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STAR4BBS D3.2 Report on additional indicators of monitoring system_Appendix 6.3 Content Level Matrix dataset
<p>This dataset contains the final set of indicators selected for the content level of the new monitoring system. It is part of the D3.2 "Report on additional indicators of monitoring system" of the STAR4BBS Project (Appendix 6.3). The list of indicators are accompanied by guidance notes, the specific sector and value chain to which they apply as well as potential examples of indicators are suggested. </p>
Introduction to static monitoring - PAMGuard tutorial dataset
<h1>Introduction to static monitoring - PAMGuard tutorial dataset</h1> <h2>GENERAL INFORMATION</h2> <p>This is a dataset to be used with the PAMGuard tutorial <em>Introduction to static monitoring in PAMGuard</em>. The dataset is from a single autonomous acoustic logger (SoundTrap) recording off the Scottish West Coast and contains a wealth of acoustic data including dolphin whistles and clicks, sonar, baleen whales and porpoises. The tutorial text and other related files can be found on the <a href="https://github.com/PAMGuardLearning/Intro2Pamguard">PAMGuardLearning GitHub page</a> or<a href="https://www.pamguard.org/69_Tutorials.html"> PAMGuard website</a>.</p> <h3>Year of data collection:</h3> <p>2018</p> <h3>Geographic location of data collection:</h3> <p>Stanton Banks, Scotland</p> <h3>Funding sources that supported the collection of the data:</h3> <p>The COMPASS project has been supported by the EU’s INTERREG VA Programme, managed by the Special EU Programmes Body. The views and opinions expressed in this document do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB).</p> <h3>Recommended citation for this dataset:</h3> <p>Risch, D., Quer, S., Edwards, E., Beck, S., Macaulay, J., Calderan, S. (2018). Acoustic data from the Scottish west coast recorded with a single-element recording unit doi: 10.5281/zenodo.13880212</p> <h2>DATA & FILE OVERVIEW</h2> <h3>Description of dataset</h3> <p>The dataset contains three days of sample recordings from a deployment of an acoustic recording device (SoundTrap ST300) with a battery pack. The SoundTrap was running an on-device click detector on data at 576kHz sample rate and saving 96kHz raw acoustic data. The click detector detects any transient sound and saves a snippet of the waveform. </p> <p>The dataset also contains processed data from the entire deployment. There data have been processed in PAMGuard software (www.pamguard.org) for low frequency moans, dolphin whistles, clicks noise and longterm spectral averages. The detection and soundscape data has been saved in and raw acoustic recordings discarded. </p> <h3>Files</h3> <p>There are two directories - _audio_ and _viewer_. *audio* contains .sud files whicha re compressed audio and metadata files. They contain raw audio, click detection data and metadata. Data from sud files can be decompressed using SoundTrap Host software (www.oceaninstruments.co.nz). </p> <p>The _viewer_ folder contains a PAMGuard database and associated detection files in the _PAMBinary_ folder and sub folders. The database contains PAMGuard settings and some basic metadata. The detection .pgdf files are non human readable files that contain detection data such as detected clicks, frequency contours of whistles, moans and other tonal sounds and a time series of soundscape metrics. These files can be opened with PAMGuard software (www.pamguard.org), MATLAB (https://github.com/PAMGuard/PAMGuardMatlab) and R (https://github.com/TaikiSan21/PamBinaries). </p> <p>The folder structure is as follows</p> <p><code>├── audio #Three example days of acoustic recordings</code><br><code>│ ├── D2_SB_189411</code><br><code>│ ├── D2_SB_189415 </code><br><code>│ └── D2_SB_189428</code><br><code>├── README.md #The readme file</code><br><code>└── viewer #Processed data from a whole deployment</code><br><code> ├── compass_database_D2_stanton.sqlite3 #PAMGuard database</code><br><code> └── PAMBinary</code><br><code> ├── 20180301 #Folders containing detection files</code><br><code> ├── 20180302</code><br><code> ├── '''</code></p> <h2>SHARING AND ACCESS</h2> <p>These data are open source under Creative Commons Attribution 4.0 International. This means that these data can be used in other tutorials as long as the original authors are credited. Using these data for scientific purposes requires the permission of the authors. </p>
Wind turbine blade structural health monitoring dataset
<p>The dataset is related to a unique experiment conducted at ETH Zurich in collaboration with the Institute of Fluid Flow Machinery, Polish Academy of Sciences. The synchronisation between fatigue loading and guided wave excitation and sensing is unique. The dataset can be used to construct and test damage indexes for structural health monitoring.</p> <p>The tests were carried out on a Sonkyo Windspot 3.5 kW wind turbine blade equipped with strain gauges, a thermocouple, and five piezoelectric transducers. One piezoelectric transducer was used for Hann windowed sine excitation whereas the remaining piezoelectric transducers were used as sensors. The fatigue loading was induced by using a 1 kN capable Tira shaker. The fatigue program is explained in the readme.txt file and involves overloading the blade with a crane up to the blade's failure. The shaker was excited by a sine signal of frequency around the first resonant frequency of the wind turbine blade. The synchronisation with guided wave excitation was realised during three characteristic moments: (1) at maximum amplitude of sine, (2) at zero crossing, and (2) at the minimum amplitude of sine. This stage of the experiment is called 'dynamic' for short, and the data is stored in respective 'raw' folders. After each set of 1000 cycles, the shaker was stopped until the blade stopped vibrating. Then another set of guided wave measurements was taken at the blade's rest position. This stage of the experiment is called 'static' for short, and the data is stored in respective 'average' folders. It contains signals averaged over 10 measurements. During the whole process strain as well as temperature were measured.</p> <p>Three files are included for data visualization: (1) 'plot_strain_temperature.m', (2) 'read_plot_static.m', and (3) 'read_plot_dynamic.m'. These are MATLAB scripts showing how to load data and visualize the dependence of strains and temperatures on fatigue cycle number or time, plot exemplary signals of guided waves, and construct a damage index for structural health monitoring of the wind turbine blade.</p> <p>The details of experimental setup can be found in the paper.</p>
Subsurface monitoring of Arbuckle Fluid Pressure
<p>This is the result of a pressure monitoring project in the Oklahoma Arbuckle Scoop-Stack from August 2016 to March 2020. The data is stored in an h5 file format, and there is an accompanying python script that can be used to decompress the data.</p>
Monitoring knowledge, risk perceptions, preventive behaviours and trust to inform pandemic outbreak response.
<p>The study is part of the large project promoted by WHO Regional Office for Europe called “<em>Monitoring knowledge, risk perceptions, preventive behaviours and trust to inform pandemic outbreak response</em>” and carried out in over 30 countries of the WHO European Region (Registered ISRCTN on 11/05/2021, ID: ISRCTN26200758). In Italy, the survey was conducted administering an online questionnaire developed <em>ad hoc</em> by the WHO in four waves (January-May 2021) to a sample of 10.000 individuals aged 18-70 years. A detailed sampling plan was developed to obtain a representative sample of the Italian adult population. The following variables were taken into account for stratification of the participants: gender by age (four age groups: 18-34 years, 35-44 years, 45-54 years, 55-70 years); geographical area (four areas: North West, North East, Centre, South and Islands); size of living centers (two classes: above and below 100,000 inhabitants); level of education (up to lower middle school, beyond lower middle school); and employment situation (employed, not employed). At the end of each survey’s wave, a weighting procedure has been applied to accurately restore the proportionality of the total sample examined with the reference population, according to the most recent data of the Italian Statistics Institute (ISTAT, 12/31/2019). In particular, data have been weighted for the main socio-demographic and geographic variables (e.g., sex by age by geographical area, occupation, educational qualification, geographical area by size of living centers). The sample size made it possible to maintain a sampling error of less than 2% (at the significance level of 95%) and to control the error of estimates within groups or subgroups of interest. The interviews were conducted by Doxa S.p.a. and carried out with the CAWI technique (Computer Assisted Web Interviewing) on an online panel and on the Confirmit software platform used by Doxa S.p.a. The average administration time was about 18-20 minutes. This study was approved by the Ethics Committee of the IRCCS San John of God Fatebenefratelli of Brescia (n° 72-2020), and all participants provided written informed consent.</p> <p>The primary objectives are to:</p> <p>● Monitor variables that are critical for population behaviour to control transmission of the novel coronavirus, including risk perceptions, knowledge, self-efficacy, confidence in institutions, behaviours, rumours, affect, worry, resilience, trust in/use of information sources and more.<br> ● Document changes over time in these factors to understand the effect of the pandemic process, new developments, events or measures taken.<br> ● Monitor possible issues, e.g. related to misinformation or distrust, as they emerge, to allow early response.<br> ● Identify relationships between variables to identify levers for effective and appropriate responses.<br> ● Explore the relationship of psychological variables (e.g. worry, resilience, trust, affect) with the epidemiological situation and the events and measures taken.<br> ● Identify gaps between perceived and actual knowledge.<br> ● Evaluate the effectiveness of pandemic response measures, and the acceptance and effectiveness of policies and restrictions implemented, including the easing of such restrictions.<br> The secondary objectives are to:<br> ● Contribute to post-outbreak evaluation, thereby contributing to the continued regional/global efforts to better understand mechanisms of crisis response.<br> ● If additional research capacity is available, the data can be triangulated with data on media reporting, COVID-19 cases and other.● If additional research capacity is available, the data can be triangulated with data on media reporting, imported or confirmed cases, etc.: The relationship between psychological variables and characteristics of the outbreak situation can be explored (i.e. how closely the perceived risk mirrors reported cases, relative import risk, media reports).<br> This approach allows a citizen-centred approach where insights into population perceptions and behaviours inform COVID-19 actions, alongside epidemiological data and considerations of economic, cultural, ethical, structural political nature and other.</p> <p>The WHO questionnaire includes 21 different thematic areas noteworthy for the investigation of COVID-19 experience. The questionnaire was translated into specific country language by each recruiting site, following the WHO’s guidelines for translations of tools into other languages. The process included the following steps: forward translation, panel experts, back-translation, pre-test and cognitive interviews and, finally, development of the final version. Variables being surveyed include the following:<br> • Socio-demography;<br> • COVID-19 personal experience;<br> • Health literacy;<br> • COVID-19 risk perception;<br> • Probability and Severity;<br> • Preparedness and Perceived self-efficacy;<br> • Prevention – own behaviours;<br> • Affect;<br> • Trust in sources of information;<br> • Use of sources of information;<br> • Frequency of Information;<br> • Trust in institutions (perceptions);<br> • Policies, interventions (perceptions);<br> • Conspiracies (perceptions);<br> • Resilience (perceptions);<br> • Testing and tracing;<br> • Fairness (perceptions);<br> • Lifting restrictions (pandemic transition phase);<br> • Unwanted behaviour;<br> • Wellbeing;<br> • COVID-19 vaccine.</p> <p> </p>
Data_Fig1_Monitoring Changes in the Oxidizing Milieu in the Endoplasmic Reticulum of Mammalian Cells Using HyPerER
<p>Data of JB_10.21769_BioProtoc.4076_Figure 1,</p> <p>The Dataset (Original publication Doi:10.21769/BioProtoc.4076) contains the original figure as PNG-format (JB_10.21769_BioProtoc.4076_Fig1.PNG). Corresponding raw data from confocal microscopy measurements provided as two files (31003A_179400_10.21769_BioProtoc.4076_JB_Hyper_14_1/2.avi). Related information provided as one meta-data-file (31003A_179400_10.21769_BioProtoc.4076_JB_Hyper_14_M1.txt) in txt format and two files in pdf format (31003A_179400_10.21769_BioProtoc.4076_JB_Hyper_14_M2/3. pdf).</p>
Sampling-event dataset of short-term monitoring in Poblacion and Kadurong Reefs in Liloan, Cebu, Philippines
<p>This is a sampling-event dataset of the short-term monitoring of Poblacion and Kadurong Reefs, two of the marine protected areas Municipality of Liloan, Cebu, Philippines. Water quality and ecological assessments were carried out to monitor the status and trends of biological and physical parameters associated with coral reefs using the standard protocols for surveying tropical marine resources. Specifically, the following measurements were conducted: (1) physico-chemical parameters, (2) phytoplankton and zooplankton occurrence and abundance, (3) fish occurrence and density, and (4) percent cover of benthic components of coral reef. The data can serve as the basis for the formulation and implementation of relevant measures for conservation and protection management of the Poblacion and Kadurong Reefs in Liloan, Cebu, Philippines. </p> <p>In this version, occurrence.csv was revised as described below:</p> <ul> <li>TaxonID for <em>Abudefduf vaigiensis</em> (Quoy & Gaimard, 1825) and <em>Hemiaulus</em> P.A.C. Heiberg, 1863 were corrected.</li> <li>Author names with corrupted characters/symbols were corrected. </li> </ul> <p> </p>
Short-term Monitoring of Coral Reef Marine Protected Areas (MPAs) in the Municipality of Liloan, Central Visayas, Philippines
<p>This is a sampling-event dataset of the short-term monitoring of Poblacion and Kadurong Reefs, two of the marine protected areas Municipality of Liloan, Cebu, Philippines. Water quality and ecological assessments were carried out to monitor the status and trends of biological and physical parameters associated with coral reefs using the standard protocols for surveying tropical marine resources. Specifically, the following measurements were conducted: (1) physico-chemical parameters, (2) phytoplankton and zooplankton occurrence and abundance, (3) fish occurrence and density, and (4) percent cover of benthic components of coral reef. The data can serve as the basis for the formulation and implementation of relevant measures for conservation and protection management of the Poblacion and Kadurong Reefs in Liloan, Cebu, Philippines.</p> <p>In this version, occurrence.csv was revised as described below:</p> <ul> <li>taxonID for <em>Abudefduf vaigiensis</em> (Quoy & Gaimard, 1825) and <em>Hemiaulus</em> P.A.C. Heiberg, 1863 were corrected.</li> <li>Author names with corrupted characters/symbols were corrected. </li> </ul>
Photos of an old actigraph (model unknown) from Ambulatory Monitoring, Inc. (AMI)
<p>Photos of an old actigraph (model unknown) from <a href="http://www.ambulatory-monitoring.com/">Ambulatory Monitoring, Inc</a> (AMI).</p> <p>If you have any information regarding this device, please let me know (danvartan@gmail.com).</p>
Harmfull algae bloom monitoring program dataset; ERDDAP, ERA5 and ONI datasets; and R script for multicriteria analisys in Santa Catarina coastal zone, Brazil.
<p>Project Harmful Algae Bloom (HAB) Monitoring Network in Santa Catarina, Brazil - Database and R script with data analysis. This project was funded by the Foundation for Research Support of the State of Santa Catarina – FAPESC and generated a database combining a HAB monitoring dataset with oceanographic (from ERDDAP) and climatic (from ERA5 and ONI) data which was submitted to multicriteria analysis using R. The HAB monitoring dataset was obtained from Cidasc/SC State Government (http://www.cidasc.sc.gov.br/defesasanitariaanimal/monitoramento-de-algas-nocivas/) and contains results of phytoplankton counts in water samples and toxin levels in shellfish samples obtained from 39 points located in shellfish farms distributed along the SC coastline. Oceanographic data were obtained from the ERDDAP/NOAA website (https://coastwatch.pfeg.noaa.gov/erddap/index.html), including the variables mean chlorophyll concentration (mg.m-3) and mean sea surface temperature (ºC); Climate data were obtained from Copernicus/ERA5 (https://cds.climate.copernicus.eu/) including the variables mean air temperature (ºC), mean pressure (Pasc.), mean cloud cover (%), mean precipitation (kg.m-2), radiation (Einsteins.m-2.day-1), mean U wind (m.s-1), and mean V wind (m.s-1).; Oceanic Niño Index (ONI) data were obtained from the NOAA website (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php); The R script involves a pre-processing routine aimed at summarizing and integrating all datasets and the subsequent data analyses carried out to evidence temporal patterns related to different type of algal blooms. Detailed methods will be provided in a scientific article.</p>
Electrical tomography resistivity (ERT) monitoring time series
<p>Multi-temporal electrical tomography resistivity (ERT) measurements for monitoring the performance of the bio-degradable bentonite mat in OAL-Austria. The first measurement was conducted on 30 July 2020 before the implementation of the mat, afterwards seasonal measurement (except for winter due to snow cover) were obtained: 19 Oct 2020, 27 Arpil 2021, 10 August 2021, 4 October 2021, 13 April 2022. A time-lapse inversion algorithm was used to prepare the final results. See OPERANDUM deliverable 4.6 for more details.</p> <p>Device: Lippmann 4point light 10 W</p>
Monitoring NBS for coastal erosion and marine flooding: the Emilia-Romagna case study
<p>The study was conducted in the context of the OPERANDUM project which is an H2020 project which aims at providing tools and methodologies for the assessment of NBS efficiency around the world. As NBS will be tested an artificial dune built with natural materials. </p> <p>The artificial dune is an engineered structure that will mimic the functioning of natural dunes. Its aims are reducing both natural dune erosion and flooding in adjacent coastal lowlands. It consists of a barrier between the sea and land, in a similar way to a seawall. Unlike the latter, the NBS are ‘dynamic’, i.e. the dune/beach system interacts a great deal and is constantly undergoing small adjustments in response to changes in wind and wave climate or sea level. Its construction involves the placement of sediment from dredged sources on the beach and it will be reinforced with a structure composed of biodegradable material. Different typologies of experimental solutions are foreseen.</p> <p>The Bellocchio Beach at Lido di Spina (Italy) was initially chosen for the study, however the Volano beach was selected as the new study area because of the strong erosion caused by an intense storm event in December 2020 at Bellocchio. The dune was built on the Volano beach and monitoring surveys were carried out on this new site. </p> <p>A morphological monitoring aimed to assess the beach evolution and the performance of the NBS were performed. Monitoring of morphology evolution of shoreline and inland area provide information about impact of the NBS on coastal erosion. Furthermore, the changes in the form of the work give information about the resistance of the NBS to wave attacks. Sedimentological campaigns have been planned in order to provide information regarding the texture of the sediments present in the area detected and possibly highlight changes after the construction of the dune.</p> <p>Three monitoring campaigns were carried out before, immediately after and six months later the construction of the dune (January, May and October 2022). All data were analysed to assess local coastal dynamics and NBS evolution. </p> <p>The monitoring consisted of: </p> <ul> <li> <p>topographic and bathymetric surveys (GNSS and multibeam/singlebeam echosounder) to generate DTMs of the entire area (10 m cell size); </p> </li> <li> <p>aerial photogrammetric surveys by UAV for the production of orthophotos and high resolutions DTMs of the emerged beach (1m cell size) and of the dune area (0.2 m cell size); </p> </li> <li> <p>sediment sampling and grain size analysis. </p> </li> </ul> <p>Surveys show that morphological and sedimentological changes are determined mostly by anthropic actions to the beach and seabed maintenance (artificial winter banks and Sacca di Goro channel). </p> <p>Regarding the dune area no significant changes in morphology were observed due to the limited period between the surveys. Appreciable signals were detected, such as the natural recolonization by pioneer plant species and the slight sand accumulation on the dune foot.</p> <p>This dataset consists of data related to monitoring activities. </p>
Dataset linking to the publication "An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020"
<p>This dataset links to the study “An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020”. This study is published in the journal “Environmental Research Letters” which can be found at <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abd81b">https://iopscience.iop.org/article/10.1088/1748-9326/abd81b</a>. The dataset contains two files, one csv file, and one shape file. The two files contain the same data to meet the different users' needs. The dataset contains variables for assessing national forest monitoring data sources i.e., RS and/or NFI. Separate indicators namely 'Use of RS', and 'Use of NFI' were used to analyze the two data sources (RS and NFI). The description of each variable for these two indicators contained in the dataset is given in the Table below.</p> <table> <caption><strong>The description of the variables in the datase</strong>t <strong>for country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Variables Name</strong></td> <td><strong>Description of the variables</strong></td> </tr> <tr> <td>Country</td> <td>Country</td> </tr> <tr> <td>ISO_A3_CODE</td> <td>ISO A3 Code for country</td> </tr> <tr> <td>ADM0_CODE</td> <td>ADMO Code for country</td> </tr> <tr> <td>CONTINENT</td> <td>Continent</td> </tr> <tr> <td>Region</td> <td>Region</td> </tr> <tr> <td>RSInd_05</td> <td>Use of remote sensing (RS) for forest area (change) monitoring 2005 Indicator</td> </tr> <tr> <td>RSSc_05</td> <td>Use of RS for forest area (change) monitoring 2005 Score</td> </tr> <tr> <td>RSInd _10</td> <td>Use of RS for forest area (change) monitoring 2010 Indicator</td> </tr> <tr> <td>RSSc _10</td> <td>Use of RS for forest area (change) monitoring 2010 Score</td> </tr> <tr> <td>RSInd_15</td> <td>Use of RS for forest area (change) monitoring 2015 Indicator</td> </tr> <tr> <td>RSSc _15</td> <td>Use of RS for forest area (change) monitoring 2015 Score</td> </tr> <tr> <td>RSInd_20</td> <td>Use of RS for forest area (change) monitoring 2020 Indicator</td> </tr> <tr> <td>RSSc _20</td> <td>Use of RS for forest area (change) monitoring 2020 Score</td> </tr> <tr> <td>DRS05_20</td> <td>Difference ‘use of RS’ 2005-2020</td> </tr> <tr> <td>NFIInd_05</td> <td>Use of national forest inventories (NFI) for forest monitoring 2005 Indicator</td> </tr> <tr> <td>NFISc_05</td> <td>Use of NFI for forest monitoring 2005 Score</td> </tr> <tr> <td>NFIInd _10</td> <td>Use of NFI for forest monitoring 2010 Indicator</td> </tr> <tr> <td>NFISc _10</td> <td>Use of NFI for forest monitoring 2010 Score</td> </tr> <tr> <td>NFIInd_15</td> <td>Use of NFI for forest monitoring 2015 Indicator</td> </tr> <tr> <td>NFISc _15</td> <td>Use of NFI for forest monitoring 2015 Score</td> </tr> <tr> <td>NFIInd_20</td> <td>Use of NFI for forest monitoring 2020 Indicator</td> </tr> <tr> <td>NFISc _20</td> <td>Use of NFI for forest monitoring 2020 Score</td> </tr> <tr> <td>DNFI05_20</td> <td>Difference ‘Use of NFI’ 2005-2020</td> </tr> </tbody> </table> <p>Indicators and Scores in the above Table for showing the use of RS and NFI data for forest monitoring in Figure 1 (1a, 1b, and 2a, 2b) are related in the following way.</p> <table> <caption><strong>The indicator values and scores of the country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Indicator</strong></td> <td><strong>Score</strong></td> </tr> <tr> <td>Low</td> <td>0</td> </tr> <tr> <td>Limited</td> <td>1</td> </tr> <tr> <td>Intermediate</td> <td>2</td> </tr> <tr> <td>Good</td> <td>3</td> </tr> <tr> <td>Very Good</td> <td>4</td> </tr> </tbody> </table> <p>The capacity changes from 2005 to 2020 in Figure 1 (1c & 2c) are related in the following way.</p> <table> <caption><strong>The indicator values and levels for country capacity changes</strong></caption> <tbody> <tr> <td><strong>Capacity change values</strong></td> <td><strong>Capacity change levels</strong></td> </tr> <tr> <td>1,2,3,4</td> <td>Increase</td> </tr> <tr> <td>0</td> <td>No change</td> </tr> <tr> <td>-1,-2,-3,-4</td> <td>Decrease</td> </tr> </tbody> </table> <p> </p>
SoilCare WP5 database with experimental monitoring data
<p>Here the database with the experimental data collected in all SoilCare study sites is included. The SoilCare database: schema (empty database) and Report 34 (D5.1) which reports and explains the database, which the SoilCare project developed and used for storing the monitoring results from the tested cropping systems and/or field agricultural experiments in the 16 Study sites can be accessed in <a href="https://doi.org/10.5281/zenodo.5541295">10.5281/zenodo.5541295</a>. </p> <p> </p> <p> </p>
GERDAT014 Self-management and self-monitoring literature search
<p>Dataset used for the literature on suitable self-monitoring or self-management tools or applications that could be used for older patients with cancer and/or multimorbidity</p>
Satellite-driven 10km global root-zone soil moisture analysis for drought monitoring
<p>Root-zone soil moisture condition is an important component of water cycle at all spatial scales, as it controls various hydrological, biological and meteorological processes such as plant transpiration and hydraulic redistribution. Passive microwave remote sensing offers the possibility to access to near-surface (0 - 5cm) soil moisture measurements over large areas, providing valuable information for agricultural and water resource management. However, the spatial resolution of satellite soil moisture estimates from passive microwave sensor is relatively coarse (25 to 50km) and infrequent in time. Data assimilation algorithms are widely used to obtain spatially complete and daily continuous soil moisture estimates from intermittent remotely sensed soil moisture data and numerical models. </p> <p>The dataset contains the most recent global surface and root-zone soil moisture conditions at 10km generated from the Satellite-Guided Root-zone moisture Analysis and Forecasting System (S-GRAFS) from 2015 to 2022. S-GRAFS is a near-real time data assimilation system that combines complementary information from model simulations and satellite observations to provide soil moisture estimates at near surface and root-zone. In S-GRAFS, satellite soil moisture observations from Soil Moisture Active Passive (SMAP) are assimilated into a simple first-order autoregressive model that captures the soil moisture conditions in response to precipitation. Satellite precipitation from Global Precipitation Measurement (GPM) is used to drive the model to simulate near surface soil moisture at 5cm. The assimilation of SMAP data relies on the four-dimensional variational (4DVAR) method to adjust the modelled surface soil moisture towards observations within a 4-day assimilation window. Soil Water Index (SWI) is then derived from the analysed surface soil moisture using an exponential filter and represents the root-zone soil wetness at approximate 1m depth. The surface and root-zone wetness from S-GRAFS can be converted into absolute soil moisture content using soil physical properties to provide essential support for a wide variety of hydrological and agricultural applications.</p>
Quantitative monitoring and mapping of the damage in Syria archeological site Afamia (Apamea).
<p>This data is associated with the paper</p> <p><strong>Remote sensing and map quantitative data in an armed conflict zone.</strong></p> <p><strong>Case study: Quantitative monitoring and mapping of the damage in Syria archeological site Afamia (Apamea).</strong></p> <p>This data is arranged in CSV file.</p> <p>TheCSV file (Data-Afamia-Syria-MOBAIED 2023) contains 143,426 rows and 9 columns that represent the 143,426 entities (points) which can be projected under an adapted GIS.</p> <p>The ID for each entity is specified at the column (A), the spatial coordinates are determined under WGS 84 / UTM zone 37N at the columns (B, C, D, E). </p> <p>The category of each entity is described for each of the years of observation at the columns (F, G, H, I) using the following code:</p> <p>(0) Outside of the archeological site Apamea</p> <p>(1) inside of the archeological site Apamea </p> <p>(2) Fields</p> <p>(3) Other</p> <p>(4) Intact Site</p> <p>(5) Intact Fields</p> <p>(7) Previous Clandestine Excavations</p> <p>(8) Recent Clandestine Excavations</p> <p>(9) Traces of former excavations</p>
Condition Monitoring for Packaging Industry dataset (CoMoPI)
<p><strong>Condition Monitoring for Packaging Industry dataset (CoMoPI)</strong></p> <p>CoMoPI dataset contains data from eight industrial packaging machines:</p> <table> <thead> <tr> <th> <p><strong>serial</strong></p> </th> <th> <p><strong>Starting Date</strong></p> </th> <th> <p><strong>Ending Date</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>A001</p> </td> <td> <p>2022-08-10 03:45:00+00:00</p> </td> <td> <p>2023-01-09 07:00:00+00:00</p> </td> </tr> <tr> <td> <p>A005</p> </td> <td> <p>2022-09-19 13:15:00+00:00</p> </td> <td> <p>2023-01-09 15:45:00+00:00</p> </td> </tr> <tr> <td> <p>B002</p> </td> <td> <p>2022-07-14 14:00:00+00:00</p> </td> <td> <p>2023-01-09 15:50:00+00:00</p> </td> </tr> <tr> <td> <p>B005</p> </td> <td> <p>2022-10-04 12:55:00+00:00</p> </td> <td> <p>2023-01-05 01:55:00+00:00</p> </td> </tr> <tr> <td> <p>C003</p> </td> <td> <p>2022-12-15 06:50:00+00:00</p> </td> <td> <p>2023-01-03 16:35:00+00:00</p> </td> </tr> <tr> <td> <p>C004</p> </td> <td> <p>2022-07-18 07:55:00+00:00</p> </td> <td> <p>2022-12-28 08:10:00+00:00</p> </td> </tr> <tr> <td> <p>E002</p> </td> <td> <p>2022-07-14 14:20:00+00:00</p> </td> <td> <p>2022-12-03 14:00:00+00:00</p> </td> </tr> <tr> <td> <p>E004</p> </td> <td> <p>2022-11-23 15:25:00+00:00</p> </td> <td> <p>2023-01-09 10:00:00+00:00</p> </td> </tr> </tbody> </table> <p>The dataset provides sensor measurements related to a specific module involved in the watertight closure of packages. It also provides all alarms and warnings generated by the packaging equipment.</p> <p><strong>Dataset description</strong></p> <p>For the sake of simplicity, the dataset is divided into three files, one for sensor measurements, one for alarms and one for warnings.</p> <p><em>Sensor measurements</em></p> <p>This data is included in the file called <em>industrial_dataset_sensors_10m_agg.csv</em>. Each row corresponds to average sensor value in a 10-minute window. Beyond the machine identifier ('_serial') and timestamp ('_time'), the following measurements are available: 'AE', 'BE', 'AF', 'BF', 'APP', 'BPP', 'AP', 'BP', 'ALE', 'BLE', 'ALP', 'BLP', 'ADS', 'BDS', 'AES', 'BES'. Suffixes A_ and B_ relate to the two elements that can perform a specific operation required by the watertight closure of packages. For confidentiality reasons, it is not possible to provide further details.</p> <p><em>Alarm measurements</em></p> <p>This data is included in the file called <em>industrial_dataset_alarm_10m_agg.csv</em>, according to the following schema:</p> <ul> <li>'_serial' : 'machine unique identifier'</li> <li>'_time' : 'timestamp',</li> <li>'AL_1' : 'Counter of AL_1 alarms' ,</li> <li>...</li> <li>'AL_123' : 'Counter of AL_123 alarms'</li> </ul> <p><em>Warning measurements</em></p> <p>This data is included in the file called <em>industrial_dataset_warnings_10m_agg</em>.csv_, according to the following schema:</p> <ul> <li>'_serial' : 'machine unique identifier'</li> <li>'_time' : 'timestamp',</li> <li>'WR_1' : 'Counter of AL_1 alarms' ,</li> <li>...</li> <li>'WR_358' : 'Counter of WR_358 alarms'</li> </ul> <p><strong>Condition monitoring</strong></p> <p>Alarms 'AL_53' are 'AL_54' are related to faulty conditions of the components and can be used as prediction target. The following alarms are generated by the target module: 'AL_17', 'AL_18', 'AL_40', 'AL_41', 'AL_42', 'AL_43', 'AL_45', 'AL_46', 'AL_47', 'AL_48', 'AL_49', 'AL_50', 'AL_51', 'AL_52', 'AL_53', 'AL_54'.</p> <p><strong>Anonymization procedure</strong></p> <p>The anonymization procedure is the following:</p> <ul> <li>To make it impossible to identify the specific piece of equipment, its serial number was replaced with a mock equipment_ID. Moreover, no additional information is provided about the machine, its location and the processed products.</li> <li>Sensor measurements were renamed and rescaled to remove all information related to actual working setup.</li> <li>Alarms and warnings underwent an anonymization process: no description is provided, and the original alarm codes were mapped to mock codes.</li> </ul>
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Open Pit Extraction, Valea Sesei and Roșia Poieni (Romania)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Open Pit Extraction (mine located at Valea Sesei and Roșia Poieni (Romania)) (3D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Underground Extraction, Pyhäsalmi (Finland)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Underground Extraction (mine located at Pyhäsalmi (Finland)) (2D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
ScienceDex guides
Understand access before you commit
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.