degradation"<br><br>Contact: zmira@g.clemson.edu<br><br><br>This work is supported by the National Science Foundation under NSF Award No. 2110309.</p>
Fate of organic solvent-soluble extractives and arabinogalactan during brown rot degradation of Siberian larch heartwood
<p>This dataset contains measurement data from the following publication: Belt, T.; Harju, A.; Venäläinen, M.; Kilpeläinen, P. (2024) Fate of organic solvent-soluble extractives and arabinogalactan during brown rot degradation of Siberian larch heartwood. European Journal of Wood and Wood Products. DOI: 10.1007/s00107-024-02146-3. The data consist of mass loss and extractive content data for samples derived from two different decay tests. The decay tests and measurement procedures are described in brief below; further details can be found in the publication.</p> <p>Decay test 1 was a stacked-sample test. Sample blocks were prepared from fresh Siberian larch heartwood and Scots pine sapwood. The decay test was conducted in test tubes over nutrient agar inoculated with <em>Coniophora puteana</em> or <em>Rhodonia placenta</em>. Each tube (N =5) received 7 heartwood or sapwood blocks stacked on top of each other (sample positions 1-7 from top to bottom). After decay, the mass losses of all larch heartwood and pine sapwood samples were measured. The decayed larch heartwood samples were individually ground to powder and extracted with methanol to obtain organic solvent-soluble extractives and with cold water to obtain arabinogalactan.</p> <p>Decay test 2 was a time-series test. Increment cores were obtained from 5 Siberian larch trees, with 8 cores obtained from each tree. Core pieces were cut from outer heartwood and split lengthwise to produce two halves: one half for the decay test and the other to act as an undecayed extractive content control. The decay test was conducted on petri dishes containing nutrient agar inoculated with <em>C. puteana</em> or <em>R. placenta</em>. One core half from each tree exposed to <em>C. puteana</em> and one exposed to <em>R. placenta</em> were removed from the decay test after 10, 20, 27 and 36 days of incubation. After decay, the mass losses of all decay test core halves were measured. The decay test and control core halves were individually ground to powder and extracted with methanol to obtain organic solvent-soluble extractives and with cold water to obtain arabinogalactan.</p> <p>Organic solvent-soluble extractives in the methanol extracts were analysed by GC-MS after trimethyl silylation of the extracts. A total of 13 extractive compounds were identified and quantified in the extracts: 5 fatty acids (palmitic, linolenic, linoleic, oleic, and stearic acid), 6 resin acids (isopimaric, pimaric, palustric, dehydroabietic, abietic, and neoabietic acid), and 2 flavonoids (dihydrokaempferol and taxifolin). Arabinogalactan in the cold water extract was analysed by GC-FID after acid methanolysis. Arabinogalactan was determined as the sum of arabinose and galactose obtained by methanolysis.</p> <p>The “Decay test 1.csv”-file gives the sample identifiers (ID, sample type, test fungus, tube number, sample position), and mass losses of the larch heartwood and pine sapwood samples, and the extractive contents of the larch heartwood samples. Extractive content data are given for all fatty acids, resin acids, and flavonoids quantified in the methanol extracts, and for arabinose and galactose quantified in the cold water extracts. Extractive contents are given on a decayed wood basis (mg/g decayed wood).</p> <p>The “Decay test 2.csv”-file gives the sample identifiers (ID, sample type, test fungus, tree number, core number, decay test time), mass losses, and extractive contents of the decayed and control larch heartwood core halves. Extractive content data are given for all fatty acids, resin acids, and flavonoids quantified in the methanol extracts, and for arabinose and galactose quantified in the cold water extracts. Extractive contents are given on a decayed wood basis (mg/g decayed wood).</p>
Computational Approach to Discovering Plastic Degradation Enzymes
<p><strong>With at least 150 million tons of plastic already in oceans and over 10 million tons of plastic entering oceans annually, plastic waste has become a major global problem. Current methods to address this problem such as incineration and landfills are unsustainable and environmentally harmful. More trending approaches such as the degradation of plastic using microbial enzymes are rarely efficient enough to be applied industrially. To fill this gap in our knowledge, we developed a computational method called IPDE (Identification of Plastic Degradation Enzymes) to systematically identify promising enzymes, enzyme combinations, and microbial species for effective plastic waste degradation. Using IPDE, we discovered 32 enzymes in ocean microbiomes, with at least 16 (50.0%) having a role in plastic degradation. Additionally, we identified 37 significant enzyme combinations, 8 (21.6%) of which contain enzymes that co-occur in the same metabolic pathways. Furthermore, we found 60 microbial species, 16 (26.6%) of which have been implied to be linked to plastic degradation in literature. The results from IPDE provide promising candidates for experimental validation, protein engineering, and industrial application to tackle this plastic waste problem. The IPDE tool is freely available at https://github.com/SophieL8/Plastic-degrading-enzymes.</strong></p>
Area Estimates of Forest Degradation and Deforestation in the Country of Georgia by Region
<p>Area estimates of forest degradation and deforestation in the country of Georgia by region from 1987 to 2019. Unit is square kilometers.</p> <p>georgia_forest_def_0512.csv: Area estimates of deforestation</p> <p>georgia_forest_deg_0512.csv: Area estimates of forest degradation</p> <p>Please cite the data as: <a href="https://www.sciencedirect.com/science/article/pii/S0034425721003680">Chen, S., Woodcock, C.E., Bullock, E.L., Arévalo, P., Torchinava, P., Peng, S. and Olofsson, P., 2021. Monitoring temperate forest degradation on Google Earth Engine using Landsat time series analysis. Remote Sensing of Environment, 265, p.112648.</a></p> <p><a href="https://authors.elsevier.com/a/1devg7qzStnwW">Click here to get 50-day free access without registration</a></p>
Fig. 1 in Assessing the potential for avifauna recovery in degraded forests in Indonesia
Fig. 1. NMDS ordination biplots of bird species (e.g., sp1) that show a significant difference in their densities between less and highly degraded forest with the habitat variables (text) superimposed. Bird species code: (sp1) yellow-bellied bulbul Alophoixus phaeocephalus; (sp2) hairy-backed bulbul Tricholestes criniger; (sp3) green iora Aegithina viridissima; (sp4) scaly-crowned babbler Malacopteron cinereum; (sp5) chestnut-rumped babbler Stachyris maculata; (sp6) rufous-tailed shama Trichixos pyrrhopygus; (sp7) blue-winged leafbird Chloropsis cochincinensis; (sp8) greater racket-tailed drongo Dicrurus paradiseus; (sp9) short-tailed babbler Malacocincla malaccensis; (sp10) black-capped babbler Pellorneum capistratum; (sp11) blue-eared barbet Psilopogon duvaucelii; (sp12) brown barbet Calorhamphus hayii; (sp13) black-headed bulbul Pycnonotus atriceps; (sp14) spectacled bulbul Pycnonotus erythropthalmos; (sp15) olive-winged bulbul Pycnonotus plumosus; (sp16) cream-vented bulbul Pycnonotus simplex; (sp17) sooty-capped babbler Malacopteron affine; (sp18) fluffybacked tit-babbler Macronous ptilosus; (sp19) purple-naped sunbird Hypogramma hypogrammicum.
Active restoration fosters better recovery of tropical rainforest birds than natural regeneration in degraded forest fragments
<ol> <li>Ecological restoration has emerged as a key strategy for conserving tropical forests and habitat specialists, and monitoring faunal recovery using indicator taxa like birds can help assess restoration success. Few studies have examined, however, whether active restoration achieves better recovery of bird communities than natural regeneration, or how bird recovery relates to habitat affiliations of species in the community.</li> <li>In rainforests restored over the past two decades in a fragmented landscape (Western Ghats, India), we examined whether bird species richness and community composition recovery in 23 actively restored (AR) sites was significantly better than recovery in paired naturally regenerating (NR) sites, relative to 23 undisturbed benchmark (BM) rainforests. We measured 8 habitat variables and tested whether bird recovery tracked habitat recovery, whether rainforest and open-country birds showed contrasting patterns, and assessed species-level responses to restoration.</li> <li>We recorded 92 bird species in 460 point-count surveys. Rainforest bird species richness was highest in BM, intermediate in AR, and lowest in NR. Contrastingly, open-country bird species richness was least in BM, intermediate in AR, and highest in NR.</li> <li>Bird community composition varied significantly across treatment types with composition in AR in transition from NR to BM. Bird community dissimilarity between sites was positively related to dissimilarity in habitat structure and floristics, and geographic distance between sites. Variance partitioning indicated that structural and floristic dissimilarity explained 90% of the variation in community composition.</li> <li>Indicator species analysis revealed significant associations of 34 species with one or more treatment types. Species associated with BM and AR treatment types were all rainforest species, while only 38% of species associated with AR and NR treatment types were rainforest species.</li> <li> <em>Synthesis and applications</em>: We show that active restoration of degraded fragments benefits rainforest birds and reduces the infiltration of open-country birds, and highlight the importance of considering rainforest and open-country species separately. In human-modified tropical rainforest landscapes, active restoration of degraded fragments fosters partial recovery and complements protection of mature forests for bird conservation.</li> </ol>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.