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27 results for “pattern databases”
A Comprehensive Global Aquatic N2O Emission Database (GANED): Unravelling N2O Emission Patterns from Different Water Bodies, 1980-2023
The Global Aquatic Nitrous Oxide Emission Database (GANED) is a comprehensive synthesis of empirical observations of N2O concentration measurements and flux records, spanning the period 1980-2023. GANED advances N2O research by providing the first global systematic emission mechanisms among the different aquatic system types, including rivers, streams, estuaries, reservoirs, ponds, lakes, open seas and coastal areas. The N2O data in GANED is further interconnected with biogeochemical metadata on dissolved oxygen, dissolved organic carbon, ammonium, nitrate, nitrite, total nitrogen, water temperature, salinity and pH, along with site data (latitude, longitude, codes of channel type, depth, surface area, elevation). The dataset explains the discrepancy that emission of N2O in aquatic bodies is determined mainly by substrate availability, and not by climatic factors, and reveals the systematic biases of concentration-only measurements, which can result in an underestimation of fluxes in effluent water of dynamically changing aquatic waters. Consequently, GANED constitutes a crucial transition “where” emissions occur to understanding “why” they differ across systems, and thus enabling targeted mitigation interventions. GANED includes 5130 records of N2O concentration and 7386 flux measurements from 3,002 unique sites, most of which are resolved to the daily time scale.
Database_Appliance_Adoption_Patterns
<p>Preliminary version of the database of appliance adoption patterns in recently electrified settlements in developing countries.</p>
Database for "A new perspective for charactering the spatio-temporal patterns of the error in GPM IMERG over mainland China"
<p>This file contains the <strong>dataset</strong> accompanying the manuscript '<strong>2020EA001232-TR'</strong> submitted to the <strong>ESS</strong> journal (https://earthandspacescience-submit.agu.org).</p> <p><strong>Title</strong>: "A new perspective for charactering the spatio-temporal patterns of the error in GPM IMERG over mainland China"</p> <p>China Merged Precipitation Analysis data (CMPA, hourly, with the resolution of , as validation data) for China Mainland is available at website http://data.cma.cn.</p> <p>Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrieval data (IMERG, half-hourly, with the resolution of , as the observed data) is available at https://pmm.nasa.gov/data-access/downloads/gpm.</p> <p>The Shuttle Radar Topography Mission data (SRTM, with a 90-m spatial resolution) could be accessed at http://srtm.csi.cgiar.org.</p>
Text-fig. 1. Result of cumulative random counting of MN 5 localities in central Europe and the Iberian Peninsula. Ten simulations were run for each area. a. Results of the count including the average in bold, showing the clearly lower diversity in IB. b. The average lines standardized, showing similar patterns in the two areas. Note that in the simulation around thirty localities were needed to capture 80 % of the regional diversity. in Generically Speaking, A Survey On Neogene Rodent Diversity At The Genus Level In The Now Database
Text-fig. 1. Result of cumulative random counting of MN 5 localities in central Europe and the Iberian Peninsula. Ten simulations were run for each area. a. Results of the count including the average in bold, showing the clearly lower diversity in IB. b. The average lines standardized, showing similar patterns in the two areas. Note that in the simulation around thirty localities were needed to capture 80 % of the regional diversity.
Data for "Effects of forest dieback on deadwood patterns: large scale trends from a cross-analysis of European databases"
<p><strong><span>Aims</span></strong></p> <p><span>We carried out an opportunistic correlative study between past crown conditions and current deadwood volumes.</span></p> <p><span>Our aim was to mobilise available data on site factors and long-term monitoring of crown vitality indicators in Europe to investigate the influence of current and recent local defoliation levels on plot-level deadwood volume.</span></p> <p><span>For a subset of level I, 16*16-km monitoring plots located throughout Europe, we benefitted from data on both (i) deadwood measurements carried out within the framework of the Forest Focus Biosoil Project </span><span>(Galluzzi et al., 2019)</span><span>, pre-processed into a consistent and harmonized deadwood dataset by </span><span>Puletti et al. (2019)</span><span>, and (ii) defoliation assessments provided yearly since 1989 by the International Co-operative Program on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests), the most comprehensive European monitoring network for the large-scale assessment of forest ecosystem health </span><span>(Vitale et al., 2014)</span><span>. </span></p> <p><span>Biosoil data on deadwood and ICP data on defoliation have never been crossed before.</span></p> <p><span>We used defoliation level as a proxy for the severity of stand dieback. Deadwood patterns can be addressed through deadwood profiles, which subdivide local deadwood stocks into classes based on size, position and decay stage.</span></p> <p><a name="_Toc175840512"></a><a name="_Toc116027761"></a><span><strong><span>ICP database and defoliation protocol</span></strong></span></p> <p><span>The International Cooperative Program to assess and monitor air pollution effects on the forest (ICP Forests) is responsible for an extensive level I monitoring system of forest sites </span><span>(Hauβmann & Fischer, 2004)</span><span>, which has been in operation since 1986. This large-scale level I network is made up of dense, spatially representative sampling points placed throughout European forests on a 16 × 16 km virtual grid, and is dedicated to monitoring forest conditions. The sampling points cover most European forested areas and encompasses ca. 6000 monitoring plots in 42 countries. In each plot, a visual evaluation of defoliation and discoloration of tree crowns is performed annually to survey forest health status (<a href="http://icp-forests.net/page/largescale-forest-condition">http://icp-forests.net/page/largescale-forest-condition</a>). Data management is presently carried out at the Programme Co-ordinating Centre (PCC) of ICP Forests in Eberswalde, Germany, and all data are available upon request. Since 1989, a standardized procedure for “annual surveys of crown condition’’ has been applied to 24 selected dominant and co-dominant trees with a minimum height of 60 cm and showing no significant mechanical damage. The defoliation and discoloration level of each tree crown is visually assessed on a sliding scale of 5% increments as the percentage of needle/leaf loss in the assessable crown as compared to a reference tree with full foliage. Mean defoliation at the plot scale was defined as the proportion of “damaged” trees i.e., with a defoliation rate of more than 25%, and used as a proxy for plot decline level. In the ICP database, the factors associated with observed defoliation related to natural disturbances or management (i.e., vertebrate or insect herbivory, fungal or fire damage, drought impacts, signs of removal of coarse woody debris, past landscape) were not recorded in a sufficiently standardized way to be used as covariates in our models. Similarly, plot-level living tree density and above-ground biomass for standing living trees (expressed in kg.ha<sup>−1</sup>), presumably surveyed in subplot 2, were not available.</span></p> <p><a name="_Toc175840513"></a><a name="_Toc116027762"></a><span><strong><span>Biosoil database and deadwood protocol</span></strong></span></p> <p><a name="_Toc116027763"></a><span>In the framework of the large collaborative European Forest Focus BioSoil-Biodiversity project</span><span>, a system of circular concentric subplots was built around certain ICP level I plots to collect additional data on stand structure and biodiversity between 2005 and 2008 (Figure 1). </span><span><span>The individual countries were responsible for selecting the ICP level I plots to be included in the BioSoil project </span></span><span><span>(Galluzzi et al., 2019)</span></span><span><span>. Overall, a total of 3243 geocoded Level I plots were considered in 19 European countries </span></span><span><span>(Puletti et al., 2017)</span></span><span><span>: Austria, Belgium (Flanders only), Cyprus, the Czech Republic, Denmark, Finland, France, Germany (eight federal states only), Hungary, Ireland, Italy, Latvia, Lithuania, Poland, Slovakia, Slovenia, Spain, Sweden and the United Kingdom (Figure 1). BioSoil project results are recorded in the multi-dimensional LI-BioDiv geodatabase that contains raw data on forest structure and vegetation records used to calculate simple plot-level structural and compositional forest variables (i.e., biomass, deadwood volume, plant alpha-diversity; </span></span><span><span>Bastrup-Birk et al. 2007; Hiederer & Durant 2010)</span></span><span><span>. At each plot, deadwood was quantified on an area of 400 m<sup>2</sup> (BioSoil subplots 1 and 2, radius of 11.28 m; </span></span><span><span>Puletti et al., 2017)</span></span><span><span>. The deadwood survey included coarse woody debris (including lying dead trees), snags (including standing dead trees) and stumps more than 10 cm in diameter. Only snags and stumps more than 130 cm in height were considered. Diameter, length or height, tree species and decay stage (5 classes) were recorded for each deadwood piece. The raw ICP deadwood data were processed by </span></span><span><span>Puletti et al. (2017, 2019)</span></span><span><span> into a consistent and harmonized pan-European deadwood dataset, which we used in this study. The dataset provides total deadwood volume and the volume of several deadwood types for each plot. Further details can be found in the ICP Forests manual (</span></span><a href="http://icp-forests.net/page/icp-forests-manual"><span><span>http://icp-forests.net/page/icp-forests-manual</span></span></a><span><span>), </span></span><span><span>Puletti et al. (2019)</span></span><span><span> and </span></span><span><span>Augustynczik et al. (2024)</span></span><span><span>.</span></span></p> <p><span><span>In our study, we considered the following response variables</span></span><span>: (i) total deadwood volume, (ii) </span><span>standing deadwood (snags) volume, (iii) volume of ground-lying deadwood, (iv) </span><span>fresh deadwood volume </span><span>(= Vm3_dec1_Biosoil + Vm3_dec2_Biosoil), and (v) decayed deadwood volume = (= Vm3_dec4_Biosoil + Vm3_dec5_Biosoil).</span></p> <p><span>A few environmental covariates were collected from the Biosoil data: (i) management intensity (grouped into two classes: recently harvested, i.e., with management evidence within the last 10 years; and not recently harvested, i.e., unmanaged (no management evidence) or managed a long time ago (management evidence but more than 10 years previously), (ii) average stand age (separated into 3 classes: mature [>100 yrs], mid-aged [41-100 yrs], young [1-40 yrs]), (iii) elevation (above sea level, a.s.l.), a continuous quantitative variable, (iv) dominant tree genus, and (v) forest type, depending on the dominant tree species: coniferous, deciduous or mixed.</span></p> <p><a name="_Toc175840514"></a><a name="_Toc116027764"></a><span><strong><span>Database joint</span></strong></span><span><strong><span>: <a name="_Toc116027765"></a>plot matching in time series</span></strong></span></p> <p><span>After harmonizing plot names and coordinates in the two datasets (ICP-defoliation and Biosoil-deadwood), only plots with matched data in both datasets were selected. Plots with a maximum of one year’s discontinuity in the data were retained, and the missing values were reconstructed from the average values in contiguous years. Plots with discontinuities in defoliation measurements of more than 2 years were deleted. We matched defoliation measurements for the Biosoil-ICP datasets from 1989 to 2007 and finally obtained 2,070 five-year, 1,804 ten-year and 1,399 fifteen-year time series. This approach made it possible to define three 10-year time series [1995-2005, 1996-2006, 1997-2007] with plots in 17 countries, from five plots in Ireland and nine in the United Kingdom, to 337 plots in Finland and 461 in France.</span></p> <p><a name="_Toc175840515"></a><a name="_Toc116027766"></a><span><strong><span>Calculation of global defoliation metrics</span></strong></span></p> <p><span>We calculated 16 univariate metrics to summarize changes in defoliation throughout the 10-year period prior to the Biosoil deadwood measurements. Some of the selected parameters describe the immediate possible effects of defoliation severity in the recent past on a given year: (i) defoliation level of the previous year (n-1), (ii) defoliation level of the year before the previous year (n-2), (iii) defoliation level of the year two years before the previous year (n-3). Other defoliation metrics relate to the cumulative effects of defoliation levels in the near or the distant past: (i) average defoliation level over the last two years, (ii) average defoliation level over the last three years, (iii) average defoliation level over the last five years, (iv) average defoliation level over the first five years of the 10-year time series, and (v) time elapsed since last peak defoliation. Several other parameters depict general trends in the level of defoliation over the 10-year time series: for cumulative metrics: (i) arithmetic mean of annual defoliation level; (ii) geometric mean of annual defoliation level; (iii) Area Under the defoliation time Curve (AUC), i.e., the cumulative sum of defoliation levels; and for the overall trend: (iv) the estimated slope of the linear regression line for defoliation level over time. Finally, some of the metrics reflect defoliation severity and repetition along the 10-year time series, and their potentially time-lagged effects: (i) maximum defoliation level; (ii) total number of years elapsed after the dieback peak level, whether successive or not; (iii) the number of peaks, consecutive or discontinuous, i.e., the number of severe defoliation events and defoliation frequency; and (iv) duration of the longest peak, i.e., the longest continuous time during which the level of defoliation was greater than the relative threshold.</span></p> <p><span>A peak in defoliation was defined as a year in which the level of defoliation exceeded a relative threshold, i.e., the third quartile value. In our 10-year time series, the peak value was 25% and above. <span><span> </span></span></span></p>
Constructing a database of alien plants in the Himalayas to test patterns structuring diversity
Open the record for dataset details and reuse information.
Regional databases demonstrate macroecological patterns less clearly than systematically collected field data
Open the record for dataset details and reuse information.
Yale Face Database - 2 different types of backdoor patterns (glasses and full-beard) added
<p>Datasets are split in Training</p> <p>To increase the size of the training and test set, the following augmentation<br> techniques are applied: a horizontal flip, and a change of brightness by -60%, -30%, +30% and +60%<br> percent.</p> <p>Each of the brightness changes is done on the original image as well as on thehorizontally flipped image.</p> <p>Backdoors patterns (glasses and full-beard) are added using FaceApp</p>
Supplementary Materials for 'Colexification patterns in Europe: A study of persistence and diffusibility in the lexicon, based on the Database of Crosslinguistic Colexifications (CLICS3)' (Linguistic Typology)
<p>The folder contains the data and scripts used for the article on 'Colexification patterns in Europe: A study of persistence and diffusibility in the lexicon, based on the Database of Crosslinguistic Colexifications (CLICS3)'</p>
Database of socio-territorial patterns of mining tailings in Chile
<p>This database originally retrieves information from the Mining and Geology Service of the Government of Chile. Then, it generates a new database from the use of layers with geographic information, specifically, of water courses, localities and infrastructure concentrations.</p>
Data from: Assessing patterns in introduction pathways of alien species by linking major invasion databases
Open the record for dataset details and reuse information.
Supplementary materials for Gast, V. and M. Koptjevskaja-Tamm, 'Colexification patterns in Europe: A study of persistence and diffusibility in the lexicon, based on the Database of Crosslinguistic Colexifications (CLICS3)
<p>The folder contains the data, scripts and plots for the paper. It includes the following third party material (Open Access) in the folder DataStageI:</p> <p>1) the clics.sqlite database from https://github.com/clics/clics3; cf. Rzymski, Christoph and Tresoldi, Tiago et al. 2019. The Database of Cross-Linguistic Colexifications, reproducible analysis of cross- linguistic polysemies. <a href="https://doi.org/10.1038/s41597-019-0341-x">DOI: 10.1038/s41597-019-0341-x</a><br> 2) the files ccCosineDist.csv and pmiWorld.csv from Jäger (2018), 'Global-scale phylogenetic linguistic inference from lexical resources', Scientific Data 5, Article number: 180189<br> 3) languoid.csv from https://glottolog.org/meta/downloads; cf. Hammarström, Harald & Forkel, Robert & Haspelmath, Martin & Bank, Sebastian. 2020. Glottolog 4.2.1. Jena: Max Planck Institute for the Science of Human History.<br> (Available online at http://glottolog.org, Accessed on 2020-07-11.)<br> * asjp.tsv from https://asjp.clld.org/, cf. Wichmann, Søren, Eric W. Holman, and Cecil H. Brown (eds.). 2018. The ASJP Database (version 18). (current version: 2020)</p>
Code, benchmarks and experiment data for the SoCS 2022 paper "Additive Pattern Databases for Decoupled Search"
<p>This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper.</p> <p>sievers-et-al-socs2022-fast-downward.zip contains the implementation based on Fast Downward. It also contains the experiment scripts compatible with Lab 7.0 for reproducing all experiments of the paper, under experiments/decoupled-abstractions. The scripts 2022-04-* contain configurations for running the experiments and the script paper-tables-*.py gathers the data and produces plots and tables. (Note that some adjustments to the scripts would need to be done because, e.g., the entire tree is not a repository anymore.)</p> <p>sievers-et-al-socs2022-ipc-benchmarks.zip contains the IPC benchmarks. It consists of the STRIPS IPC benchmarks used in all optimal sequential tracks of IPCs up to 2018 (suite optimal_strips from https://github.com/aibasel/downward-benchmarks).</p> <p>sievers-et-al-socs2022-autoscale-benchmarks.zip contains the Autoscale 21.11 benchmarks (from https://github.com/AI-Planning/autoscale-benchmarks).</p> <p>sievers-et-al-socs2022-lab.tar.gz contains a copy of Lab 7.0 (https://github.com/aibasel/lab).</p> <p>sievers-et-al-socs2022-raw-data.zip and sievers-et-al-socs2022-processed-data.zip contain the experimental data. Directories without the "-eval" ending (sievers-et-al-socs2022-raw-data.zip) contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories with the "-eval" (sievers-et-al-socs2022-processed-data.zip) ending contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.</p> <p>Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.</p>
Fig. 1 in A volunteer-populated online database provides evidence for a geographic pattern in symptoms of black spot infections
Fig. 1. Example photos of a blacknose dace, a stoneroller (Campostoma sp.), a creek chub, and a chub (Nocomis sp.) exhibiting evidence of a black grub infection.
Fig. 3 in A volunteer-populated online database provides evidence for a geographic pattern in symptoms of black spot infections
Fig. 3. If an observation of blacknose dace, creek chub, chubs (Nocomis spp.), or stonerollers (Campostoma spp.) is reported on iNaturalist from the watersheds of Northern Lake Erie or Lake Ontario – Niagara Peninsula it is more likely to exhibit macroscopic signs of infection by metacercariae. Also a high percentage of the observations by user bkorol exhibited symptoms, all from Eastern Georgian Bay which is adjacent to the two other watersheds mentioned. Data were analyzed using conditional information trees in r using package caret to train and tune the model. Percentage of actual observations summarized in bar charts below each split that significantly described the data.
Fig. 2 in A volunteer-populated online database provides evidence for a geographic pattern in symptoms of black spot infections
Fig. 2. Watersheds shaded by the percentage of observations with signs of black spot infection for four groups of fishes, each of which show higher prevalence in the watersheds near Toronto and the northern shoreline of Lake Erie. Watersheds are transparent if only 1 or 2 fish were observed in them, otherwise they increase in opaqueness as sample number increase, those with>15 observations are solid. Refer to Table 1 for numbers.
DAS Urban Mobility Pattern Database
<p>DISCONTINUED!!!! New version: https://doi.org/10.5281/zenodo.8068608</p> <p>This database is based on the MINIDAS format designed by the Incorporated Research Institutions for Seismology (IRIS) with the objective of storing information captured by distributed acoustic systems. These files correspond to the hdf5 type, which facilitates their reading.</p> <p>In this same dataset you can find the official script to work with this kind of data (for more information see official repository, https://github.com/DAS-RCN/RCN_DASformat).</p> <p> </p>
Normative Database for Pattern Electroretinogram (PERG) and Flash Electroretinogram (FERG)
ClinicalTrials.gov study NCT02609204. IPD Sharing: NO. Countries: 1. Publications: 0.
Clinical Characteristics and Practice Patterns of Type 2 Diabetes Mellitus Patients Treated With OADs in Japan: Analysis of Medical and Health Care Database of the MDV
ClinicalTrials.gov study NCT03092752. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Database used in the article: The consumption of alcohol by adolescent schoolchildren: Differences in the triadic relationship pattern between rural and urban environments.
<p>Database used in the study: The consumption of alcohol by adolescent schoolchildren: Differences in the triadic relationship pattern between rural and urban environments.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.