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3,655 results for “Structural data”
Local Earthquake Tomography Code and Data for study the Lithosphere Structure in the Collision Zone of the NW Himalayas
<p>The tomography model presented in the paper "Lithosphere Structure in the Collision Zone of the NW Himalayas Revealed by Local Earthquake Tomography" are obtained using the LOTOS code by Koulakov (2009). Here, we present the full version of the code with initial data and parameters used for calculating P and S velocity models beneath the NW Himalaya. This version of the code is adopted for the Windows OS and contains the entire program listing and the full project structure for Microsoft Visual Studio 2010 and Intel Visual Fortran. Detailed description of the code can be found at <a href="http://www.ivan-art.com/science/LOTOS">www.ivan-art.com/science/LOTOS</a></p>
Data for "Training data composition affects performance of protein structure analysis algorithms" by A. Derry, K. A. Carpenter, & R. B. Altman
<p><strong>Description</strong></p> <p>This repository contains all data used in "Training data composition affects performance of protein structure analysis algorithms", published in the Pacific Symposium on Biocomputing 2022 by A. Derry, K. A. Carpenter, & R. B. Altman. </p> <p>The data consists of the following files:</p> <ul> <li>ema_zenodo_data.tar.gz: train, validation, and test splits for Estimation of Model Accuracy task, in LMDB format</li> <li>design_zenodo_data.tar.gz: train, validation, and test splits for Protein Sequence Design task, in JSON format</li> <li>enz_cat_res_zenodo_data.tar.gz: train, validation, and test splits for Catalytic Residue and Enzyme Prediction task, in TF record format</li> </ul> <p>Details on dataset construction can be found in our paper and dataloaders can be found in our <a href="https://github.com/awfderry/ml-structure-bias">Github repo</a>.</p> <p><strong>Reference</strong></p> <p>A. Derry*, K. A. Carpenter*, & R. B. Altman, "Training data composition affects performance of protein structure analysis algorithms", 2021.</p> <p><strong>Dataset References</strong></p> <p>Datasets used were derived from the following works:</p> <p>Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2019). Critical assessment of methods of protein structure prediction (CASP)—Round XIII. In <em>Proteins: Structure, Function and Bioinformatics</em> (Vol. 87, Issue 12, pp. 1011–1020). https://doi.org/10.1002/prot.25823</p> <p>Ingraham, J., Garg, V. K., Barzilay, R., & Jaakkola, T. (2019). <em>Generative Models for Graph-Based Protein Design</em>. https://openreview.net/pdf?id=SJgxrLLKOE</p> <p>Furnham, N., Holliday, G. L., de Beer, T. A. P., Jacobsen, J. O. B., Pearson, W. R., & Thornton, J. M. (2014). The Catalytic Site Atlas 2.0: cataloging catalytic sites and residues identified in enzymes. <em>Nucleic Acids Research</em>, <em>42 </em>(Database issue), D485–D489.</p>
Data from: Dinosaurian survivorship schedules revisited: new insights from an age-structured population model
<p>Little is known on dinosaur population biology due to insufficient information on age-dependent fecundities and mortalities. So far, survivorship curves (hereafter SC) of only six dinosaurs (four tyrannosaurs, one ceratopsian, one hadrosaur) were erected from bone assemblages of aged specimens. They indicate high survival throughout most of their life with presumable higher mortalities after hatching and increasing mortalities towards its end. However, all studies ignored that assemblages must preserve stationary age distributions (i.e., the population's age distribution is stable and its size is constant over time as overall population fecundities match mortalities, hereafter SAD population) to infer a reliable SC for a taxon.</p> <p>To assess SCs of these dinosaurs, I built a simple population model with age-dependent fecundities and survival rates. Its few input parameters are maximum longevity, age at sexual maturation and maximum annual offspring number, on which information exists in these dinosaurs. As bone histological studies and scaling relationships provide estimates on its three parameters, my model is also applicable to other extinct taxa.</p> <p> Modelling suggests that bone assemblages did not preserve SAD populations. SCs determined for SAD populations of <i>Albertosaurus sarcophagus</i>,<i> Gorgosaurus libratus</i>, <i>Dasplatosaurus torosus</i> and <i>Tyrannosaurus rex</i> indicated that low mortalities follow high mortalities early in their life or that mortalities were rather constant throughout their life. In <i>Psittacosaurus lujiatuensis</i> modelling suggests low mortalities throughout most of its life that increase towards its end. The SC of <i>Maiasaura peeblesorum</i> was not questioned by my model as it is unable to capture sigmoidal or other composite SCs.</p>
Code and data for: Emergence of spatially structured populations by area-concentrated search
<p>The idea that populations are spatially structured has become a very powerful concept in ecology, raising interest in many research areas. However, despite dispersal being a core component of the concept, it typically does not consider the movement behavior underlying any dispersal. Using individual-based simulations in continuous space, we investigate the emergence of a spatially structured population in landscapes with spatially heterogeneous resource distribution and with organisms following simple area-concentrated search (ACS); individuals do not, however, perceive or respond to any habitat attributes per se but only to their foraging success. We investigated effects of different resource clustering patterns in landscapes (single large cluster vs. many small clusters) and different resource densities on spatial structure of populations and movement between resource clusters of individuals. As the results, we found that foraging success increased with increasing resource density and decreasing number of resource clusters. In a wide parameter space, the system exhibited attributes of a spatially structured population with individuals concentrated in areas of high resource density, searching within areas of resources, and 'dispersing' in a straight line between resource patches. 'Emigration' was more likely from patches that were small or of low quality (low resource density), but we observed an interaction effect between these two parameters. With the ACS implemented, individuals tended to move deeper into a resource cluster in scenarios with moderate resource density than in scenarios with high resource density. 'Looping' from patches was more likely if patches were large and of high quality. Our simulations demonstrate that spatial structure in populations may emerge if critical resources are heterogeneously distributed and if individuals follow simple movement rules (such as ACS). Neither the perception of habitat nor an explicit decision to emigrate from a patch on the side of acting individuals is necessary for the emergence of spatial structure.</p>
Terrestrial laser scanning data Wytham Woods: individual trees and quantitative structure models (QSMs)
<p>This dataset was used for the analysis of the following publication:<br> <em>Laser scanning reveals potential underestimation of biomass carbon in temperate forest. Calders, K, Verbeeck, V, Burt, A, Origo, N, Nightingale, J, Malhi, Y, Wilkes, P, Raumonen, P, Bunce, R G H and Disney, M. Ecological Solutions and Evidence (accepted)</em></p> <p><strong>Any use of this dataset should cite the paper above </strong>(Creative Commons Attribution 4.0 International Public License).</p> <p>Contact: kim.calders@ugent.be</p> <p> </p> <p>================================================<br> Dataset<br> ================================================</p> <p><strong>General</strong>: <br> TLS data were collected in leaf-off conditions during late November 2015 - January 2016. Windy days were avoided to ensure data quality. We used a RIEGL VZ-400 terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH). The instrument has a beam divergence of 0.35 mrad and operates in the infrared (wavelength 1550 nm) with a range up to 350 m. The pulse repetition rate for each scan was 300 kHz, the minimum range was 0.5 m and the angular sampling resolution was 0.04°. This resulted in 22,500,000 outgoing pulses for a single scan, resulting in a beam diameter of 2.45 cm and beam spacing of 3.5 cm at 50 m (for example). The azimuth angle range was 0-360° and the zenith angle range was 30-130°. Therefore an additional scan was acquired at each scan location with the scanner tilted at 90° from the vertical to complete sampling of the full hemisphere at each location. Scans were done in a larger 6 ha area using an approximate 20 m × 20 m grid, to ensure the best possible data quality within our 1.4 ha study area. Trees which had at least more than half of their stem at tree diameter 1.3 m inside the boundaries of the study area were included</p> <p>[ Note that this dataset contains 876 individual trees, but after applying the boundary conditions, 835 trees within the study area were used in the analysis of the paper >> see TLS_Inventory.ipynb]</p> <p>Full details of the methods to segment individual trees and generate the QSMs can be found in the paper <em>Calders et al. Ecological Solutions and Evidence.</em></p> <p><strong>Tree ID:</strong><br> Tree IDs can have numbers only or numbers + letters. A number only means this was a base with one stem. A number + letter means individual trees (split below 1.3m), that share a common tree base.</p> <p><strong>Datasets:</strong><br> 1) DATA_clouds_txt & DATA_clouds_ply: Individually segmented trees in *txt and *ply format. File naming is [tree_id].*txt or [tree_ply].*tx</p> <p>2) DATA_QSM_opt: optimised QSMs using TreeQSM v2.0 (https://github.com/InverseTampere/TreeQSM). File naming is [tree_id]-[dmin0]-[rcov0]-[nmin0]-[dmin]-[rcov]-[nmin]-[lcyl]-[NoGround]-[iteration].mat </p> <p>3) Raw scan data can be found here: http://dx.doi.org/10.5285/ed9156e1697343e4ad82e83ed550e345</p> <p> </p> <p>================================================<br> Paper analysis<br> ================================================</p> <p>We have provided all scripts (analysis_and_figures) that were used to:</p> <p>1 ) analyse the data (TLS_Inventory.ipynb):<br> ----- Analysis of point clouds and QSMs using TLS_Inventory.py.ipynb > tls_summary.csv (#876 trees)<br> ----- Link with census &1.4ha > trees_summary.csv (#835 trees)</p> <p>2) generate the paper figures:<br> ----- various *.R and *.ipynb scripts in the main folder and /allometriesTLS/</p> <p> </p> <p>================================================<br> Funding<br> ================================================</p> <p>The TLS fieldwork was funded through the Metrology for Earth Observation and Climate project (MetEOC-2), grant number ENV55 within the European Metrology Research Programme (EMRP). The EMRP is jointly funded by the EMRP participating countries within EURAMET and the European Union. Funds for purchase of the UCL RIEGL VZ-400 instrument was provided by the UK NERC National Centre for Earth Observation (NCEO) and UCL Geography. The census of the forest plot was supported by an ERC Advanced Investigator Grant to Yadvinder Malhi (GEM-TRAIT, grant number 321131).</p>
Data for the article "Strain-induced shape anisotropy in antiferromagnetic structures"
<p>Data for the article "Strain-induced shape anisotropy in antiferromagnetic structures" </p> <p>URL: https://link.aps.org/doi/10.1103/PhysRevB.106.094430<br> DOI: 10.1103/PhysRevB.106.094430</p>
Data from: Factors at multiple scales drive parasite community structure
<p>1. Understanding how ecological communities are assembled remains a key goal of ecosystem ecology. Because communities are hierarchical, factors acting at multiple scales can contribute to patterns of community structure. Parasites provide a natural system to explore this idea, as they exist as discrete communities within host individuals, which are themselves part of a community and metacommunity.</p> <p>2. We aimed to understand the relative contribution of multi-scale drivers in parasite community assembly and assess how patterns at one level may mask those occurring at another. Specifically, we wanted to disentangle patterns caused by passive sampling from those determined by ecological drivers, and how these vary with scale.</p> <p>3. We applied a Markov Random Fields model and assessed measures of β-diversity and nestedness for 420 replicate parasite infracommunities (parasite assemblages in host individuals) across two freshwater mussel host species, three sites and two time periods, comparing our results to simulations from four different ecologically relevant null models.</p> <p>4. We showed that β-diversity between sites (explaining 25% of variation in parasite distribution) and host species (41%) is greater than expected, and β-diversity between individual hosts is smaller than expected, even after accounting for parasite prevalence and characteristics of host individuals. Further, parasite communities were significantly less nested than expected once parasite prevalence and host characteristics were both accounted for, but more nested than expected otherwise, suggesting a degree of modularity at the within-host level that is masked if underlying host and parasite characteristics are not taken into account. The Markov Random Fields model provided evidence for possible competitive within-host parasite interactions, providing a mechanism for the observed infracommunity modularity.</p> <p>5. An integrative approach that examines factors at multiple scales is necessary to understand the composition of ecological communities. Further, patterns at one level can alter the interpretation of ecologically important drivers at another if variation at higher scales is not accounted for. </p>
Data for: Similar environmental cues guide timing of breeding and seasonal shifts in songbird social structure
<p>Seasonally breeding animals often exhibit different social structures during non-breeding and breeding periods that coincide with seasonal environmental variation. Therefore, ongoing climate change may play an important role in determining the future structure of animal societies, especially if climate determines when seasonal shifts in social structure occur. However, we know little about the environmental cues that determine the timing of seasonal shifts in social structure, a lack of knowledge that contrasts with our well-defined knowledge of the environmental cues that trigger a shift to breeding physiology in seasonally breeding species. Here we tested whether the environmental cues that drive seasonal shifts in social structure are similar to those that determine timing of breeding in the red-backed fairywren (<em>Malurus melanocephalus</em>), an Australian songbird. Social network analyses revealed that social groups, which are highly territorial during the breeding season, interact in social "communities" on larger ranges during the non-breeding season. Interactions among non-breeding groups were related to rainfall, with more rainfall leading to reductions in home range size and fewer interactions among non-breeding social groups. Similarly, onset of breeding was also determined by rainfall during the non-breeding season, with greater rainfall leading to earlier breeding. These findings reveal that for some species, the cues that determine the timing of shifts in social structure across seasonal boundaries can be similar to those that determine timing of breeding. This study increases our understanding of how social structure and the selection pressures that result from different social structures might respond to changing climates.</p>
Data set for "The interfacial structure of InP(100) in contact with HCl and H2SO4 studied by reflection anisotropy spectroscopy"
<p>This is the experimental raw data set associated with the following publication: M. Löw, M. Guidat, J. Kim, and MM May, <em>The interfacial structure of InP(100) in contact with HCl and H<sub>2</sub>SO<sub>4</sub> studied by reflection anisotropy spectroscopy</em>, RSC Advances 12 (2022), 32756-32764. <a href="https://doi.org/10.1039/D2RA05159A">DOI:10.1039/D2RA05159A</a></p> <p>The data set is organised along the figures of the publication. '.ers', '.erc', and '.ert' are spectra, colour plots, and transients, respectively, in the native format of Laytec's EpiRAS. '.par' files are in the data format from the Princeton Applied Research VersaSTAT 3F potentiostat. Spectra files containing the name 'Si100' are from Si(100) wafers with native oxide, used for zero-line correction as described in the paper.</p>
Data for: Richness, food webs structures and ecosystem functioning
<p>This dataset has information on the spatial distribution of fish species from the middle Paraná River and the diet information of 15 fish-eating species. The objective of the paper was to evaluate the relationship between richness, the structure of food webs and standing biomass. In addition to the information on diet and spatial distribution, there is a readme file, two Rstudio scripts and an R environment (all the results of the work can be found in the latter).</p>
Magnetotelluric data from Santos basin (SE Brazil) and inversion resistivity models exploring basin wedge and deep crustal structure beneath.
<p><strong>Magnetotelluric data</strong></p> <p>Processed data from 90 magnetotelluric broadband stations acquired in are available in Electrical Data Interchange (EDI) and ModEM format.</p> <p>The MMT data were recorded in 2007 by WesternGeco Electromagnetics as part of the National Observatory Rio de Janeiro project funded by Petrobras. The campaign comprised a total of 92 sites from shallow water (about 50 m depth) to deep water (about 1600 m depth). The stations are placed along three NW-SE parallel profiles in the northwest part of Santos basin. The central profile is approximately 160 km long and consists of 56 stations, while the west profile and east profile extend about 55 km each and contain 18 and 16 stations, respectively.</p> <p> </p> <p><strong>Models</strong></p> <p>Inversion models and predicted data are present for two different starting resistivity model testes 10 and 1 Ohm.m. The inversion models were estimated using ModEM - modular system for inversion of electromagnetic geophysical data.</p>
Data from "Mapping Protoplanetary Disk Vertical Structure with CO Isotopologue Line Emission"
<p>CO isotopologue line emission image cubes ("[DISK]_[LINE]_cube.fits"), line+continuum image cubes ("[DISK]_[LINE]_cube_wcont.fits"), and zeroth moment maps ("[DISK]_[LINE]_M0.fits") associated with Law et al., 2023, "Mapping Protoplanetary Disk Vertical Structure with CO Isotopologue Line Emission," The Astrophysical Journal</p> <p>The raw data are available on the ALMA archive (see Table 2 in the paper for a listing of the relevant ALMA project codes).</p>
Data from: Plant Community Structure in Tropical Rain Forest Fragments of the Western Ghats, India
<p><strong>DESCRIPTION</strong></p><p>This dataset includes vegetation plot data on trees, lianas, understorey plants, and regeneration, and related data and species name matching files in five rainforest sites collected in 2003 as part of the following study:</p><p>MUTHURAMKUMAR, S., AYYAPPAN, N., PARTHASARATHY, N., MUDAPPA, D., RAMAN, T. R. S., SELWYN, M. A. & PRAGASAN, L. A. 2006. <a href="http://doi.org/10.1111/j.1744-7429.2006.00118.x">Plant community structure in tropical rain forest fragments of the Western Ghats, India</a>. <i>Biotropica</i> 38: 143–160. DOI: 10.1111/j.1744-7429.2006.00118.x</p><p>The regeneration data were analysed and presented in the following publication and related dataset:</p><p>OSURI, A. M., CHAKRAVARTHY, D., MUDAPPA, D., RAMAN, T. R. S., AYYAPPAN, N., MUTHURAMKUMAR, S. & PARTHASARATHY, N. 2017. Successional status, seed dispersal mode and overstorey species influence tree regeneration in tropical rain-forest fragments in Western Ghats, India. <i>Journal of Tropical Ecology</i> 33(4): 270-284. DOI: <a href="http://doi.org/10.1017/S0266467417000219">10.1017/S0266467417000219</a></p><p>OSURI, A. M., CHAKRAVARTHY, D., MUDAPPA, D., RAMAN, T. R. S., AYYAPPAN, N., MUTHURAMKUMAR, S. & PARTHASARATHY, N. 2017. <a href="http://doi.org/10.5061/dryad.vd0nn">Data from: Successional status, seed dispersal mode and overstorey species influence tree regeneration in tropical rain-forest fragments in Western Ghats, India</a>, Dryad, Dataset, https://doi.org/10.5061/dryad.vd0nn</p><p><br><strong>CONTACTS</strong></p><p>CONTACT #1<br>1. Name: <a href="https://orcid.org/0000-0002-1347-3953">T. R. Shankar Raman</a><br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: trsr@ncf-india.org<br>5. ORCID: https://orcid.org/0000-0002-1347-3953</p><p>CONTACT #2<br>1. Name: <a href="https://orcid.org/0000-0001-9708-4826">Divya Mudappa</a><br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: divya@ncf-india.org<br>5. ORCID: https://orcid.org/0000-0001-9708-4826</p><p>CONTACT #3<br>1. Name: <a href="https://orcid.org/0000-0001-9909-5633">Anand M. Osuri</a><br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: aosuri@ncf-india.org<br>5. ORCID: https://orcid.org/0000-0001-9909-5633</p><p>CONTACT #4<br>1. Name: <a href="https://orcid.org/0000-0003-4383-557X">N. Ayyappan</a><br>2. Work Address: French Institute of Pondicherry, No. 11, Post Box No. 33, Saint Louis Street, Pondicherry – 605 001, India.<br>3. Work Phone: + 91- 413-2231616<br>4. Email address: ayyappan.n@ifpindia.org<br>5. ORCID: https://orcid.org/0000-0003-4383-557X</p><p>CONTACT #5<br>1. Name: <a href="https://orcid.org/0000-0002-7791-8499">S. Muthuramkumar</a><br>2. Work Address: V.H.N.S.N. College, 3/151-1, College Road, Virudhunagar - 626001, Tamil Nadu, India.<br>3. Work Phone: + 91-4562-280154<br>4. Email address: muthuramkumar@vhnsnc.edu.in<br>5. ORCID: https://orcid.org/0000-0002-7791-8499</p><p>CONTACT #6<br>1. Name: <a href="https://orcid.org/0000-0002-4172-5441">N. Parthasarathy</a><br>2. Work Address: Department of Ecology and Environmental Sciences, Pondicherry University, R Venkat Raman Nagar, Kalapet, Pondicherry 605014, India<br>3. Work Phone: + 91-413-2654326<br>4. Email address: parthapu@yahoo.com<br>5. ORCID: https://orcid.org/0000-0002-4172-5441</p><p><br><strong>KEYWORDS</strong></p><p>Anamalai hills; biodiversity hotspot; disturbance; endemics; fragmentation; lianas; plant conservation; tree diversity; tropical rain forest; understory plants.</p><p><br><strong>GEOGRAPHIC COVERAGE</strong></p><p>1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India</p><p>2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p><p><br><strong>TEMPORAL COVERAGE</strong></p><p>1. Begins: 2003-03-01 (Year, Month, Day)<br>2. Ends: 2003-04-30 (Year, Month, Day)</p><p><br><strong>METHODS</strong></p><p>Methods involved systematic vegetation plots for trees, lianans and understorey plants as described in Muthuramkumar et al. 2006 (<i>Biotropica</i> 38: 143–160. DOI: 10.1111/j.1744-7429.2006.00118.x) and for tree and woody regeneration as described in Osuri et al. 2017 (<i>Journal of Tropical Ecology</i> 33(4): 270-284. DOI: 10.1017/S0266467417000219). The vegetation sampling methods are briefly described below.</p><p>The present study was conducted in five tropical wet evergreen forest fragments located on the Valparai plateau (Fig. 1): Akkamalai (AK, 2600 ha), Upper Manamboli (UM, 100 ha), Lower Manamboli (LM, 100 ha), Tata Finlay (TF, 32 ha), and Injipara (IP, 18 ha).</p><p>In each site, vegetation was sampled in randomly placed noncontiguous plots of 20 × 20 m located at least 50 m apart and at least 20 m into the fragment interior from the edges, major trails, or roads. We sampled 20 plots each in IP, TF, and LM, and 25 plots each in UM and AK. Within each plot, all trees ≥30cm girth at breast height (gbh, at 1.3 m; corresponding to DBH of 9.55 cm) and lianas ≥1 cm diameter at breast height (DBH) were identified to species, counted, and their girth/diameter measured. For multi-stemmed trees bole girths were measured separately, basal area calculated and summed. Each 20 x 20 m plot was divided into four 10 × 10 m quarters.</p><p>For understory plants, 2 × 2 m quadrats were laid at the four corners of the 20 × 20 m plot (one in each of the corresponding four quarters) and all shrubs, undershrubs, herbs, ferns, and small twiners found within the quadrats were enumerated and identified. The regeneration sampling was done in a 5 × 5-m plot (0.0025 ha) placed at the outer corner of the first (south-west) quarter of the 20 x 20 m plot. Within each regeneration plot, we identified, counted and measured all tree saplings >1 cm diameter at breast height (dbh, at 1.3 m) and <9.55 cm dbh (equivalent to <30 cm girth at breast height, gbh). Woody shrubs of 1–9.55 cm dbh were alsorecorded in the regeneration plots (but these were excluded in the Osuri et al. 2017 analysis).</p><p>For vegetatively propagating plants a clump of stems that is basally connected was considered as one individual. Canopy height was measured with a range finder and canopy closure was measured using a spherical densiometer. Vouchers were identified with regional flora and confirmed with the Western Ghats collections available in the herbarium of Salim Ali School of Ecology, Pondicherry University, from our previous works in the region.</p><p> </p><p><strong>ACKNOLWEDGEMENTS</strong></p><p>Funders and other supporters of the research are acknowledged in the original publications. The compilation and publication of this dataset was carried out as part of an NCF project supported by Fondation Franklinia.</p><p><br><strong>FILES INCLUDED</strong></p><p>Besides the 00_README.txt file that contains this metadata, the dataset includes the following 11 files, whose details and contents are explained below.</p><p><br><strong>01_all_sites.csv</strong></p><p><i>Description</i>: The file contains details of the five study sites (three continuous forest and two forest fragment sites).<br> <br><i>Note</i>: Current Name of TF (Tata Finlay) site is Old Valparai, current name of Akkamalai (AK) is Iyerpadi-Akkamalai complex. Sites and codes correspond to the Muthuramkumar et al. 2006 paper (https://doi.org/10.1111/j.1744-7429.2006.00118.x).</p><p><i>Column names and descriptions:</i><br>eventDate: Date range when sampling was carried out in the sites<br>old_sitename: Name of the site as used in the Muthuramkumar et al. (2006) paper (https://doi.org/10.1111/j.1744-7429.2006.00118.x)<br>sitecode: Site code as used in the Muthuramkumar et al. (2006) paper (https://doi.org/10.1111/j.1744-7429.2006.00118.x)<br>site: Site name as at present and used in this dataset<br>decimalLatitude: latitude in decimal degrees North<br>decimalLongitude: longitude in decimal degrees East<br>geodeticDatum: Geodetic Datum WGS 84<br>coordinateUncertaintyInMeters: Uncertainty in metres of the GPS location (as only one location available for entire site where points were distributed)<br>type: Indicates whether site was continuous rainforest or rainforest fragment<br>Area_ha: Area in hectares<br>Altitude_min_m: Minimum altitude in metres of sampled plots<br>Altitude_max_m: Maximum altitude in metres of sampled plots<br>Ownership: Whether site is in privately owned land or within state-protected reserve<br>Average_canopy_height_m: average canopy height in metres<br>Canopy_closure_%: estimated canopy closure in percentage<br>Nearby_plantations: Adjoining plantations</p><p><br><strong>02_all_trees_adult_data.csv</strong></p><p><i>Description</i>: The file contains records of all adult trees >= 30 cm girth at breast height of 1.3 m (gbh) recorded within 20 m x 20 m plots across three continuous forests and two forest fragments.</p><p><i>Note</i>: Same as in the Osuri et al. (2017) dataset (https://doi.org/10.5061/dryad.vd0nn), with <i>Tithonia diversifolia</i> added back in Injipara and data from one additional site (Manamboli Lower) added back from the original dataset corresponding to the Muthuramkumar et al. 2006 paper (https://doi.org/10.1111/j.1744-7429.2006.00118.x).<br> <br><i>Column names and descriptions:</i><br>x: Row index<br>site: Name of forest site<br>plot_no: An unique plot number assigned to each 20m x 20m adult tree plot within each site<br>q_no: An unique number assigned to each of four 10m x 10m quarters within each adult plot<br>t_no: An unique number assigned to each individual tree within each site.<br>old_code: Species codes used at the time of data collection (refer to Appendix A of the main paper for full species names, and the 06_all_species_names.csv file with this dataset)<br>osuri_code: Revised species codes used in the Osuri et al. 2017 paper in <i>Journal of Tropical Ecology</i> 33: 270-284 (https://doi.org/10.1017/S0266467417000219) and related dataset (https://doi.org/10.5061/dryad.vd0nn)<br>current_code: Species codes used at present<br>gbh_1 to gbh_16: Girth at breast height of single- (gbh_1) and multi-stemmed (gbh_2 – gbh_16) individuals, measured in centimetres (cm)<br>P_ID: Unique plot ID created by combining columns site and plot_no</p><p><br><strong>03_all_liana_data.csv</strong></p><p><i>Description</i>: The file contains records of all lianas >= 1 cm diameter at breast height of 1.3 m (dbh) recorded within 20 m x 20 m plots across three continuous forests and two forest fragments.</p><p><i>Note</i>: Lianas were not included in the Osuri et al. (2017) dataset (https://doi.org/10.5061/dryad.vd0nn).</p><p><i>Column names and descriptions:</i><br>x: Row index<br>site: Name of forest site<br>plot_no: An unique plot number assigned to each 20m x 20m adult tree plot within each site<br>q_no: An unique number assigned to each of four 10m x 10m quarters within each adult plot<br>t_no: An unique number assigned to each individual tree within each site.<br>old_code: Species codes used at the time of data collection (refer to Appendix A of the main paper for full species names, and the 06_all_species_names.csv file with this dataset)<br>osuri_code: Indicated as NA since these data were not used in the Osuri et al. 2017 paper in <i>Journal of Tropical Ecology</i> 33: 270-284 (https://doi.org/10.1017/S0266467417000219) and related dataset (https://doi.org/10.5061/dryad.vd0nn)<br>current_code: Species codes used at present<br>dbh_1 to dbh_11: Diameter at breast height of single- (dbh_1) and multi-stemmed (dbh_2 – dbh_11) individuals, measured in centimetres (cm) <br>P_ID: Unique plot ID created by combining columns site and plot_no</p><p><br><strong>04_all_herbs_data.csv</strong></p><p><i>Description</i>: The file contains records of all understorey plants (shrubs, undershrubs, herbs, ferns, and small twiners) recorded in 2 m × 2 m quadrats laid at the four corners of each 20 m × 20 m plot in three continuous forests and two forest fragments.</p><p><i>Note</i>: Understorey plants were not included in the Osuri et al. (2017) dataset (https: //doi.org/10.5061/dryad.vd0nn).</p><p><i>Column names and descriptions:</i><br>x: Row index<br>site: Name of forest site<br>plot_no: An unique plot number assigned to each 20m x 20m plot within each site<br>corner_no: An unique number assigned to each of four 2 m x 2 m quadrat laid at the four corners of the 20 m x 20 m plot<br>t_no: A number assigned to each individual species recorded within the corner plot.<br>old_code: Species codes used at the time of data collection (refer to Appendix A of the main paper for full species names, and the 06_all_species_names.csv file with this dataset)<br>osuri_code: Indicated as NA since these data were not used in the Osuri et al. 2017 paper in <i>Journal of Tropical Ecology</i> 33: 270-284 (https://doi.org/10.1017/S0266467417000219) and related dataset (https://doi.org/10.5061/dryad.vd0nn)<br>current_code: Species codes used at present<br>count: Number of individuals counted (for vegetatively propagating plants a clump of stems that was basally connected was considered as one individual)<br>P_ID: Unique plot ID created by combining columns site and plot_no</p><p><br><strong>05_all_regeneration_data.csv</strong></p><p><i>Description</i>: The file contains records of woody seedlings and saplings (1-5 cm diameter at breast height at 1.3 m, dbh) and larger-stemmed trees (>5 cm dbh) recorded within single 5 m x 5 m regeneration plots nested within 20 m x 20 m plots. Plots were located in three continuous forests and two forest fragments. Data were filtered during analysis in Osuri et al. (2017, <i>Journal of Tropical Ecology</i>) to retain only seedling and saplings, defined as individuals with effective diameter <=5 cm.</p><p><i>Note</i>: Same as in the Osuri et al. (2017) dataset, with <i>Tithonia diversifolia</i> added back in Injipara from original dataset; and data from one additional site (Manamboli Lower) added back from the Muthuramkumar et al. 2006 dataset.<br> <br><i>Column names and descriptions:</i><br>x: Row index<br>site: Name of forest site<br>plot_no: An unique plot number assigned to each 20m x 20m adult tree plot within each site<br>q_no: The 5 m x 5 m plot was placed in the SW corner of the 20 m x 20 m plot in this q_no which indicates one of the four 10 m x 10 m quarters of the 20 m x 20 m plot, where each quarter was given a unique number in each site<br>t_no: An unique number assigned to each individual seedling, sapling or tree within each site.<br>old_code: Species codes used at the time of data collection (for full species names refer to 06_all_species_names.csv file with this dataset)<br>osuri_code: Revised species codes used in the Osuri et al. 2017 paper in <i>Journal of Tropical Ecology </i>33: 270-284 (https://doi.org/10.1017/S0266467417000219) and related dataset (https://doi.org/10.5061/dryad.vd0nn)<br>current_code: Species codes used at present<br>dbh_1 to dbh_12: Diameter at breast height of single- (dbh_1) and multi-stemmed (dbh_2 – dbh_12) individuals, measured in centimetres (cm) <br>eff_dbh: Effective diameter at breast height (cm)- calculated as ((dbh)^2 +(dbh_1)^2 +...+(dbh_12)^2)^(1/2),<br>P_ID: Unique plot ID created by combining columns site and plot_no</p><p><br><strong>06_all_canopy_readings.csv</strong></p><p><i>Description</i>: The file contains canopy-related measurements taken in each 20 m × 20 m plot in three continuous forests and two forest fragments.</p><p><i>Note</i>: Units of light meter reading were not recorded</p><p><i>Column names and descriptions:</i><br>site: Name of forest site<br>plot_no: An unique plot number assigned to each 20m x 20m adult tree plot within each site<br>reading: A number assigned to the 1 to 4 readings taken in each plot<br>light: Light measurement taken with a light meter in the plot<br>canopy_openness: Canopy openness (scored from 0-100%) using a spherical densiometer (Canopy cover = 100 - canopy openness)<br>P_ID: Unique plot ID created by combining columns site and plot_no</p><p><br><strong>07_all_extracanopy_trees_data.csv</strong></p><p><i>Description</i>: The file contains records of additional trees outside the 5 x 5 m plot whose canopy was overhead of the plot.</p><p><i>Note</i>: Species codes are used to denote presence (not count of stems) of that species in the overhead canopy.</p><p><i>Column names and descriptions:</i><br>site: Name of forest site<br>plot_no: An unique plot number assigned to each 20m x 20m adult tree plot within each site<br>q_no: The 5 m x 5 m plot was placed in the SW corner of the 20 m x 20 m plot in this q_no which indicates one of the four 10 m x 10 m quarters of the 20 m x 20 m plot, where each quarter was given a unique number in each site<br>old_code: Species codes used at the time of data collection (for full species names refer to 06_all_species_names.csv file with this dataset)<br>osuri_code: Revised species codes used in the Osuri et al. 2017 paper in J<i>ournal of Tropical Ecology </i>33: 270-284 (https://doi.org/10.1017/S0266467417000219) and related dataset (https://doi.org/10.5061/dryad.vd0nn)<br>current_code: Species codes used at present<br>P_ID: Unique plot ID created by combining columns site and plot_no</p><p><br><strong>08_all_species_names.csv</strong></p><p><i>Description</i>: This file provides species codes and species scientific names as originally used in the Muthuramkumar et al. 2006 paper (https://doi.org/10.1111/j.1744-7429.2006.00118.x), and as matched with the Global Biodiversity Information Facility (GBIF) species name matching tool</p><p><i>Note</i>: For plots that had no species occurrences (old_code = No herbs, Noliana), NA has been used for other columns</p><p><i>Column names and descriptions:</i><br>group: Code indicating main dataset group where species occurs (tree and regeneration data, liana data, understorey plants data)<br>old_code: Species codes used at the time of data collection (refer to traits data file for full species names)<br>osuri_code: Revised species codes if used in the Osuri et al. 2017 paper in <i>Journal of Tropical Ecology</i> 33: 270-284 (https://doi.org/10.1017/S0266467417000219) and related dataset (https://doi.org/10.5061/dryad.vd0nn) or else indicated as NA<br>current_code: Species codes used at present<br>original_name: Scientific name of plant species as used at the time of the original publication (Muthuramkumar et al. 2006)<br>original_fullname: Scientific name and authorship of plant species as used at the time of the original publication (Muthuramkumar et al. 2006)<br>original_family: Family of the plant species as used at the time of original publication<br>GBIFname: Scientific name as matched by GBIF species name matching tool<br>key: GBIF name matching tool key number<br>matchType: Type of match<br>confidence: Confidence returned by name matching tool<br>status: Whether accepted name or synonym<br>rank: Taxanomic rank (level) to which identified<br>kingdom: Taxonomic Kingdom<br>phylum: Taxonomic Phylum<br>class: Taxonomic Class<br>order: Taxonomic Order<br>family: Taxonomic Family<br>genus: Taxonomic Genus<br>species: Taxonomic Species<br>canonicalName: Canonical part of scientific name matched by GBIF<br>authorship: Authorship of scientific name matched by GBIF<br>scientificName: Current scientific name (from species, genus, or family columns)</p><p><br><strong>09_tabula_Biotropica_appendix1.csv</strong></p><p><i>Description</i>: This file contains tabled values extracted from Appendix 1 of Muthuramkumar et al. 2006 paper in Biotropica (DOI: 10.1111/j.1744-7429.2006.00118.x); extraction from PDF carried out using Tabula software (https://tabula.technology/)</p><p><i>Note</i>: Last 5 columns contain total abundance (count of individuals/stems) in the corresponding site.</p><p><i>Column names and descriptions:</i><br>slno: serial number<br>habit: Plant habit indicating trees, lianas, or understorey plants<br>species: species name as used in Appendix 1 of Muthuramkumar et al. 2006<br>asterisk: species endemic to Western Ghats are indicated by an asterisk (∗ ), and invasive species by double asterisk (∗∗ ).<br>voucher_number: voucher number of herbarium specimen deposited in the herbarium of Salim Ali School of Ecology, Pondicherry University, India.<br>family: plant family as in Appendix 1<br>Iyerpadi-Akkamalai: abundance (total number of individuals counted) in this site<br>Manamboli_Upper: abundance (total number of individuals counted) in this site<br>Manamboli_Lower: abundance (total number of individuals counted) in this site<br>Old_Valparai: abundance (total number of individuals counted) in this site<br>Injipara: abundance (total number of individuals counted) in this site</p><p><br><strong>10_Muthuramkumar et al 2006_Abstract and Appendix 1_extract_Biotropica.pdf</strong></p><p>Extracted PDF of first page with Abstract and Appendix 1 of Muthuramkumar et al. 2006 (DOI: 10.1111/j.1744-7429.2006.00118.x).</p><p><br><strong>11_figure_1_Biotropica_paper.jpg</strong></p><p>JPEG image of Figure 1 (map of study area) from the following publication:<br>MUTHURAMKUMAR, S., AYYAPPAN, N., PARTHASARATHY, N., MUDAPPA, D., RAMAN, T. R. S., SELWYN, M. A. & PRAGASAN, L. A. 2006. Plant community structure in tropical rain forest fragments of the Western Ghats, India. Biotropica 38: 143–160. DOI: 10.1111/j.1744-7429.2006.00118.x</p>
Data and code from: Shifting social-ecological fire regimes explain increasing structure loss from Western wildfires
<p class="MsoNormal"><span>Higuera, P.E., M.C. Cook, J.K. Balch, E.N. Stavros, A.L. Mahood, and L.A. St. Denis. 2023. Shifting social-ecological fire regimes explain increasing structure loss from Western wildfires. PNAS Nexus 2: In Press.</span></p> <p class="MsoNormal"><span>Structure loss is an acute, costly impact of the wildfire crisis in the western United States ("West"), motivating the need to understand recent trends and causes. We document a 246% rise in West-wide structure loss from wildfires between 1999–2009 and 2010–2020, driven strongly by events in 2017, 2018, and 2020. Increased structure loss was not due to increased area burned alone. Wildfires became significantly more destructive, with a 160% higher structure loss rate (loss/kha burned) over the past decade. Structure loss was driven primarily by wildfires from unplanned human-related ignitions (e.g. backyard burning, power lines, etc.), which accounted for 76% of all structure loss and resulted in 10 times more structures destroyed per unit area burned compared to lightning-ignited fires. Annual structure loss was well explained by area burned from human-related ignitions, while decadal structure loss was explained by state-level structure abundance in flammable vegetation. Both predictors increased over recent decades and likely interacted with increased fuel aridity to drive structure-loss trends. While states are diverse in patterns and trends, nearly all experienced more burning from human-related ignitions and/or higher structure loss rates, particularly California, Washington, and Oregon. Our findings highlight how fire regimes – characteristics of fire over space and time – are fundamentally social-ecological phenomena. By resolving the diversity of Western fire regimes, our work informs regionally appropriate mitigation and adaptation strategies. With millions of structures with high fire risk, reducing human-related ignitions and rethinking how we build are critical for preventing future wildfire disasters.</span></p>
Data and scripts from "Unsupervised learning for structure detection in plastically deformed crystals"
<p>This documents contains the scripts and dataset used for the paper "Unsupervised learning for structure detection in plastically deformed crystals".</p> <p> </p> <p>More precisely it contains 4 folders :</p> <p><br> DumpForFigures : subfolder containing the atomic positions in .dump format (see lammps documentation) used for the article figures.</p> <p>DumpForTraining : subfolder containing the atomic position in .dump format (see lammps documentation) used for training the autoencoder.</p> <p>ScriptsToDetectStructuresFromDump : subfolder containing the script sused to detect the substructures of the system by combining autoencoder and clustering methods. This folder contains a readme with the details of the contents.</p> <p>ScriptToGenerateDump : subfolder containing the scripts used to generate the atomic data with molecular dynamics. These data are then used to train the autoencoder. This folder contains a readme with the details of the contents.</p> <p>REQUIREMENTS :</p> <p> </p> <p>Lammps</p> <p>Python3 with packages :</p> <p>-numpy</p> <p>-matplotlib</p> <p>-pyscal</p> <p>-sci-kit learn</p> <p>-pytorch</p> <p>-glob</p> <p> </p> <p> </p> <p> </p>
Data for: Historical and contemporary processes drive global phylogenetic structure across geographical scales: Insights from bat communities
<p><strong>Aim</strong>: Patterns of evolutionary relatedness among co-occurring species are driven by scale-dependent contemporary and historical processes. Yet, we still lack a detailed understanding of how these drivers impact the phylogenetic structure of biological communities. Here, we focused on bats – one of the most speciose and vagile groups of mammals – and test the predictions of three general biogeographical hypotheses that are particularly relevant to understanding how paleoclimatic stability, local diversification rates, and geographical scales shaped their present-day phylogenetic community structure.</p> <p><strong>Location</strong>: Worldwide, across restrictive geographical extents: global, east-west hemispheres, biogeographical realms, tectonic plates, biomes, and ecoregions.</p> <p><strong>Time period</strong>: Last Glacial Maximum (~22,000 years ago) to the present.</p> <p><strong>Major taxa studied</strong>: Bats (Chiroptera)</p> <p><strong>Methods</strong>: We estimated bat phylogenetic community structure across restrictive geographical extents and modelled it as a function of paleoclimatic stability, and in situ net diversification rates.</p> <p><strong>Results</strong>: Limiting geographical extents from larger to smaller scales strongly changed the phylogenetic structure of bat communities. The magnitude of these effects is less noticeable in the western hemisphere, where frequent among-realm biota interchange could have been maintained through bats' adaptive traits. Highly phylogenetically related bat communities are generally more common in regions that changed less in climate since the last glacial maximum, supporting the expectation that stable climates allow for increased phylogenetic clustering. Finally, increased in situ net diversification rates are associated with greater phylogenetic clustering in bat communities.</p> <p><strong>Main conclusions</strong>: We show that the worldwide phylogenetic structure of bat assemblages varies as a function of geographical extents, dispersal barriers, paleoclimatic stability and in situ diversification. The integrative framework used in our study, which can be applied to other taxonomic groups, has proven useful to not only explain the evolutionary dynamics of community assembly but could also help tackle questions related to scale dependence in community ecology and biogeography.</p>
Data from: Genomics reveals the role of admixture in the evolution of structure among sperm whale populations within the Mediterranean Sea
<p>In oceanic ecosystems, the nature of barriers to gene flow, and the processes by which populations may become isolated are different from the terrestrial environment, and less well understood. In this study, we investigate a highly mobile species (the sperm whale, <em>Physeter macrocephalus</em>) that is genetically differentiated between an open North Atlantic population and the populations in the Mediterranean Sea. We apply high-resolution single nucleotide polymorphisms (SNP) analysis to study the nature of barriers to gene flow in this system, comparing gene flow across the putative boundary into the Mediterranean (Strait of Gibraltar and Alboran Sea region) with novel analyses on structuring among sperm whale populations within the Mediterranean basin. Our data support a recent founding of the Mediterranean, around the time of the last glacial maximum, and shows concerted historical demographic profiles in both the Atlantic and the Mediterranean. In each region, there is evidence for a population decline around the time of the founder event, more extreme within the Mediterranean Sea where effective population size is substantially lower. While differentiation is strongest at the Atlantic/Mediterranean boundary, there is also significant differentiation between the Eastern and Western basins of the Mediterranean Sea. We propose, however, that the mechanisms are different. While post-founding gene flow was reduced between the Mediterranean and Atlantic populations, within the Mediterranean an important factor differentiating the basins is likely a greater degree of admixture between the Western basin and the North Atlantic.</p>
Data for: Amazonian birds in more dynamic habitats have less population genetic structure and higher gene flow
<p>Understanding the factors that govern variation in genetic structure across species is key to the study of speciation and population genetics. Genetic structure has been linked to several aspects of life history, such as foraging strategy, habitat association, migration distance, and dispersal ability, all of which might influence dispersal and gene flow. Comparative studies of population genetic data from species with differing life histories provide opportunities to tease apart the role of dispersal in shaping gene flow and population genetic structure. Here, we examine population genetic data from sets of bird species specialized on a series of Amazonian habitat types hypothesized to filter for species with dramatically different dispersal abilities: stable upland forest, dynamic floodplain forest, and highly dynamic riverine islands. Using genome-wide markers, we show that habitat type has a significant effect on population genetic structure, with species in upland forest, floodplain forest, and riverine islands exhibiting progressively lower levels of structure. Although morphological traits used as proxies for individual-level dispersal ability did not explain this pattern, population genetic measures of gene flow are elevated in species from more dynamic riverine habitats. Our results suggest that the habitat in which a species occurs drives the degree of population genetic structuring via its impact on long-term fluctuations in levels of gene flow, with species in highly dynamic habitats having particularly elevated gene flow. These differences in genetic variation across taxa specialized in distinct habitats may lead to disparate responses to environmental change or habitat-specific diversification dynamics over evolutionary time scales.</p>
Supplementary data for: "Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence"
<p>Uploaded on 20. February 2023</p> <p>This is the supplementary data for the publication</p> <p>"Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence" (2023) by Edwin Hirtreiter, Lukas Schulze Balhorn, Artur M. Schweidtmann</p> <p>Corresponding author: A. M. Schweidtmann, E-mail: a.schweidtmann@tudelft.nl<br> Delft University of Technology, Department of Chemical Engineering, Process Intelligence Group, Van der Maasweg 9, 2629 HZ Delft, The Netherlands</p> <p>The folder contains json files with the training (train), test (test), and augmented training (train_augm) data files. The json files contain syntetically generated SFILES. </p> <p>The pre-print of the manuscript is accessible at https://doi.org/10.48550/arXiv.2211.05583</p>
Data from: Temperature-dependent mechanical behavior of aluminum AM structures generated via multi-layer friction surfacing
<p>This dataset contains the data for the publication " Temperature-dependent mechanical behavior of aluminum AM structures generated via multi-layer friction surfacing "</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.