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zenodo48/100

Data release for "Things that might Go bump in the night: Assessing structure in the binary black hole mass spectrum"

Data release accompanying "Things that might go bump in the night: Assessing structure in the binary black hole mass spectrum" Included are: <ul> <li>500 mock catalogs containing 69 events each, in netCDF4 format (can be found in `with_z_evo_lalprior_69_evs_prod_mock_PE.tar.gz`)</li> <li>A corresponding injection set using O3 sensitivity (`with_z_evo_lalprior_69_evs_prod_injections.h5`)</li> <li>Files containing hyperposterior samples resulting from a Power Law + Spline fit to 100 of the 69-event mock catalogs (`PowerLawSpline_69evs_20knots_2t100_*_result.json`)</li> <li>Files containing hyperposterior samples resulting from a smoothed power law fit to 100 of the 69-event mock catalogs (`Truncated_69evs_*_result.json`)</li> </ul> Code using these files to create all plots in the paper can be found at https://git.ligo.org/amanda.farah/bump-significance Code used to create the mock catalogs can be found at https://git.ligo.org/amanda.farah/mock-PE

opencc-by-4.0Dec 2022View details →

zenodo48/100

Data from: Flock size and structure influence reproductive success in four species of flamingo in 540 captive populations worldwide

Summary This dataset accompanies the publication "Flock size and structure influence reproductive success in four species of flamingo in 540 captive populations worldwide" published in Zoo Biology. It contains anonymised data from 540 captive flamingo populations, and includes the four species: Phoeniconaias minor, Phoenicopterus chilensis, Phoenicopterus roseus and Phoenicopterus ruber. Data were sourced from the Zoological Information Management System (ZIMS), operated by Species360 (https://www.species360.org/). ZIMS is the largest real-time database of comprehensive and standardized information spanning more than 1,200 zoological collections globally, and provides the number of institutions currently managing each flamingo species and both their current and historic population sizes. These data were used to investigate the relationship between reproductive success and both flock size, and structure, on a global scale. This dataset also contains climatic data provided by WorldClim, which were used to assess the influence of climatic variables on captive flamingo reproductive success globally. The WorldClim database averages 19 different climatic variables derived from monthly temperature and rainfall values at a 1 km spatial resolution for the period 1970-2000. Using geographic coordinates (latitude and longitude) we calculated several climatic metrics for each institution.    Description of the Dataset One file is provided for each species (P. minor, P. chilensis, P. roseus and P. ruber) as a csv file. Each file contains the following 15 columns: <ul> <li>Institution Code: An anonymous code used to identify individual zoological institutions.       </li> <li>Country: The country where the institution is located.</li> <li>Year: Current year (t).</li> <li>Flock Size: Flock size in year t.</li> <li>Males: The number of males in the flock in year t.           </li> <li>Females: The number of females in the flock in year t.</li> <li>Unsexed: The number of unsexed individuals in the flock in year t.</li> <li>Proportion of Females: The proportion of the flock made up of female individuals in year t.        </li> <li>Proportion of Unsexed: The proportion of the flock made up of unsexed individuals in year t.</li> <li>Hatches: Number of birds hatched in year t.</li> <li>Proportion of Additions: The proportion of the flock in year t made up of additions from year t-1 (not including new birds hatched into the flock).</li> <li>MAP: Mean annual precipitation (mm).</li> <li>MAT: Mean annual temperature (°C).</li> <li>MAP Var: Mean annual variation in precipitation (MAP coefficient of variation).</li> <li>MAT Var: Mean annual variation in temperature (MAT standard deviation).</li> </ul> Note: Mean Annual Temperature (MAT) is provided by WorldClim as °C multiplied by 10, and similarly mean annual variation in temperature as MAT standard deviation multiplied by 100. In the corresponding publication, both were divided (by 10 and 100 respectively) prior to modelling to avoid confusion in the units used.   Acknowledgements We acknowledge and thank all Species360 member institutions for their continued support and data input. The research which data refers to was funded by the Irish Research Council Laureate Awards 2017/2018 IRCLA/2017/60 to Y.M.B. Additionally, S.Q.S. received funding from the International Max Planck Research School for Organismal Biology. The Species360 Conservation Science Alliance would like to thank their sponsors: the World Association of Zoos and Aquariums, Wildlife Reserves of Singapore, and Copenhagen Zoo.    Disclaimer Despite our best efforts at screening the data for errors and inconsistencies, some information could be erroneous. Similarly, data contained within ZIMS are based on submitted records from individual institutions, and are not subject to editorial verification, potentially permitting errors or failure to update species holdings etc. Despite this, ZIMS represents the only global database of zoo collection composition records, and as a result, is used by the IUCN, Convention on International Trade in Endangered Species (CITES), the Wildlife Trade Monitoring Network (TRAFFIC), United States Fish and Wildlife Service (USFWS) and Department for Environment, Food and Rural Affairs (DEFRA).    Credit If you use this dataset, please cite the corresponding publication: Mooney, A., Teare, J. A., Staerk, J.,Smeele, S. Q., Rose, P., Edell, R. H., King, C. E., Conrad, L., & Buckley, Y. M. (2023). Flock size and structure influence reproductive success in four species of flamingo in 540 captive populations worldwide. Zoo Biology, 1–14. <a href="https://doi.org/10.1002/zoo.21753">https://doi.org/10.1002/zoo.21753</a>

opencc-by-4.0Jan 2023View details →

zenodo48/100

Quantitative electronic structure and work-function changes of liquid water induced by solute - data

Data set pertaining to the article "Quantitative electronic structure and work-function changes of liquid water induced by solute" | Physical Chemistry Chemical Physics, 24, 1310 (2022). Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07 using the NXmpes user contributed format suggested by the Fairmat consortium, see https://www.nexusformat.org/ https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html A few extensions specific to liquid jet-experiments were added to the standard, and are explained in the notes-group on the top level of each file. NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format: * nexpy (distributed with python) * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium) In each NeXus file-entry, two types of spectra are shown: 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data'). 2. As-measured data ('raw'). Files with extension .txt are comma-separated ascii-files. The following files are provided: Photoemission data pertaining to solute measurements using the cut-off as energy reference: NaI_data.h5 tbai_data.h5 Biased spectra were typically recorded in the following order: [cut-off (fine), cut-off (coarse), (valence band)*(N repeats)]*(M repeats) To avoid the saving of overly complex hdf5-files, these data were saved in a different order, namely: [cut-off (fine)*(M repeats), cut-off (coarse)*(M repeats), (valence band)*(N*M repeats)]. Numeric representations of the traces shown in the article's figures: Figure_1a-data.txt Figure_1b-data.txt Figure_2a-data.txt Figure_2b-data.txt Figure_2c-data.txt Figure_3-data.txt Figure_4-data.txt Figure_5a-data.txt Figure_5b-data.txt Figure_6a-data.txt Figure_6b-data.txt Figure_6c-data.txt Figure_7_diff_spectra-data.txt Figure_8-data.txt Traces shown in several figures are included only in the data file pertaining to the figure in which they occur first.   Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .

opencc-by-4.0Sep 2021View details →

zenodo48/100

Bibliographic Data from the Computational Methods Applied to Earthen Historical Structures Review

This database contains all the bibliographic information about the 293 records found after applying the Search Strategy used for the Computational Methods Applied to Earthen Historical Structures Review. Such strategy consisted on using relevant keywords grouped into three different search queries within ”TITLE-ABS-KEY”, for the years 2019-2023: <ol> <li>(”earthen heritage” OR ”earthen historical building*” OR ”earthen historical structure*” OR ”earthen architect*” OR ”earthen monument*”).</li> <li>(adobe OR ”rammed earth” OR cob ) AND (”computational method*” OR ”numerical analy*”).</li> <li>(adobe OR ”rammed earth” OR cob ) AND (fem OR dem OR la OR ”finite element” OR ”discrete element” OR ”limit analysis”).</li> </ol> The search was conducted on April 7, 2023.

opencc-by-4.0May 2023View details →

zenodo48/100

Bibliographic Data from the SoTL in Civil and Structural Engineering Systematic Review

This database contains all the bibliographic information found after applying the Search Strategy used for the SoTL in Civil and Structural Engineering Systematic Review. The following electronic databases were searched: <ul> <li>Scopus.</li> <li>Web of Science.</li> <li>OsloMet Library.</li> <li>Google Scholar (no bibliographic information is presented since this database does not allow to download such data).</li> </ul> A total of 84 records were found in Scopus, 43 in Web of Science, and 55 in OsloMet Library. The search was conducted on September 1, 2023. The information is presented in .ris, .bib, and .csv format.

opencc-by-4.0Oct 2023View details →

edi48/100

Periphyton Abundance and Structural Traits, Diatom Taxa Relative Abundance, and Associated Environmental Data from Samples Collected from the Greater Everglades, Florida, USA from September 2005 - ongoing

This data package contains benthic algae (periphyton) and environmental data collected annually during the wet season between 2005 and 2021 from sites distributed throughout the greater Everglades ecosystem. This project is part of the Comprehensive Everglades Restoration Program's Monitoring and Assessment Plan (CERP MAP) intended to document baseline variability in periphyton attributes for assessing the effectiveness of restoration projects. A total of 200 primary sampling units (PSU) of 800 m x 800 m are nested in 32 landscape units (LSU) and each year, random coordinates are 'drawn' within each PSU and one draw is visited in each sampleable PSU. Sampled periphyton is processed for aggregate structural traits (i.e., biomass, chlorophyll-a, organic content, and phosphorus concentration) and for diatom taxa. For diatoms, slides are prepared, and at least 500 frustules are enumerated and identified to the lowest possible taxonomic resolution per slide. Taxon abundances are then relativized to the total count. These data accompany environmental and spatial data for each sampled draw. In addition to the CERP MAP data, this dataset also includes data on the same variables collected from up to 21 primary sampling units in the Broward County Water Preserve Area beginning in 2020. The data in this package replace and supersede those in package knb-lter-fce.1210 (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-fce&identifier=1210).

openCC (other)Sep 2025View details →

edi48/100

Gridded 1-hectare estimates of shrub community structure at the Jornada Basin LTER site derived from NAIP (2011) and LiDAR (2019) data

This dataset contains four raster maps of shrub community structure at the Jornada Basin LTER site in southern New Mexico U.S.A. These shrub structure estimates were created by combining an existing categorical shrub map (Ji et al. 2019) with USGS LiDAR shrub height estimates from 2019. The resulting raster dataset includes four bands of spatially aligned shrub volume, cover, height, and density estimates at one hectare resolution. Data are also included in tabular format, extracted from the 1 hectare grid upon which estimates were created. These shrub structure estimates are intended to facilitate analyses of habitat structure and community dynamics within the northern Chihuahuan Desert.

openCC0Dec 2023View details →

zenodo44/100

Research Data Supporting "Understanding Structural and Electronic Properties of Bismuth Trihalides and Related Compounds"

Research Data Supporting "Understanding Structural and Electronic Properties of Bismuth Trihalides and Related Compounds" DOI: 10.1021/acs.inorgchem.9b03214

openmit-licenseJan 2020View details →

zenodo44/100

Underlying data for "Interpretation of Hydrogen-Deuterium Exchange Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles"

This dataset contains code, data, and figures used in the article "Interpretation of Hydrogen-Deuterium Exchange Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles". Contents: code/* - Underlying code used to analyze molecular dynamics trajectories and calculate predicted HDX-MS data, used to reweight structural ensembles to best fit target HDX-MS data, and used to structurally cluster simulation frames after reweighting data/* - Simulation trajectories of the TeaA protein, along with two sub-trajectories corresponding to only 'closed' or 'open' TeaA frames, and predicted HDX-MS deuterated fractions used as target data in simulation reweighting. Also simulation trajectories of the LeuT protein, in either 'outward-facing' or 'inward-facing' conformational states embedded in a DMPC bilayer, and experimental HDX-MS deuterated fractions used as target data in simulation reweighting figures/* - Underlying data and scripts used to create all figures and movies used in the article. Where appropriate, README files include instructions for regenerating data used in the article, and details of the Python packages used to run Python scripts are available in conda_environment.yml

opencc-zeroSep 2019View details →

zenodo44/100

ARMOR and NALMA data corresponding to "Observations of anomalous charge structures in supercell thunderstorms in the Southeastern United States"

Dataset includes dual-polarization C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR) data in Raw and quality-controlled Universal Format (UF) from a selected period on 10 April 2009 as well as the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) data in American Standard Code for Information Interchange (ASCII) format from selected period on 10 April 2009.  The ARMOR is located at the Huntsville International Airport in Huntsville, Alabama at 34.64597, -86.77131, 200 m MSL. A set of 15 radar sampling volumes between 1712 UTC and 1821 UTC on 10 April 2009 are included in the dataset. Each of the raw and corrected UF files contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s-1), spectrum width (m s-1), differential phase (°), and total power (dBZ) data. The corrected UF files additionally contain horizontal reflectivity and differential reflectivity data corrected for attenuation and differential attenuation following the methods of Bringi et al. (2001). The corrected files also contain estimated differential propagation phase (°) and computed specific differential phase (° km-1) data (Hubbert and Bringi 1995).    ARMOR file naming conventions are as follows:    RAW_NA_000_125_20090410171216.gz RAW: file format 125: can scan type, where 125 indicates a full or sector volume plan position indicator  20090410171216: date and time in the order of year, month, day, hour, minute, and second   ARMOR_20090410171216_qc1.uf.gz ARMOR: radar name 20090410171216: date and time in the order of year (YYYY), month (MM), day (DD), hour (HH), minute (MM), and second (SS) qc1: denotes ARMOR processed data uf: denotes the file format    NALMA data consist of undecimated VHF source-level lightning measurements in hourly files. The center of the network is located at 34.72461, -86.64533. The network consisted of 11 sensors distributed throughout north Alabama and south-central Tennessee. Information about contributing stations is available in the header of each hourly file, including the station location, status, and the number of sources detected by each station. Further network-specific information documented by Koshak et al. (2004) while Rison et al. (1999) discuss LMA characteristics. Source data include information about the time the source was detected (UTC seconds of the day), latitude and longitude (decimal degrees), altitude (m), reduced chi2 value associated with post-processing (unitless), power (dBW), and a network mask indicating the detecting stations (unitless). The format is (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x).    Hourly file naming conventions are as follows:   LYLOUT_090410_160000_3600.dat.gz LYLOUT: LMA file designator 090410: date in order of last two digits of year (YY), month (MM), and day (DD) 160000: time in order of hour (HH), minute (MM), and second (SS) 3600: length of period covered in file in seconds (3600 s = 1 hour)   Acknowledgments:  Data were collected with support from NASA MSFC Award NNM05AA22A.   References: Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. IEEE Transactions on Geoscience and Remote Sensing, 39(9), 1906–1915. https://doi.org/10.1109/36.951081 Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. Journal of Atmospheric and Oceanic Technology, 12, 643–648.  Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. Journal of Atmospheric and Oceanic Technology, 21(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2 Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. Geophysical Research Letters, 26(23), 3573–3576.

opencc-by-4.0Apr 2020View details →

zenodo44/100

ARMOR and NALMA data corresponding to 2008 storms analyzed in "Examining conditions supporting the development of anomalous charge structures in supercell thunderstorms in the Southeastern United States"

Total lightning and dual-polarization Doppler velocity data are available from the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) and the C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR), respectively, over selected periods on 6 February 2008 and 11 April 2008. NALMA data are provided in American Standard Code for Information Interchange (ASCII) format and ARMOR data are provided in Raw and quality-controlled Universal Format (UF), where quality control methods are described below.    The NALMA data are provided in hourly files which include undecimated point location (source-level) data corresponding to the detection of very high frequency (VHF) radiation emitted during the breakdown of lightning (Rison et al., 1999; Thomas et al., 2001). Source locations were reported from active sensors configured in an 11-sensor array distributed throughout North Alabama and South Central Tennessee, the center of which is located at 34.72641, -86.64533 (Koshak et al. 2004).  Data files include information on the time that each source was detected (UTC seconds of the day), the latitude, longitude, and altitude of each source’s location (decimal degrees and m, respectively), the reduced chi2 value associated with data processing (unitless), a station mask indicating which sensors contributed to the resolved location of each source (unitless). These data are provided in a line-by-line format of (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x). The 2008 data files additionally include a header section that provides further information about each sensor in the network and its relative contribution to the dataset.    The hourly fine naming conventions are as follows for the February 2008 data: LMA_NA_6.2_125_2008-02-06_10-00-00.dat.gz LMA_NA: LMA file designator corresponding to the NALMA 2008-02-06: year (YYYY)-month (MM)-day (DD) 10-00-00: UTC time, (HH)-minute (MM)-second (SS)   And for the April 2008 data: LYLOUT_080411_180000_3600.dat.gz LYLOUT: LMA file designator 080411: date in order of last two digits of year (YY), month (MM), and day (DD) 180000: UTC time in order of hour (HH), minute (MM), and second (SS) 3600: length of period covered in file in seconds (3600 s = 1 hour)   ARMOR data are provided as sets of 14 (14) sampling volumes corresponding to the 6 February 2008 (11 April 2008) periods between 1002 UTC and 1119 UTC (1844 UTC and 1952 UTC). Each RAW and processed UF file contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s-1), spectrum width (m s-1), differential phase (º), and total power (dBZ) data. Horizontal reflectivity and differential reflectivity data were corrected for attenuation and differential attenuation, differential propagation phase (º) was estimated, and specific differential phase (º km-1) was calculated during post-processing (Hubbert and Bringi 1995, Bringi et al. 2001).   Acknowledgments:  NALMA data were collected with support from NASA MSFC Award NNM05AA22A.   References: Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. IEEE Transactions on Geoscience and Remote Sensing, 39(9), 1906–1915. https://doi.org/10.1109/36.951081 Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. Journal of Atmospheric and Oceanic Technology, 12, 643–648.  Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. Journal of Atmospheric and Oceanic Technology, 21(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2 Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. Geophysical Research Letters, 26(23), 3573–3576. Thomas, R. J., Krehbiel, P. R., Hamlin, T., Harlin, J., & Shown, D. (2001). Observations of VHF source powers radiated by lightning. Geophysical Research Letters, 28(1), 143–146. https://doi.org/10.1029/2000GL011464

opencc-by-4.0Jul 2020View details →

zenodo44/100

Data for "Atomic structure of solute clusters in Al-Zn-Mg alloys"

This dataset contains the data used in the publication entitled "<a href="https://www.sciencedirect.com/science/article/abs/pii/S1359645420310119">Atomic structure of solute clusters in Al-Zn-Mg alloys</a>", published in Acta Materialia 17. December 2020. The data contained herein are: <ul> <li>As-acquired transmission electron microscopy (TEM) images.</li> <li>Atom probe tomography data.</li> <li>All structural models used in density functional theory (DFT) calculations.</li> <li>Structures used for simulating scanning-TEM (STEM) images and nanobeam diffraction (NBD) patterns.</li> </ul>   The TEM images includes high angle annular dark field (HAADF) images and selected area diffraction patterns. These are given in .dm3/.dm4 files, and can be opened in e.g. the "<a href="https://www.gatan.com/products/tem-analysis/gatan-microscopy-suite-software">Gatan Microscopy Suite" </a>software. The images are also given as .tif images. The files are names after the "Figx_alloy_condition_xxx". "Figx" refers to the figure in the main article, "alloy" describes the alloy used and "condition" describes from what ageing condition. The uncorrected image series used for Fig. 6c (in the article) is included and requires the <a href="http://lewysjones.com/software/smart-align/">SmartAlign </a>plugin in the Gatan Microscopy Suite to analyse the dataset. SmartAlign allows for correcting rigid and non-rigid distortions in the STEM images in order to reduce effect of specimen drift and scan noise during acquisition.  The ATP data is given as a .xlsx file. The data here is the processed data after applying the maximum separation algorithm. The data here is used to produce Figs. 2b and 2c in the paper.  The structures used in the DFT calculations are given here as .cif files. These are separated into "Single_clusters" and "Stacked_clusters"  and named according to Tabs. 1 and 2 in the Supplementary material of the paper. The two structures used for simulating STEM-HAADF and NBD patterns are given in the folder "TEM_simulations". "Mg32Zn124D_94x94" was used for NBD and "Mg32Zn124D_X_Zn4" was used for HAADF-STEM. The stack used for Supplementary Fig. 7c is labeled "Mg32Zn124D_94x94_slab_1Allayerop.cif".

opencc-by-4.0Dec 2020View details →

zenodo44/100

Data for investigating structural complexity of individual Scots pine trees

Tree functional traits together with processes such as forest regeneration, growth, and mortality affect forest and tree structure. Forest management inherently impacts these processes. Moreover, forest structure, biodiversity, resilience, and carbon uptake can be sustained and enhanced with forest management activities. To assess structural complexity of individual trees, comprehensive and quantitative measures are needed, and they are often lacking for current forest management practices. Fractal analysis and a single scale, independent metric called box dimension offer means for assessing structural complexity of individual trees. Terrestrial laser scanning (TLS) point clouds provide three-dimensional (3D) information on trees that can be utilized in generating the box dimension metric. This data set includes information needed for generating the box dimension from 741 individual Scots pine (Pinus sylvestris L.) trees from 9 sample plots with different thinning treatments located in southern boreal forests. The thinning treatments include two intensities of thinning and control treatment (i.e., no thinning treatment since the establishment). The data set can be used in characterizing structural complexity of individual Scots pine trees of various size as well as assessing effects of various thinning treatments on it. Please see the data descriptor for more information on the data structure and its possibilities. Please keep the designated corresponding author informed of any plans to use the data. Consultation or collaboration with the original investigators is strongly encouraged. Publications and data products that make use of the data must include proper acknowledgement.

opencc-by-4.0Jan 2021View details →

zenodo44/100

NoSyms: A neural network approach to detecting data structures in raw memory

This data was used for a experiments with graph convolutional neural networks for memory forensics as part of a bachelor thesis (included as pdf). Abstract: This work presents a neural network based approach for data structure detection in raw memory that does not require an entirely matching description of the target data structure. Instead, it’s merely necessary to provide multiple descriptions of data structures similar to the target as training data in the form of debugging symbols. The core contribution of this work is a formal description and implementation of encoding data structure definitions as well as raw memory contents such that they can be processed by graph convolutional neural networks. A description and implementation of a neural network meant to detect data structures in the memory contents of a Linux Kernel demonstrates the practical applicability of the described approach. The Code is available on GitHub <a href="https://github.com/NiklasBeierl/nosyms">https://github.com/NiklasBeierl/nosyms</a>. nokaslr_dump is the qemu memory snapshot used to test the model. nokaslr.raw is the "raw" form of the snapshot as produced by Volatility 3's layerwriter plugin. symbols-training-data contains the Volatility symbol JSON files from which training data was derived. nokaslr_pointers.csv lists the kernel space pointers in the snapshot and nokaslr_tasks.csv lists task structs in the snapshot. Both were extracted via a Volatility plugins that are included in the GitHub Repo. vmlinux-5.4.0-58-generic.json is the symbol file for the kernel the snapshot was taken from. other-symbols.zip contains symbol files I generated vor various other kernels but did not end up using, use at your own discretion.

opencc-by-4.0Jun 2021View details →

zenodo44/100

Structural variant discovery and genotyping in next-generation sequencing data

Code, logs, data, and summaries for detection and genotyping of genomic structural variants in the D.melanogaster Sussex LHM hemiclones (and one in-house reference line individual), using Genomestrip/2.0 The unfiltered CNV pipleline results are lhm_gs.cnvs.raw.vcf.gz Filtered CNV results (including removal of bad samples) are filtered.goodS.lhm_gs.cnvs.raw.vcf.gz The file uploaded to NCBI dbVAR (which comprises of the filtered CNVs and indels >50bp from the HaplotypeCaller method) is lhm_sx16.dbVAR.vcf.gz The NCBI dbVAR accession number is nstd134. Code, logs and summary data are in the zipped archives, named accordingly. The archive reference_data.zip contains additional input files required for Genomestrip, including a shell script for making some of them. The file gstrip_lhm_RG_bams.list is also an input for Genomestrip, indicating bam file names and paths. The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554

opencc-by-4.0Oct 2016View details →

zenodo44/100

Representative Counties of Germany and Their Structural Data

<h2>Content</h2> A dataset of counties that are representative for Germany with regard to <ul> <li>the average disposable income,</li> <li>the quota of divorces,</li> <li>the respective quotas of employees working in the services (excluding logistics, security, and cleaning) and the MINT sectors,</li> <li>the proportions of age groups in the total proportion of the respective population, with age groups in five-year strata for the population aged between 30 and 65 and the population in the age range between 65 and 75 each considered separately for the calculation of representativeness.</li> <li>population density, if the benchmark is defined as the national average density weighted by county population. This value is calculated by averaging the population-per-square-kilometer figures of all German counties, weighted by the population size of each county. In contrast to a simple arithmetic mean of county-level densities, which would disproportionately reflect sparsely populated areas, this method reflects the actual density of the environments where people live. The resulting value for the selected counties falls within the 95% confidence interval of the national weighted average.</li> </ul> In addition, data from the four big cities Berlin, München (Munich), Hamburg, and Köln (Cologne) were collected and reflected in the dataset. The dataset is based on the most recent data available at the time of the creation of the dataset, mainly deriving from 2022, as set out in detail in the readme.md file. <h2>Method applied</h2> The selection of the representative counties, as reflected in the dataset, was performed on the basis of official statistics with the aim of obtaining a confidence rate of 95%. The selection was based on a principal component analysis of the statistical data available for Germany and the addition of the regions with the lowest population density and the highest and lowest per capita disposable income. A check of the representativity of the selected counties was performed. In the case of Leipzig, the city and the district had to be treated together, in deviation from the official territorial division, with respect to a specific use case of the data.

opencc-zeroMay 2024View details →

zenodo44/100

Data repository of multi-temporal high-resolution data products of ecosystem structure derived from country-wide airborne laser scanning surveys of the Netherlands

This data repository contains a set of multi-temporal data products of ecosystem structure derived from four national ALS surveys of the Netherlands (AHN1–AHN4) (folders: 1_AHN1, 2_AHN2, 3_AHN3, and 4_AHN4). Four sets of 25 LiDAR-derived vegetation metrics representing ecosystem height, cover, and structural variability are provided at 10 m spatial resolution, providing valuable data sources for a wide range of ecological research and field beyond. A preview of all generated LiDAR metrics are also provided (folder: 5_Maps). All 25 LiDAR metrics were calculated using Laserfarm workflow  (<a href="https://laserfarm.readthedocs.io/en/latest/">https://laserfarm.readthedocs.io/en/latest/</a>) (building on the user-extendable features from the “Laserchicken” software: <a href="https://laserchicken.readthedocs.io/en/latest/#features">https://laserchicken.readthedocs.io/en/latest/#features</a>). All metrics are calculated with the normalized point cloud. More details on metric calculation are provided on GitHub (Laserchicken: <a href="https://github.com/eEcoLiDAR/laserchicken">https://github.com/eEcoLiDAR/laserchicken</a> and Laserfarm: <a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a>), as well as on the “Laserchicken” documentation page (<a href="https://laserchicken.readthedocs.io/en/latest/">https://laserchicken.readthedocs.io/en/latest/</a>). We also provided masks to minimize the influence of water surfaces, buildings and roads, powerlines and NA values in the data products (folder: 6_Masks).  To supplement the generated data products, we also provided a set of raster layers that contains point/pulse density of each AHN survey and the DTM and DSM raster layers for each AHN dataset (folder: 7_Auxiliary_data). To test the robustness of the LiDAR metrics, we also compared the metrics generated from different pulse densities across different habitat types (folder: 8_Sensitivity_analysis). Two use cases demonstrated the utility of the presented data products: (use case 1) monitoring forest structural change across time using multi-temporal ALS data and (use case 2) comparison of vegetation structural difference within Natura 2000 sites. The used data are also provided (folder: 9_Use_case). Note that all the raster layers are provided at 10 m resolution under the local Dutch coordinate system “RD_new” (EPSG: 28992, NAP:5709). To gain more insights of the pre-classification accuracy of the AHN datasets, we also conducted a preliminary assessment of the effect of terrain filtering on vegetation change detection across AHN datasets (i.e. AHN2–AHN4). The data used in this analysis are made available (folder: 10_Ground_classification). An overview of all the folders in the repository: 1.     AHN1 2.     AHN2 3.     AHN3 4.     AHN4 5.     Maps Those folders contain four sets of 25 LiDAR metrics at 10 m resolution generated from each AHN dataset. The file names and their corresponding LiDAR metrics can be found in Table 1. An additional folder (5_Maps) contains the maps (.pdf format) of all 25 metrics for each AHN dataset. 6. Masks <ul> <li>ahn3_10m_mask_building_road_water.tif</li> <li>ahn4_10m_mask_building_road_water.tif</li> <li>ahn4_10m_mask_powerline.tif</li> <li>ahn1_10m_NA_mask.tif</li> <li>ahn2_10m_NA_mask.tif</li> <li>ahn3_10m_NA_mask.tif</li> <li>ahn4_10m_NA_mask.tif</li> </ul>   It contains two mask layers of water surfaces, buildings and roads for both AHN3 and AHN4 data products based on the Dutch cadaster data (TOP10NL) from 2018 (corresponding to AHN3) and 2021 (corresponding to AHN4) (<a href="https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl">https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl</a>). In the masks, water surfaces, buildings and roads were merged into one class with pixel value assigned to 1 and the rest has the pixel value of 0. There is also a powerline mask generated from the AHN4 dataset at 10 m resolution, where pixels containing powerlines were assigned a value of 1 and the rest as NoData. We provide those masks to minimize the inaccuracies of the data products caused by human infrastructures and water surfaces. We also provided a mask for each AHN dataset where NA value occurs — areas with no vegetation points (“unclassified” class in the AHN datasets). Pixels with NA value were assigned with a value of 1 and the rest as 0. 7. Auxiliary data (1) Point_density <ul> <li>ahn1_10m_point_density.tif</li> <li>ahn2_10m_point_density.tif</li> <li>ahn3_10m_point_density.tif</li> <li>ahn4_10m_point_density.tif</li> </ul> (2) Pulse_density <ul> <li>ahn3_10m_pulse_density.tif</li> <li>ahn4_10m_pulse_density.tif</li> </ul> (3) Flighttime <ul> <li>ahn3_10m_flighttime.tif</li> <li>ahn4_10m_flighttime.tif</li> </ul> (4) DTM_DSM <ul> <li>ahn2_10m_dtm.tif</li> <li>ahn2_10m_dsm.tif</li> <li>ahn3_10m_dtm.tif</li> <li>ahn3_10m_dsm.tif</li> <li>ahn4_10m_dtm.tif</li> <li>ahn4_10m_dsm.tif</li> </ul> It contains four raster layers representing the point density of each AHN dataset, two raster layers for pulse density of the AHN3 and AHN4, two raster layers for flight timestamp of the AHN3 and AHN4, and six DTM and DSM layers for AHN2–AHN4. All raster layers are provide at 10 m resolution. 8. Sensitivity analysis <ul> <li>Dunes</li> <li>Marsh</li> <li>Grassland</li> <li>Shrubland</li> <li>Woodland</li> <li>Code</li> <li>Figure</li> </ul> It contains the 25 metrics generated from point clouds with the original and down-sampled pulse densities (original pulse density of the AHN4, pulse density of the AHN3, ½ of the pulse density of the AHN3, and ¼ of the pulse density of AHN3) for each habitat type (i.e. dunes, marsh, grassland, shrubland, and woodland). We also provided the code and the figures generated from this analysis. 9. Use_case (1) Multi-temporal_AHN <ul> <li>Data</li> <li>Usecase_multi-temporal_AHN.R</li> </ul> It contains the input data for the use case data processing (i.e. Data folder), including the shapefile of the area (i.e. shp folder), and extracted pixel value from six selected LiDAR metrics from AHN1–AHN5 (i.e. Metrics folder), and the selected LiDAR metrics of the area (e.g. Hp95 folder), and the R code for data processing (i.e. Usecase_multi-temporal_AHN.R). (2) Natura2000 <ul> <li>Data</li> <li>Natura2000_end2021_HABITATCLASS.csv</li> <li>Natura2000_NL_habitat_grouped.csv</li> <li>Usecase_Natura2000.R</li> </ul> It contains a folder of the input data used for the use case (i.e. Data folder), including the shapefile (i.e. shp folder) of the Natura 2000 sites in the Netherlands (i.e. Nature2000_NL_RDnew.shp) and the 100 random sample plots from each habitat type (e.g. woodland_points.shp), and the LiDAR metrics from AHN4 used for demonstrating the vegetation  structure within each habitat type (i.e. AHN4_metrics folder). The table “Natura2000_end2021_HABITATCLASS.csv” is the original attribute table of Natura 2000 sites, including information related to the description of habitat classes (column “DESCRIPTION”), the code corresponding to the habitat class (column “HABITATCODE”), the code for the specific site (column “SITECODE”), and the percentage of the cover of a specific habitat class in one site (column “PERCENTAGECOVER”). The table “Natura2000_NL_habitat_grouped.csv” contains two subtabs, one (i.e. “Habitatclass”) is the copy of the original attribute table of Natura 2000 sites in the Netherlands, and the other one (i.e. “Habitat_class_summary”) is the grouped habitat type based on the dominant habitat class (i.e. class with the highest percentage cover) in each site. Different colors indicate different habitat types, corresponding to the colors in the first tab (“Habitatclass”) where the dominant habitat class was highlighted for each site. 10. Ground classification <ul> <li>Raw_point_cloud</li> <li>Computed_metrics </li> <li>Plottings_and_code</li> <li>ArcGIS_project</li> </ul> It contains four subfolders: (1) The original point cloud for each sample area (AHN2–AHN4) (subfolder: Raw_point_cloud); (2) The 25 LiDAR metrics computed from the original point clouds with pre-classification of AHN and from the new terrain filtering method across AHN2–AHN4 (subfolder: Computed_metrics); (3) Generated violin plots for the comparison of vegetation change detection and the python code employed (subfolder: Plottings_and_code); (4) an ArcGIS project which the shapefiles of the study area and sample plots are provided (subfolder: ArcGIS_project). Code availability Jupyter Notebooks for processing AHN datasets: <a href="https://github.com/ShiYifang/AHN">https://github.com/ShiYifang/AHN</a> Laserfarm workflow repository: <a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a> Laserchicken software repository: <a href="https://github.com/eEcoLiDAR/laserchicken">https://github.com/eEcoLiDAR/laserchicken</a> Code for downloading AHN dataset: <a href="https://github.com/ShiYifang/AHN/tree/main/AHN_downloading">https://github.com/ShiYifang/AHN/tree/main/AHN_downloading</a> Code for generating masks for AHN datasets: <a href="https://github.com/ShiYifang/AHN/tree/main/AHN_masks">https://github.com/ShiYifang/AHN/tree/main/AHN_masks</a> Code for demonstration of ecological use cases: <a href="https://github.com/ShiYifang/AHN/tree/main/Use_case">https://github.com/ShiYifang/AHN/tree/main/Use_case</a>

opencc-by-4.0Oct 2024View details →

zenodo44/100

Tree and habitat structure data from rainforest fragments and coffee plantations in the Anamalai Hills, Western Ghats, India

TITLETree and habitat structure data from rainforest fragments and coffee plantations in the Anamalai Hills, Western Ghats, India  DESCRIPTIONThis dataset contains point-centred quarter (PCQ) data on trees and habitat structure measurements data from rainforest fragments and some coffee plantations in the Valparai Plateau and Anamalai Tiger Reserve, Tamil Nadu, India. The data were gathered to quantity habitat parameters for bird and small carnivorous mamm community studies. Data were gathered mainly by T. R. Shankar Raman and Divya Mudappa (2000 to 2003), Hari Sridhar (2005), and Akshay Surendra (2019).PublicationsSpecific portions of the dataset have been used in the following publications:<ul><li>Mudappa, D. 2001. <a href="https://hdl.handle.net/10603/101890">Ecology of the brown palm civet Paradoxurus jerdoni in the tropical rainforests of the Western Ghats, India</a>. Ph. D. thesis, Bharathiar University, Coimbatore. https://hdl.handle.net/10603/101890</li><li>Raman, T. R. S. 2001. <a href="https://archive.org/details/raman-2001-ph-d-thesis-iisc">Community ecology and conservation of mid-elevation tropical rainforest bird communities in the southern Western Ghats, India</a>. PhD thesis, Indian Institute of Science, Bangalore. https://archive.org/details/raman-2001-ph-d-thesis-iisc</li><li>Raman, T.R.S. 2006. <a href="https://doi.org/10.1007/s10531-005-2352-5">Effects of Habitat Structure and Adjacent Habitats on Birds in Tropical Rainforest Fragments and Shaded Plantations in the Western Ghats, India</a>. Biodiversity and Conservation 15: 1577–1607. https://doi.org/10.1007/s10531-005-2352-5</li><li>Sridhar, H., & Sankar, K. 2008. <a href="https://doi.org/10.1017/S0266467408004823">Effects of habitat degradation on mixed-species bird flocks in Indian rain forests</a>. Journal of Tropical Ecology 24: 135-147. https://doi.org/10.1017/S0266467408004823</li><li>Surendra, A. & Raman, T. R. S. 2022. <a href="https://doi.org/10.1101/2022.10.22.513365">Forest bird decline and community change over 19 years in long-isolated South Asian tropical rainforest fragments</a>. Preprint. BioRxiv 2022.10.22.513365. https://doi.org/10.1101/2022.10.22.513365  </li></ul>A related dataset is the following: Raman, T. R. S. (2020). Data from: Effects of Habitat Structure and Adjacent Habitats on Birds in Tropical Rainforest Fragments and Shaded Plantations in the Western Ghats, India. Dryad Dataset. https://doi.org/10.5061/dryad.4mw6m907q  Curation and correctionsData were collated, curated, and corrected before this upload. Besides addition of new columns, explanations of metadata, and other corrections included few related to canopy measurements, effective girth of multi-stem trees, and species identification.AcknowledgementsWe are grateful to P. Jeganathan and P. R. Shankar for assistance with data collection in 2000. Others who assisted with field research, and funding agencies related to the specific studies, are acknowledged in the above publications. The data compilation and publication was carried out as part of a grant from Fondation Franklinia to NCF. CONTACTS  CONTACT #1 1. Name: T. R. Shankar Raman 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: trsr@ncf-india.org 5. ORCID: https://orcid.org/0000-0002-1347-3953CONTACT #2 1. Name: Divya Mudappa 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: divya@ncf-india.org 5. ORCID: https://orcid.org/0000-0001-9708-4826CONTACT #3 1. Name: Hari Sridhar 2. Work Address: Wildlife Institute of India, Post Bag #18, Chandrabani, Dehradun – 248001, Uttarakhand, India; Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: harisridhar1982@gmail.com 5. ORCID: https://orcid.org/0000-0003-3286-0120CONTACT #4 1. Name:  Akshay Surendra 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India; School of the Environment, Yale University, New Haven, CT – 06511, USA; New York Botanical Garden, 2900 Southern Blvd, Bronx, NY 10458 3. Work Phone: +91 821 2515601 4. Email address: akshaysurendra1@gmail.com 5. ORCID: https://orcid.org/0000-0003-2719-7432   GEOGRAPHIC COVERAGE1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India2. 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) TEMPORAL COVERAGE1. Begins: 2000-01-01 (Year, Month, Day) 2. Ends: 2019-12-31 (Year, Month, Day) METHODSMethods involved are described in the publications listed above. The vegetation sampling methods are briefly described below.PCQ data: Trees ≥30cm girth at breast height (gbh, at 1.3 m) were sampled in replicate point-centred quarter (PCQ) points in each of the sites (fragments or coffee plantations).All trees in the PCQ plots were identified to species, or in a few cases to genus, using available field guides. Using a tape measure, distance from plot centre to the middle of the bole and GBH were recorded for each tree. At each of the PCQ plots, circular plots were laid to enumerate shrubs and cut trees and record presence or absence of lianas, cane, Lantana etc as described in the metadata. Canopy and leaf litter variables were measured at replicate points, spaced 25  to 50 m apart, in each site. Elevation readings were also taken at these points using an altimeter or handheld GPS. Canopy height was measured using a rangefinder. Percentage canopy cover was measured using a spherical densiometer at each of the 25 points in each site. Vertical stratification was assessed by noting presence or absence of foliage in the following height intervals (in metres): 0–1, 1–2, 2–4, 4–8, 8–16, 16–24, 24–32, and > 32, directly above and in a 0.5 m radius around each point. Leaf litter depth on the forest floor was measured using a calibrated wooden probe at each point. Where ground vegetation and litter were disturbed along trails, the samples were taken away from trails in the forest floor. FILES INCLUDED Besides the 00_README.txt file that contains this metadata, the dataset includes the following 7 files, whose details and contents are explained below. (Wherever used in the various files, NA implies not available.)  01) sites.csv -- Details of study sites verbatimLocality: Name of locality as originally used Fragment: Name of rainforest fragment or coffee plantation decimalLongitude: Longitude in decimal degrees North (WGS 84 datum) decimalLatitude: Latitude in decimal degrees East (WGS 84 datum) habitat: Habitat type as mature tropical rainforest, tropical rainforest fragment, or coffee plantation Description: Description of the place  02) allpcqdata.csv -- Tree data from point-centred quarter (PCQ) surveys Year: Year of survey for  bird and vegetation study verbatimLocality: Name of locality as originally used Fragment: Name of rainforest fragment or coffee plantation Point_name: Name ID of point-centred quarter (PCQ) point as used within a survey year pointID: Unique ID of point-centred quarter (PCQ) point including year of survey Tree_no: Tree number ID given to the four trees in each PCQ plot (T1 to T4) verbatimIdentification: Scientific name of tree species as originally written or identified scientificName: Scientific name as currently identified under updated taxonomy nativeAlien: Category indicating whether species is native or alien to the region/country kingdom: Taxonomic Kingdom phylum: Taxonomic Phylum Distance_eff: Distance in metres from centre of PCQ plot to centre of tree trunk Girth_eff: Girth in centimetres (cm) at breast height (1.3 m) of the tree after correction (using appropriate formula) in the case of multi-stemmed individuals locationRemarks: Code for site name as originally used SpCode: Species code as originally used during data entry TreeHeight: Tree height in metres (only  available in 2019 survey) identificationRemarks: Notes related to identification if available occurrenceRemarks: Notes related to multi-stemmed individuals (girths in cm) if available and note on one possibly errorneous girth  03) pcqlocations.csv -- Locations of sample PCQ points pointID: Unique ID of point-centred quarter (PCQ) point including year of survey note: Site name code decimalLatitude: Latitude in decimal degrees East (WGS 84 datum) decimalLongitude: Longitude in decimal degrees North (WGS 84 datum) coordinateUncertaintyInMeters: Approximate uncertainty of the location in metres  04) allhabitat.csv -- Data on habitat structure variables Year: Year of survey for  bird and vegetation study verbatimLocality: Name of locality as originally used Fragment: Name of rainforest fragment or coffee plantation Point: ID of replicate survey point within the Fragment 0-1m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 0-1 m above ground 1-2m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 1-2 m above ground 2-4m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 2-4 m above ground 4-8m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 4-8 m above ground 8-16m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 8-16 m above ground 16-24m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 16-24 m above ground 24-32m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 24-32 m above ground over32m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band greater than 32 m above ground VertStrata: Number of vertical strata with foliage (sum of preceding 8 columns) CanopyHeight: Canopy height in metres CanopyOpenness: Canopy openness in percentage as measured using a spherical densiometer CanopyCover: Canopy cover (closure) in percentage as measured using a spherical densiometer CanopyOverlap: Canopy overlap rank: 0-open sky above; 1-branches above barely touching; 2-overlapping branches above, sky visible; 3-overlapping branches, sky not visible UC: Canopy overlap rank as above, for understorey vegetation only MC: Canopy overlap rank as above, for the midstorey only CC: Canopy overlap rank as above, for the upper canopy only Altitude: Altitude above sea leavel in metres, measued from hand-held altimeter or GPS device RfShrub: Number of shrubs (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point Coffee: Number of coffee bushes (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point Maesopsis: Number of alien Maesopsis eminii stems (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point Strobilanthes: Number of Strobilanthes shrubs (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point TotalShrub: Total number of shrubs within 2 m radius of point Liana: Presence (1) or absence (0) of woody lianas within 5 m radius of point Cane: Presence (1) or absence (0) of cane (Calamus sp.) within 2 m radius of point Lantana: Presence (1) or absence (0) of Lantana camara shrubs within 2 m radius of point Bamboo: Presence (1) or absence (0) of bamboo culms within 2 m radius of point LeafLitter: Depth of leaf litter in cm (to 0.5 cm accuracy) measured using a calibrated wooden probe CutTrees: Number of cut trees within 5 m radius of point  05) gbifnames.csv -- Results of GBIF name matching tool sno: Serial number verbatimScientificName: Scientific name of tree species as originally written or identified scientificName: Scientific name after matching with Global Biodiversity Information Facility (GBIF) database to lowest taxonomic level sciNameWithAuthor: Scientific name with author as provided by GBIF name matching tool key: GBIF key as provided by GBIF name matching tool matchType: Type of match as provided by GBIF name matching tool confidence: Confidence as provided by GBIF name matching tool status: Status as accepted name or synonym as provided by GBIF name matching tool rank: Taxonomic rank as provided by GBIF name matching tool kingdom: Kingdom as provided by GBIF name matching tool phylum: Phylum as provided by GBIF name matching tool class: Class as provided by GBIF name matching tool order: Order as provided by GBIF name matching tool family: Family as provided by GBIF name matching tool genus: Genus as provided by GBIF name matching tool species: Species as provided by GBIF name matching tool canonicalName: Canonical name as provided by GBIF name matching tool authorship: Author of name as provided by GBIF name matching tool  06) plots2000.csv -- Data from 5 m radius circular plots in select sites verbatimLocality: Name of locality as originally used Fragment: Name of rainforest fragment or coffee plantation PlotID: ID of 5 m radius plot Treeno: Serial number of tree in the plot verbatimIdentification: Scientific name of tree species as originally written or identified scientificName: Scientific name as currently identified under updated taxonomy Girth_eff: Girth in centimetres (cm) at breast height (1.3 m) of the tree after correction (using appropriate formula) in the case of multi-stemmed individuals nativeAlien: Category indicating whether species is native or alien to the region/country kingdom: Kingdom as provided by GBIF name matching tool phylum: Phylum as provided by GBIF name matching tool occurrenceRemarks: Notes related to multi-stemmed individuals (girths in cm) if available and identification 07) anampcqs4gbif.rmd -- Text file with code in the R statistical and programming language This R code was used for converting data in this Zenodo dataset into Darwin Core occurrence dataset for upload to the Global Biodiversity Information Facility (GBIF, https://www.gbif.org). The published dataset can now be accessed at: https://doi.org/10.15468/cmsveh Changes in Version 2In sites.csv, changed habitat from "Rainforest" to "Tropical rainforest fragment" for PuthuthottamAdded the anampcqs4gbif.rmd file with R code

opencc-by-4.0Oct 2023View details →

zenodo44/100

Data for "A Quantum Definition of Molecular Structure"

Supplemental data for our article "A Quantum Definition of Molecular Structure".Version 1.1.0 contains data for additional k-medoids runs performed on different subsets of the complete sample.

opencc-by-4.0Oct 2023View details →

zenodo44/100

Data description: Deprivation of loading during early healing of rat Achilles tendons affects extracellular matrix composition and structure, and reduces cell density and cell alignment

<a name="_Hlk158643946"></a>Data description: Deprivation of loading during early healing of rat Achilles tendons affects extracellular matrix composition and structure, and reduces cell density and cell alignment Malin Hammerman, Maria Pierantoni, Hanna Isaksson *, Pernilla Eliasson * * joint last authors This dataset contains microscope images obtained from sections of healing and intact rat Achilles tendons undergoing different in vivo loading protocols and different time points post-transection. The data presented are the full resolution microscope images available in lower resolution in the accompanying manuscript’s Supplementary Figures 4-6. Each zipped folders contain images (tif-files) from all time-points for each respective staining and loading group.    <ul> <li>Col1: Sections stained with Collagen 1 antibodies</li> <li>Col3: Sections stained with Collagen 3 antibodies</li> <li>Elastin: Sections stained with Elastin antibodies</li> <li>Full_loading: Free cage activity</li> <li>Reduced_loading: Paralysis of the calf muscle with Botox</li> <li>Minimal_loading: Botox combined with joint fixation using a steel-orthosis</li> <li>Intact_reference: Contralateral uninjured Achilles tendons, used as reference</li> </ul> More description of the datasets inside the zipped files are available below and in the file 'Data Description.pdf'   Brief re-cap of methods Histological analysis was performed on healing Achilles tendons from Female Sprague-Dawley rats, specific-pathogen free (11-12 weeks, weight 299 ± 15 g), that had undergone full transection [13] of the right Achilles tendon, and been exposed to different levels of loading. Altered loading was imposed through two mechanisms. Reduced loading involved intramuscular Botox injections in the right calf muscles to induce plantar flexor muscle paralysis [24]. Additionally, the rats in the minimal loading group received a steel-orthosis around their right hindlimb directly after surgery [24]. Snap frozen tendons in OCT were sectioned longitudinally (7 μm thickness) and stained with immunofluorescent staining for collagen 1, collagen 3, or elastin. Sections were counterstained with DAPI followed by mounting. The tissue sections were imaged under a microscope (DMi8, Leica Microsystems, Wetzlar, Germany, with a Hamamatsu Orca LT Flash sCMOS camera) where fluorescence was detected at 550 nm (secondary antibody Alexa Fluor 594), 470 nm (secondary antibody Alexa Fluor 488) and 385 nm (DAPI), and exposure time was held constant for each color channel regarding magnification and staining. Mapping images of the entire tendon were obtained for one section per group (n=1 per healing time, loading group and ECM matrix protein). All images were adjusted to the negative control, where the primary antibody was omitted, to correct for unspecific antibody detection. Microscope images and description of file-names All data is presented in the form of .tif files. Please refer to the scale bars in the images. All image-files are named using the following abbreviations, as described below. As an example, the file name “Tendon_col1_FL_1W_col1.tif” refers to a tendon section stained for collagen 1 from a rat exposed to full loading for a period of 1 week after tendon transection, where only the channel for collagen 1 is shown, whereas “Tendon_col1_FL_1W_merged.tif” includes the channels for both staining for collagen 1 and DAPI of the same section. Col1: Sections stained with Collagen 1 antibodies Col3: Sections stained with Collagen 3 antibodies Elastin: Sections stained with Elastin antibodies dapi: Sections stained with 4',6-Diamidino-2-Phenylindole Dihydrochloride. FL:   Full loading (free cage activity), RL:   Reduced loading (paralysis of the calf muscle with Botox), ML:  Minimal loading (Botox combined with joint fixation using a steel-orthosis) IT:    Intact contralateral Achilles tendons, used as reference. 1W: Healing time point 1 week after transection 2W: Healing time point 2 weeks after transection 3W: Healing time point 3 weeks after transection 20W: Healing time point 20 weeks after transection Settings for brightness and contrast Collagen 1 1w FL 2000-12 000, UL 4000-10 000, ML 4000-12 000 2w FL 2500-10 000, UL 4000-10 000, ML 5000-12 000 3w FL 2000-12 000, UL 3500-13 000, ML 3500-14 000 12w FL 3000-12 000 20w FL 3000-11 000 IT 2000-8 000 Collagen 3 1w FL 3000-12 000, UL 4000-10 000, ML 4000-13 000 2w FL 2000 - 7 000, UL 2500-12 000, ML 2000-12 000 3w FL 2000-12 000, UL 3500-13 000, ML 3500-14 000 12w FL 3000-12 000 20w FL 2000-12 000 IT 3000-12 000 Elastin 1w FL 4000-10 000, UL 5000 - 8000, ML 3500-12 000 2w FL 3000-12 000, UL 3000-12 000, ML 3000-12 000 3w FL 2500-12 000, UL 2000-12 000, ML 2500-12 000, 12w FL 3500-12 000 20w FL 3500-12 000 IT 2000-12 000

opencc-by-4.0Apr 2024View details →

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