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858 results for “monitoring data”
Data from Open Research Data: SNSF monitoring report 2017-2018
<p>This file collection is part of Open Research Data: SNSF monitoring report 2017-2018 (doi 10.5281/zenodo.3618123). This report gives a first overview on the research data management practices adopted by researchers since the introduction of the SNSF Open Research Data Policy in 2017. </p> <p>Please cite this data collection as:<br> Milzow, Katrin; von Arx, Martin; Sommer, Cornélia; Cahenzli, Julia and Perini, Lionel (February 2020). Data from Open Research Data: SNSF monitoring report 2017-2018. Zenodo (10.5281/zenodo.3618209)</p> <p>Further information is given in the corresponding monitoring report<br> Milzow, Katrin; von Arx, Martin; Sommer, Cornélia; Cahenzli, Julia and Perini, Lionel (February 2020). Open Research Data: SNSF monitoring report 2017-2018. Zenodo (10.5281/zenodo.3618123)</p> <p> </p> <p><strong>Contact</strong></p> <p>Swiss National Science Foundation (SNSF)</p> <p>Open Research Data group</p> <p>E-mail: ord@snf.ch</p>
Brazil STAR Project Microgrid Monitoring Data
<p>Contains monitoring data from microgrids deployed in rural areas of the State of Amazonas, Brazil. The data collections started in January of 2018 and ended in March 2019.</p> <p>Contained in the folder CSVs.zip are data from four housing units (denoted as STAR-A, STAR-B, STAR-C and STAR-D) consisting of:<br> 1) Battery information,<br> 2) Solar photovoltaic (PV) generation information, and<br> 3) Measurements from individual appliance monitors (IAMs).</p> <p>Folders are named in the format YYYY-MM-DD-STAR-X_SYS, where<br> 1) YYYY is the year,<br> 2) MM is the month,<br> 3) DD is the day,<br> 4) STAR-X is indicator for the housing unit, i.e., STAR-A, STAR-B, STAR-C, or STAR-D.<br> 5) SYS denotes the "system" and refers to one of the following: Battery, PV, or IAM.</p> <p>The files YYYY-MM-DD-STAR-X_Battery contain the following information in their columns in the order presented:<br> 1) Timestamp<br> 2) Battery current (mA)<br> 3) Ampere-hours (mAh)<br> 4) State of Charge (SOC) (%)<br> 5) Number of charge cycles<br> 6) Number of full discharges<br> 7) Battery voltage (mV)<br> 8) Battery power (W)<br> 9) Amount of discharge energy (0.01 kWh)<br> 10) Amount of charge energy (0.01 kWh)</p> <p>The files YYYY-MM-DD-STAR-X_PV contain the following information in their columns in the order presented:<br> 1) Timestamp<br> 2) Panel voltage (mV)<br> 3) Panel power (W)<br> 4) Energy generated (0.01 kWh)<br> 5) Battery state of operation: 0(Off); 1(Low power); 2(Fault); 3(Bulk); 4(Absorption); 5(Float); 6(Inverting)</p> <p>The files YYYY-MM-DD-STAR-X_IAM contain the following information in their columns in the order presented:<br> 1) Timestamp<br> 2) Real power (W)<br> 3) Reactive power (W)<br> 4) Voltage (unknown unit)<br> 5) IAM ID</p>
Data from: DNA metabarcoding for biodiversity monitoring in a national park: screening for invasive and pest species
<ol> <li><span>DNA metabarcoding was utilized for a large-scale, multi-year assessment of biodiversity in Malaise trap collections from the Bavarian Forest National Park (Germany, Bavaria). </span></li> <li><span>Principal Component Analysis of read count-based biodiversities revealed clustering in concordance with whether collection sites were located inside or outside of the National Park.</span></li> <li><span>Jaccard distance matrices of the presences of BINs at collection sites in the two survey years (2016 and 2018) were significantly correlated.</span></li> <li><span>Overall similar patterns in the presence of total arthropod BINs, as well as BINs belonging to four major arthropod orders across the study area, were observed in both survey years, and are also comparable with results of a previous study based on DNA barcoding of Sanger-sequenced specimens.</span></li> <li><span>A custom reference sequence library was assembled from publicly available data to screen for pest or invasive arthropods among the specimens or from the preservative ethanol.</span></li> <li> <span>A single 98.6% match to the invasive bark beetle </span><span>Ips duplicatus</span><span> was detected in an ethanol sample. This species has not previously been detected in the National Park.</span> </li> </ol>
Greenland Peregrine Falcon eggshell thickness monitoring data 1972 - 2019
<p>The Peregrine Falcon (Falco peregrinus tundrius) population in Greenland has been monitored in different survey areas in South and West Greenland since 1972. At visits to Peregrine Falcon nests, eggshell fragments from hatched eggs as well as addled (dead) eggs left behind have been collected with the aim of monitoring the thickness of the eggshells as well as analysing the whole eggs for contaminants. The shell thickness serves as a proxy for the falcons’ exposure to certain persistant organic pollutants, in particular DDT and its breakdown products (see summaries in Cade et al. 1988).</p> <p>This data set contains the raw data on 6665 eggshell thickness measurements of:<br> 1. Whole eggs from South Greenland 1986-2015<br> 2. Eggshell fragments from the study area in South Greenland 1981-2019<br> 3. Eggshell fragments from the study area around Kangerlussuaq in West Greenland 1972-1989</p> <p>The data set contains a mix of measurements of shell thickness including or excluding the eggshell membranes from the same clutch of eggs. Based on those measurements the average membrane thickness is 0.071 mm (SD=0.013) – a figure confirmed by other studies – and this ’membrane factor’ can be added or subtracted for comparisons with other data sets.</p> <p>Further details regarding the sampling areas, measurement methods and the results of trends analyses of changes in shell thickness are provided in Falk et al. (2006 and 2018).</p> <p>The file named <em>1_Data_Eggshell_Thickness_1972-2019.csv</em> contains the raw measurements data and the file <em>2_ReadMe_Eggshell_Thickness_1972-2019.txt</em> specifies the content. </p> <p>The file named <em>3_Rscript_Eggshell_Thickness_1972-2019.R</em> provides an R script for summarizing and plotting the data as shown in the file <em>4_Plot_Eggshell_Thickness_1972-2019.pdf</em></p>
Data for Wang and Kent, GRL, 2021, "RESET: A method to monitor thermoremanent alteration in Thellier-series paleointensity experiments"
<p>This zip file contains data presented in the GRL paper “RESET: A method to monitor thermoremanent alteration in Thellier-series paleointensity experiments” by Wang and Kent, 2021. </p> <p>Folder “Figure2e” contains a subfolder that contains all the raw hysteresis measurements (with heated temperatures in degree Celsius in file names) and an Excel spreadsheet that summarizes the hysteresis parameters, which are used in plotting the Day diagram as shown in Figure 2e.</p> <p>Folder “Figure2f-s” contains raw FORC data of specimens GA79.5y and GA84.6y measured after each heating treatment (with heated temperatures in degree Celsius in file names) that are used in plotting the FORC diagrams as shown in Figure 2f to 2s.</p>
Data for: A Comparison of the Influence of Different Multi-Core Processors on the Runtime Overhead for Application-Level Monitoring
<p>Application-level monitoring is required for continuously operating software systems to maintain their performance and availability at runtime. Performance monitoring of software systems requires storing time series data in a monitoring log or stream. Such monitoring may cause a significant runtime overhead to the monitored system.</p> <p>In this paper, we evaluate the influence of multi-core processors on the overhead of the Kieker application-level monitoring framework. We present a breakdown of the monitoring overhead into three portions and the results of extensive controlled laboratory experiments with microbenchmarks to quantify these portions of monitoring overhead under controlled and repeatable conditions. Our experiments show that the already low overhead of the Kieker framework may be further reduced on multi-core processors with asynchronous writing of the monitoring log.</p> <p>Our experiment code and data are available as open source software such that interested researchers may repeat or extend our experiments for comparison on other hardware platforms or with other monitoring frameworks.</p> <p>This dataset supplements the paper and contains the raw experimental data as well as several generated diagrams for each experiment.</p>
Data for: A Benchmark Engineering Methodology to Measure the Overhead of Application-Level Monitoring
<p>Application-level monitoring frameworks, such as Kieker, provide insight into the inner workings and the dynamic behavior of software systems. However, depending on the number of monitoring probes used, these frameworks may introduce significant runtime overhead. Consequently, planning the instrumentation of continuously operating software systems requires detailed knowledge of the performance impact of each monitoring probe.</p> <p>In this paper, we present our benchmark engineering approach to quantify the monitoring overhead caused by each probe under controlled and repeatable conditions. Our developed MooBench benchmark provides a basis for performance evaluations and comparisons of application-level monitoring frameworks. To evaluate its capabilities, we employ our benchmark to conduct a performance comparison of all available Kieker releases from version 0.91 to the current release 1.8.</p> <p>This dataset supplements the paper and contains the raw experimental data as well as several generated diagrams for each experiment.</p>
Data for: Performance Benchmarking of Application Monitoring Frameworks
<p>Application-level monitoring of continuously operating software systems provides insights into their dynamic behavior, helping to maintain their performance and availability during runtime. Such monitoring may cause a significant runtime overhead to the monitored system, depending on the number and location of used instrumentation probes. In order to improve a system’s instrumentation and to reduce the caused monitoring overhead, it is necessary to know the performance impact of each probe.<br /> While many monitoring frameworks are claiming to have minimal impact on the performance, these claims are often not backed up with a detailed performance evaluation determining the actual cost of monitoring. Benchmarks can be used as an effective and affordable way for these evaluations. However, no benchmark specifically targeting the overhead of monitoring itself exists. Furthermore, no established benchmark engineering methodology exists that provides guidelines for the design, execution, and analysis of benchmarks.<br /> This thesis introduces a benchmark approach to measure the performance overhead of application-level monitoring frameworks. The core contributions of this approach are 1) a definition of common causes of monitoring overhead, 2) a general benchmark engineering methodology, 3) the MooBench micro-benchmark to measure and quantify causes of monitoring overhead, and 4) detailed performance evaluations of three different application-level monitoring frameworks. Extensive experiments demonstrate the feasibility and practicality of the approach and validate the benchmark results. The developed benchmark is available as open source software and the results of all experiments are available for download to facilitate further validation and replication of the results.</p> <p>This dataset supplements the thesis and contains the results of all experiments, including the raw result data, the results of additional experiments, and the configuration of our benchmarks.</p>
SNSF Open Access Monitoring: Data Publications 2013-2014
<p>This spreadsheet contains supplemental data to the report: "Open Access to Publications: SNSF monitoring report 2013 - 2015" (http://doi.org/10.5281/zenodo.584131), where it was found that at least 39% of publications out of SNSF funded grants are either Gold or Green Open Access.</p> <p>The initial data is based on the reported publications to the Swiss National Science Foundation via the grant administration system mySNF as of <strong>11. September 2015.</strong> This data is also available as open data on p3.snf.ch. Please be aware, that grant beneficiaries can update the metadata to the reported publications, including the Open Access status anytime during and after the end of a grant.</p> <p>Prior to validation, the actual publication date of publications with “in press/accepted” status was verified insofar as possible, and the details updated (approx. 3'000 publications). For further processing only publications with <strong>publication date 2013-2014</strong> were considered. Publications with the status “in press/accepted” status were excluded. Duplicates, i.e. publications cited as output for more than one project (approx. 2'500) are also filtered out. Where identifiable as such, pure abstracts (e.g. in journal supplements), working papers and pre-prints were also excluded.</p> <p><br> The basic goal was to locate a DOI for the publication and update the metadata. 78% of the 17'420 publications scrutinised have in the meantime been assigned a DOI. Using the DOI and the linked metadata available in CrossRef, unique searches could be performed in additional sources such as DOAJ, Pubmed and Pubmed Central, OpenAIRE (repository aggregator) and ADS (Astrophysics Data System). After the initial search for open access full text in these external sources, a search was made in Google Scholar using the DOI of the remaining publications still to be validated and links to any open access full text were extracted. Where even the Google Scholar search failed to turn up open access full text, the status was set to “closed access”. The OA status of 4'212 publications from the period 2013 to 2014 remained unclear, but it is more likely that they are in closed rather than open access mode.</p>
Data and results for manuscript "Multi-frequency electrical impedance tomography as a non-invasive tool to characterize and monitor crop root systems "
<p>Root systems are essential in nutrient uptake and translocation, but are difficult to characterize non-invasively with existing methods. We propose electrical impedance tomography (EIT) as a new tool for the imaging and monitoring of crop root systems. In a laboratory experiment we demonstrate the capability of the method to capture physiological responses of root systems with high spatial and temporal resolution. We conclude that EIT is a promising functional imaging technique for crop roots.</p> <p>This package contains measured raw EIT data, electrical imaging results, spectral results from the Debye decomposition, and the Python scripts used to generate the plots in the manuscript.</p>
Data and results for manuscript "Flow dynamics in hyper-saline aquifers: hydro-geophysical monitoring and modeling"
<p>The paper presents a general methodology that will help understand how freshwater and saltwater may interact in natural porous media, with a particular view at practical applications such as the storage of freshwater underground in critical areas such as semi-arid zones around the Mediterranean sea. The methodology is applied to a case study in Sardinia and shows how a mix of advanced monitoring and mathematical modeling tremendously advance our understanding of these systems.</p> <p>This package contains the raw cross-hole time-lapse ERT data, additional field data, the ERT inversion results of the field data as well as the modeling data in terms of the concentration distribution of the density-dependent flow and transport model and the inverted synthetic ERT monitoring results.</p>
Research data supporting "Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy"
<p>Research data supporting the publication:</p> <p>M. Bergholt, 2017, Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy, Biomaterials, Volume 140, September 2017, Pages 128–137, DOI: 10.1016/j.biomaterials.2017.06.015</p>
Data set from ambient vibration monitoring and static loading of a steel stringer bridge subject to imposed damage
<p>This data set contains structural response measurements acquired from the Route 345 Bridge over Big Sucker Brook in Waddington, NY prior to demolition and replacement. This steel stringer bridge was instrumented with dual-axis accelerometers and strain transducers and response measurements were obtained prior to and following mechanically imposed damages. Accelerometer measurements were obtained under vehicle passes and strain measurements were acquired during static loading of the span with a truck positioned at three prescribed locations. All measurement data is contained in a single h5-file (hierarchical data format version 5). The data is shared with the intent of promoting the advancement of structural health monitoring and vibration-based damage detection. </p> <p><strong>Version 2 Note: </strong>This version corrects the strain measurement data. Due to an index counter error in the script that compiled the strain measurement data into the h5-file, the strain measurement data in the Version 1 data were incorrectly sourced from a single scenario. The strain measurements are corrected in this new version. No other changes were made in the h5-file.</p>
US National Native Bee Monitoring RCN Data Management Workshop: CC BY NC Videos
<p>The US National Native Bee Monitoring Research Coordination Network (RCN) held a two-day workshop on data management best practices for native bee inventory, survey, and monitoring data on March 28 and 30, 2023. Videos in this data set were played at the workshop. These videos are released with a CC BY-NC license. These videos are for non-commercial use only. Please cite the presenter(s) of the video(s) you use. This data set includes the following videos: </p><ul><li>APHIS National Honey Bee Pests and Diseases Survey by Anne LeBrun</li><li>Importance of FAIR in Biodiversity Science and Research by Elizabeth R. Ellwood</li><li>Bugflow: A Community Driven Repository for Entomology Digitization Resources by Crystal Maier</li><li>A Primer on iNaturalist Bee Data by Keng-Lou James Hung, Paige Chesshire, Michael Orr, Alice Hughes, Jess Mullins, Katherine Parys, Patricia Simpson, Lindsie McCabe, Neil Cobb, and John Ascher</li></ul>
Data supporting the Master thesis "Monitoring von Open Data Praktiken - Herausforderungen beim Auffinden von Datenpublikationen am Beispiel der Publikationen von Forschenden der TU Dresden"
<p>Data supporting the Master thesis "Monitoring von Open Data Praktiken - Herausforderungen beim Auffinden von Datenpublikationen am Beispiel der Publikationen von Forschenden der TU Dresden" (Monitoring open data practices - challenges in finding data publications using the example of publications by researchers at TU Dresden) - Katharina Zinke, Institut für Bibliotheks- und Informationswissenschaften, Humboldt-Universität Berlin, 2023</p> <p>This ZIP-File contains the data the thesis is based on, interim exports of the results and the R script with all pre-processing, data merging and analyses carried out. The documentation of the additional, explorative analysis is also available. The actual PDFs and text files of the scientific papers used are not included as they are published open access.</p> <p>The folder structure is shown below with the file names and a brief description of the contents of each file. For details concerning the analyses approach, please refer to the master's thesis (publication following soon).</p> <p>## Data sources </p> <p>Folder 01_SourceData/</p> <p>- PLOS-Dataset_v2_Mar23.csv (PLOS-OSI dataset)</p> <p>- ScopusSearch_ExportResults.csv (export of Scopus search results from Scopus)</p> <p>- ScopusSearch_ExportResults.ris (export of Scopus search results from Scopus)</p> <p>- Zotero_Export_ScopusSearch.csv (export of the file names and DOIs of the Scopus search results from Zotero)</p> <p>## Automatic classification </p> <p>Folder 02_AutomaticClassification/</p> <p>- (NOT INCLUDED) PDFs folder (Folder for PDFs of all publications identified by the Scopus search, named AuthorLastName_Year_PublicationTitle_Title) </p> <p>- (NOT INCLUDED) PDFs_to_text folder (Folder for all texts extracted from the PDFs by ODDPub, named AuthorLastName_Year_PublicationTitle_Title)</p> <p>- PLOS_ScopusSearch_matched.csv (merge of the Scopus search results with the PLOS_OSI dataset for the files contained in both)</p> <p>- oddpub_results_wDOIs.csv (results file of the ODDPub classification)</p> <p>- PLOS_ODDPub.csv (merge of the results file of the ODDPub classification with the PLOS-OSI dataset for the publications contained in both)</p> <p>## Manual coding </p> <p>Folder 03_ManualCheck/</p> <p>- CodeSheet_ManualCheck.txt (Code sheet with descriptions of the variables for manual coding)</p> <p>- ManualCheck_2023-06-08.csv (Manual coding results file)</p> <p>- PLOS_ODDPub_Manual.csv (Merge of the results file of the ODDPub and PLOS-OSI classification with the results file of the manual coding)</p> <p>## Explorative analysis for the discoverability of open data<br> <br>Folder04_FurtherAnalyses </p> <p>Proof_of_of_Concept_Open_Data_Monitoring.pdf (Description of the explorative analysis of the discoverability of open data publications using the example of a researcher) - in German</p> <p>## R-Script </p> <p>Analyses_MA_OpenDataMonitoring.R (R-Script for preparing, merging and analyzing the data and for performing the ODDPub algorithm)</p>
Supplementary Table for Earth observation data-driven cropland soil monitoring: A review
<p>Table including 46 manuscripts written in English referring to topsoil monitoring related to Earth observation data-driven cropland soil monitoring: A review paper.</p>
Data from: Monitoring microarthropods assemblages along a pH gradient in a forest soil over a 60 years' time period
<p>The goal of this study was to assess the development, over 60 years, of microarthropod communities over a pH gradient in forest soil.</p> <p>Site Description</p> <p>Hackfort is an oak coppice grove in the East-Southeast of the city of Zutphen in the province of Gelderland, the Netherlands, 52°06′09.7″ N, 6°15′56.0″ E (see Figure 1). The experimental area is about 1.5 ha and is divided in a 10 m × 10 m grid. Vegetation is dominated by common oak (<em>Quercus robur</em>), mixed with birch (<em>Betula pendula</em>), and had in 1959, an understory of wood sage plugs (<em>Teucrium scorodonia</em>), wood anemone (<em>Anemone nemorosa</em>), bracken (<em>Pteridium aquilinum</em>), and wavy-hair grass (<em>Deschampsia flexuosa</em>). In later years, the understory became more dominated by bramble species (<em>Rubus fruticosus </em>and<em> R. idaeus</em>) and common nettles (<em>Urtica dioica</em>) at the edges of the forest, due to increased N deposition from adjacent farmland. The forest is situated at the transition from western riverine deposits and eastern periglacial cover sands. The soil is a riverine deposit with a few elevation differences, making a number of gradients in clay and loam content, which results in many short-distance gradients in soil types, varying from typic haplaquolls with the largest loam contents, via psammaquentic haplorthods to humaqueptic spodic psammaquents, slightly elevated and low in loam contents.</p> <p>Microarthropod Sampling and pH Measurement</p> <p>In 1959, samples were taken at three subsequent dates: 11 September, 9 October, and 30 October. Samples in 1987 were taken on one date, 9 October, just as on 30 October 2019. Samples were taken following a standard procedure, developed at the Institute for Applied Biological Research in Nature, Wageningen, the Netherlands (later merged into the Research Institute for Nature management, Institute for Forestry and Nature Research and Alterra resp., now known as Wageningen Environmental Research); this procedure has been published by Siepel and van de Bund in 1988 (Siepel and van de Bund, 1988). Each mineral soil sample has 100 cc: a volume of 5 cm diameter and 5 cm depth plus litter on top. In 1959, two samples per date were taken on each plot, making a total of 6 samples (only pooled data are available); in 1987 and in 2019, 4 and 5 samples for each plot were taken on, respectively (data per sample available).</p> <p>Soil cores were put on a Tullgren funnel for 1 week, during which temperature was increased from 35 to 45 °C, and then, microarthropods were collected in 70% alcohol and later put into 20% lactic acid for clarification and identification (Siepel, 1990; Siepel and van de Bund, 1988). The Tullgren funnel used for extraction (Siepel, 1990) has been used ever since 1936 and efficiency has not changed as the tool and protocol was the same all over the years.</p> <p>Identification was done to the species level as much as possible using at present the keys for Oribatida(Weigmann and G., 2006), for Gamasina (Lehtinen, 1994), for Uropodina (Karg, 1989), and for Collembola (Hopkin, 2007). Material from the extractions of 1959 and 1987 was re-examined as far as possible to check the correct species identification. In the 1959 and 1987 samples, only oribatid mites were identified to the species level, whereas in 1959, all species of <em>Quadroppiidae, Oppiidae</em>, and <em>Suctobelbidae</em> were pooled. In 2019, all microarthropods were identified to the species level.</p> <p>Sorting and identification of the 1959 microarthropods was carried out by an experienced acarologist (J.G. de Gunst), in 1987, this was done by a student (C. Arnold) and completed and checked by the second author. For the 2019 samples, we decided to demonstrate the potential difference in picking out the microarthropods from the extraction fluid into the slides for identification as part of the experiment: the first author made a first series of slides including all distinguished animals (dataset 2019 a), while the second author made an extra set of slides with the animals missed by the first (dataset 2019 b). The first author did know since the beginning that the second author would check all samples after her sorting session. In this way, we intended to demonstrate the potential difference in this crucial part of the procedure by a starting and an experienced professional. In the analysis, we compare dataset (2019 a) with (2019 a + b), in order to highlight the difference between a starting and an experienced acarologist. Nomenclature adopted was updated according to current standards, following, e.g., the checklists for Oribatida (Siepel et al., 2009), for Astigmatina (Siepel et al., 2016), and for Mesostigmata (Siepel, 2018). Values of pH-KCl were measured in the core material after the extraction of the microarthropods, both in 1959, 1987, and 2019.</p> <p> </p> <p>We have four data files:</p> <p>1959 hackfort microarthropods data.csv</p> <p>1989 hackfort microarthropods data.csv</p> <p>2019 hackfort microarthropods data.csv</p> <p>pH data Hackfort 1959-2019.csv.</p> <p> </p> <p>Explanation of the variables in the datasets:</p> <p>higher taxon: Oribatida, Astigmata, Mesostigmata, Prostigmata, Collembola or Protura</p> <p>Name in De Gunst 1959: taxonomic identification by De Gunst in 1959</p> <p>Valid name: Henk Siepel re-checked these species names in 2019</p> <p>Plot: plot 1, plot 2, plot 3, plot 4, plot 5</p> <p>a: identified by Yuxi Guo</p> <p>b: re-checked by Henk Siepel from remaining soil microarthropods in slide</p> <p>pH(KCL) and pH(H2O): pH values based on indicated methods</p> <p> </p>
Molecular data from "Between a rock and a dry place: phylogenomics, biogeography, and systematics of ridge-tailed monitors (Squamata: Varanidae: Varanus acanthurus complex)"
<p><strong>Phylogenetic_dataset.csv</strong>: Unfiltered DArTseq data used in phylogenetic analyses. Readable by 'dartR' (Gruber et al. 2018).</p> <p><strong>Population_dataset.csv</strong>: Unfiltered DArTseq data used in population-level analyses. Readable by 'dartR' (Gruber et al. 2018).</p> <p><strong>Reference.csv</strong>: Spreadsheet listing individuals included in molecular analyses. Includes vouchers, species, name of each sample in DArTseq data sets, and GenBank accession numbers (GB) for mitochondrial data. ABTC stands for Australian Biological Tissue Collection; AA and CCM for field numbers of uncatalogued specimens. Other collection acronyms follow Sabaj (2019). We refrain from assigning individuals that were not included in the molecular analyses to any given species.</p>
Data of microbiological decomposition monitoring of pine coniferous litter in the soils at the Moscow region by the ICP IM method
<p>Данные мониторинга микробиологического разложения хвойного опада сосны в почвах Московской области по методу ICP IM</p> <p>Data of microbiological decomposition monitoring of pine coniferous litter in the soils at the Moscow region by the ICP IM method</p> <p> </p> <p>Исследования проведены на базе двух особо охраняемых природных территориях (далее ООПТ) в Московской области и г. Москве, расположенных на расстоянии 90 км друг от друга. На обеих ООПТ работы проводились на постоянных пробных площадках (ППП) площадью 1 га.</p> <p>Эталон – Приокско-Террасный государственный природных биосферный заповедник. В заповеднике заложены 4 ППП, расположенные в бассейне малой реки Тоденка, большая часть бассейна которой находится в границах Заповедника. ППП расположены не далее 2 км от русла реки, в преобладающих по площади в ООПТ типах лесах: сосняке сложном (две ППП), березняке сложном широкотравном и дубраве широкотравной. В сосняках Заповедника были заложены 2 ППП, различающиеся по увлажнению и месторасположению, на террасе и на коренном берегу.</p> <p>Модельная находится под более высокой антропогенной нагрузкой в г. Москве с лесопарковая часть природно-исторического парка «Кузьминки-Люблино» (далее – Лесопарк), имеющая также схожий с Заповедником рельеф и породный состав лесов. Территории Заповедника и Лесопарка имеют сходство физико-географических условий формирования: обе ООПТ расположены на надпойменных террасах крупных рек Волжского бассейна (рек Оки и Москвы соответственно), на обеих территориях формируются слабо дифференцированные дерново-подзолистые почвы ржавоземы на флювиогляциальных песках (Brunic Arenosols) под сосновыми и березовыми лесами. Основные физико-химические свойства почв Лесопарка соответствуют естественным аналогам.</p> <p><strong>Метод изучения скорости разложения опада.</strong> Методической основой проводимых измерений скорости биоразложения опада является метод подпрограммы «MB Microbial decomposition» программы ICP IM [https://www.syke.fi/en-US/Research__development/Nature/Monitoring/Integrated_Monitoring/Manual_for_Integrated_Monitoring] с некоторыми изменениями.</p> <p>Изменения методики касались срока экспозиции. Мы использовали стандартный срок экспозиции в 1 год и отказались от экспозиции иголок более 1 года, как рекомендовано в методике ICP IM. Такая модификация метода позволила существенно сократить трудозатраты при получении сравнимых результатов.</p> <p>Измерение разложения опада проводились методом закладки конвертов из нейлоновой сетки 8 на 8 см с ячеей 1 мм с упакованными в них пробами на срок 1 год. Конверты запечатывались металлическими скобами. Пробы закладывались и снимались в последней декаде октября – начале ноября. В таблицах и тексте год указывается по году снятия образца, соответственно, пробы, заложенные в 2013 г. и собранные в 2014 г. относятся к 2014 г.</p> <p>Согласно методике, в экспериментах по изучению разложения опада использовались иголки сосны обыкновенной (<em>Pinus</em> <em>sylvestris</em> L.), которые собирались с ветвей невысоких деревьев (10–20 лет) в одном и том же квартале Заповедника и только пожелтевшие, перед их массовым опадом (обычно в начале октября). Предварительно все пробы высушивались до абсолютно сухого веса при 85 <sup>0</sup>С и взвешивались перед упаковкой в конверты. Взвешивание осуществлялось с точностью до 0.001 г, вес проб соснового опада 1 г.</p> <p>Конверты каждый год раскладывались на каждой ППП размерами 100×100 м возле углов и в центре пробных площадей в пределах квадрата 3×3 м в фиксированных точках. Конверты располагались в верхних 5 см почвы, под углом в 15<sup>0</sup>, под моховым покровом (при его наличии) или подстилкой и привязывались леской для удобства поиска. Не допускалось размещение конверта в приствольном круге деревьев. После экспозиции в течение 12 месяцев (350–380 суток) конверты снимались, а содержимое проб аккуратно с использованием пинцета промывалось дистиллированной водой от твердых частиц субстрата и мицелия грибов до остатков иголок и высушивалось до абсолютно сухого веса. Измерение потери массы каждой пробы проводилось методом взвешивания с точностью 0.001 г. Ежегодно закладывалось по 5 проб на каждой ППП в сосняке и березняке, но через год не всегда удавалось найти все заложенные конверты, так как происходили ветровалы и иные нарушения. На ППП в дубняке закладывалось по 10 конвертов.</p>
Data set from long-term wind and acceleration monitoring of the Gjemnessund Bridge
<p>The Gjemnessund Bridge has been monitored by accelerometers and anemometers for almost ten years. The data collected between 2013 and 2018 are now available in this open-access research entry, for free access and download. The data is collected in two h5-files (hierachical data format), with sampling rates 2 Hz and 10 Hz, downsampled from the raw sampling rate of 200 Hz. Some minimal signal processing is applied to the data in line with that applied to the Hardanger Bridge data described in Fenerci et al. (2021) and the Bergsøysund Bridge data described in Kvåle et al. (2022). The structure of the data is identical to that of the latter reference, which is described in a preprint appended to that research entry. The Python package opyndata available on GitHub contains useful tools compatible with the format of the dataset, for data import, processing and visualization.</p>
ScienceDex guides
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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.