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8,460 results for “MONITORING”
Preliminary table of Citizen Science initiatives for monitoring soil health
<p>This matrix is the result of collaborative work for Deliverable 1.1 (WP1; T1.1) of the ECHO project. It facilitated the creation of an overview of the current state of the art in projects, initiatives, or activities that have already involved citizens in monitoring soil health, from both inside and outside the European Union. From this, strategic recommendations for ECHO were derived, ensuring that this project not only makes a valuable contribution to the field of soil health monitoring but also sets a precedent for future citizen science endeavors.</p>
National Open Access Monitor, Draft Report: Stakeholder Feedback: Response Dataset
<p>This dataset contains the response data from the 'National Open Access Monitor, Draft Report: Stakeholder Feedback' Form which was open from 16th to 30th November 2023 under the National Open Access Monitor Project. A PDF reference copy of the Feedback Form is available here: <a href="https://zenodo.org/doi/10.5281/zenodo.10141988">https://zenodo.org/doi/10.5281/zenodo.10141988</a></p><p>The purpose of the form was to capture stakeholder feedback on the National Open Access Monitor, Ireland Draft Report, for actioning by OpenAIRE in the final National Open Access Monitor Report to be delivered in January 2024. The draft is an interim report, and includes reference to the initial feedback from IReL and the National Open Access Monitor Project Advisory Group.</p><p><strong>To note: </strong></p><ul><li>Responses have been pseudonymised to the level of stakeholder-group e.g. Contributor I, Research Performing Organisation I, where requested by the participant in the participant consent form: <a href="https://doi.org/10.5281/zenodo.7589770">https://doi.org/10.5281/zenodo.7589770</a></li><li>This is the original raw data file, in csv format, as downloaded from the Online Surveys platform and subsequently pseudonymised.</li></ul><p>-----------------------</p><p>The context for the feedback form is detailed in the National Open Access Monitor Project Plan: <a href="https://doi.org/10.5281/zenodo.7331431">https://doi.org/10.5281/zenodo.7331431</a>, the National Open Access Monitor Advisory Group Meeting Minutes, 27th October 2023: <a href="https://zenodo.org/doi/10.5281/zenodo.10105023 ">https://zenodo.org/doi/10.5281/zenodo.10105023 </a>and the OpenAIRE National Open Access Monitor Ireland, Draft Report: <a href="https://zenodo.org/doi/10.5281/zenodo.10136295">https://zenodo.org/doi/10.5281/zenodo.10136295</a></p><p>This project is managed by <a href="http://www.irel.ie/">IReL </a>and has received funding from Ireland's National Open Research Forum under the NORF Open Research Fund. <a href="https://norf.ie/funding/">https://norf.ie/funding/ </a><a href="https://norf.ie/orf-projects-announcement/">https://norf.ie/orf-projects-announcement/</a></p>
Long-term monitoring of the fish community in the Minho Estuary (NW Iberian Peninsula)
<p>The dataset contains data from fyke nets deployed in the Minho Estuary (Portugal) from 2010 to 2019. The fyke nets were used for fish sampling and data collection. The sampling frequency varied but, on average, data was collected weekly using five different fyke nets. However, due to technical issues (e.g. lost or damaged fyke nets), the sampling pattern is not constant, with some fyke nets staying underwater for shorter or longer periods, and occasionally having fewer than five fyke nets per parentEventID. The dataset includes various terms such as parentEventID, eventID, eventDate, year, startDayOfYear, endDayOfYear, country, countryCode, geodeticDatum, decimalLatitude, decimalLongitude, coordinateUncertaintyInMeters, DEIMS.iD, habitat, basisOfRecord, samplingProtocol, sampleSizeValue, sampleSizeUnit, samplingEffort, occurrenceStatus, occurrenceID, organismQuantity, organismQuantityType, degreeOfEstablishment, vernacularName, scientificName, acceptedNameUsageID, taxonRank, kingdom, phylum, order, family, genus, and scientificNameAuthorship.</p>
Extracted Source Properties Catalog for "Monitoring the X-ray Variability of Bright X-ray Sources in M33"
<p>Supplemental data to the article "Monitoring the X-ray Variability of Bright X-ray Sources in M33" accepted for publication in ApJ. Contains all extracted source properties for the 56-source final catalog, including single-ObsID extractions and merged values. See ReadMe for column descriptions and additional comments.</p>
Dataset for "ZnO decorated Graphene-based NFC tag for personal NO2 exposure monitoring during a workday"
<p>Dataset with all measurements performed and related to the publication "ZnO decorated Graphene-based NFC tag for personal NO2 exposure<br>monitoring during a workday" Published in Sensors MDPI 2024 by A. Santos and co-workers.</p>
Data from: The effect of probe density coverage on the detection of oenological tannins in quartz crystal microbalance with dissipation monitoring (QCM-D) experiments
<p>Polyphenols, crucial compounds in grapes, musts, and wines, influence grape ripening, must fermentation, and final wine quality. Current detection methods for polyphenols are expensive, time-consuming, and reliant on specialized laboratories and personnel. This study proposes the use of a functionalized acoustic sensor to address these limitations and efficiently detect oenological polyphenols.</p> <p>The method employs a quartz crystal microbalance with dissipation monitoring (QCM-D) combined with a gelatin-based probe layer to detect the target analyte. The sensor is functionalized by optimizing probe coverage density, accomplished through the use of 12-mercaptododecanoic acid (12-MCA) for probe immobilization onto the gold sensor surface, along with dithiothreitol (DTT) as a reducing and competitive binding agent. Varying concentrations of 12-MCA and DTT allow for control over probe density, with QCM-D measurements demonstrating effective adjustment, ranging from 0.2 × 10^13 to 2 × 10^13 molecules cm^−2. The study also explores the interaction between the probe and tannins, confirming the ability of the sensor to detect them. Notably, lower probe coverage yields higher detection signals when normalized to probe immobilization signals. Additionally, significant alterations in the mechanical properties of the functionalization layer occur after interaction with samples.</p> <p>Combining QCM-D with gelatin functionalization presents promising applications in the wine industry. This approach enables real-time monitoring, requires minimal sample preparation, and offers high sensitivity for quality control purposes.</p>
Change detection technique comparison in long-term wetland monitoring: datasets and maps of the Poitevin Marsh (France)
<h3>For a full description of the methodology and results, please see the following article:</h3> <div> <div>Demarquet, Q., Rapinel, S., Gore, O., Dufour, S., Hubert-Moy, L., 2024. Continuous change detection outperforms traditional post-classification change detection for long term monitoring of wetlands. <em>International Journal of Applied Earth Observation and Geoinformation </em>133, 104142. <a href="https://doi.org/10.1016/j.jag.2024.104142">https://doi.org/10.1016/j.jag.2024.104142</a></div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> </div> <h3># Datasets</h3> <p>Points datasets are projected in WGS84 (EPSG:4326), and are provided in the open source GeoPackage format.</p> <p>The first dataset (<strong>Dataset_1.gpkg</strong>) contains training and validation points for random forest classification of EUNIS habitats in the Poitevin Marsh. This dataset consists of 3360 training and 840 validation points (total: 4200).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>CLASS</em>": EUNIS first level habitat type, classified as following:<br> <ul> <li>1: EUNIS habitat A</li> <li>2: EUNIS habitat B</li> <li>3: EUNIS habitat C1J5</li> <li>4: EUNIS habitat C3</li> <li>5: EUNIS habitat E</li> <li>6: EUNIS habitat G</li> <li>7: EUNIS habitat I</li> <li>8: EUNIS habitat J</li> </ul> </li> <li>"<em>DATE</em>": Date associated with EUNIS habitat sample</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>TYPE</em>": Either training ("<em>train</em>") or validation ("<em>test</em>") sample</li> </ul> <p>The second dataset (<strong>Dataset_2.gpkg</strong>) contains points for the Olofsson correction method. This dataset consists of 326 points where the change classes are classified as following: -10 (wetland loss), 10 (wetland gain), 100 (stable existing wetland), and 200 (stable damaged wetland).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>REFERENCE</em>": Change class reference</li> <li>"<em>CCDC</em>": Change class obtained from the Continuous Change Detection and Classification approach</li> <li>"<em>PCCD</em>": Change class obtained from the Post-Classification Change Detection approach</li> </ul> <p>Supplementary layout files (<strong>Dataset_1.qml</strong> and <strong>Dataset_2.qml</strong>) support formatting of the points in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># EUNIS habitat</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. </p> <p>Habitat maps are given for the two approaches in years 1984 and 2022:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_HABITAT_1984.tif</strong> and <strong>CCDC_HABITAT_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_HABITAT_1984.tif </strong>and <strong>PCCD_HABITAT_2022.tif</strong>)</li> </ul> <p>Supplementary layout files (<strong>CCDC_HABITAT_1984.qml, CCDC_HABITAT_2022.qml, PCCD_HABITAT_1984.qml, PCCD_HABITAT_2022.qml</strong>) support formatting of raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># Change detection during the 1984-2022 period</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. Raster values follow the classification scheme used in Dataset_2.</p> <p>Change detection maps are given for the two approaches:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_CHANGE_1984_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_CHANGE_1984_2022.tif</strong>)</li> </ul> <p>Supplementary layer files (<strong>CCDC_CHANGE_1984_2022.qml</strong> and<strong> PCCD_CHANGE_1984_2022.qml</strong>) support formatting of the raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># GEE repository</h3> <p>To get direct access to GEE scripts and assets, please follow those two links:</p> <p>https://code.earthengine.google.com/?accept_repo=users/demarquetquentin/CCDC_Poitevin</p> <p>https://code.earthengine.google.com/?asset=projects/ee-quen-dem/assets/CCDC_Poitevin</p>
Dataset for "Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation"
<p>This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 187223) in association with the recent publication entitled “Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation”. This work aims to study the capacitive response of a resonating capacitive device coated with phase changing material (jojoba oil) as it melts when crossing its melting temperature. Several configuration were simulated with different electrode spacing, oil volume and encapsulation thickness and the induced changes in capacitance were tested experimentaly. An eco-friendly implementation of the optimized spiral resonating devices was tested wirelessly over a custom made near field antenna and the frequency of resonance was measured as the oil melted over the structure, irreversibly changing its resonance frequency. The data that was collected in the frame of this work is present in this repository. More information about the content of the dataset is present in the included README file.</p>
EJPSOIL ARTEMIS on-farm monitoring of soil health and ecosystems services (meta)data
<p>This database includes the data and metadata from the initial on-farm monitoring od soil health and soil related ecosystem services of the EJPSOIL ARTEMIS project. </p>
Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment - Measurements and Locations
<p>Many wetlands in East Africa are farmed and wetland reservoirs are used for irrigation, livestock, and fishing. Water quality and agriculture have a mutual influence on each other. Turbidity is a principal indicator of water quality and can be used for, otherwise, unmonitored water sources. Low-cost turbidity sensors improve in situ coverage and enable community engagement. The availability of high spatial resolution satellite images from the Sentinel-2 multispectral instrument and of bio-optical models, such as the Case 2 Regional CoastColor (C2RCC) processor, has fostered turbidity modeling. However, these models need local adjustment, and the quality of low-cost sensor measurements is debated. We tested the combination of both technologies to monitor turbidity in small wetland reservoirs in Kenya. We sampled ten reservoirs with low-cost sensors and a turbidimeter during five Sentinel-2 overpasses. Low-cost sensor calibration resulted in an R² of 0.71. The models using the C2RCC C2X-COMPLEX (C2XC) neural nets with turbidimeter measurements (R² = 0.83) and with low-cost measurements (R² = 0.62) performed better than the turbidimeter-based C2X model. The C2XC models showed similar patterns for a one-year time series, particularly around the turbidity limit set by Kenyan authorities. This shows that both the data from the commercial turbidimeter and the low-cost sensor setup, despite sensor uncertainties, could be used to validate the applicability of C2RCC in the study area, select the better-performing neural nets, and adapt the model to the study site. We conclude that combined monitoring with low-cost sensors and remote sensing can support wetland and water management while strengthening community-centered approaches.</p> <p>The provided dataset includes a point shapefile with the studied reservoirs in central Kenya and a data table with the sampling date (Sentinel-2 overpass plus/minus one day), low-cost sensor setup number, reservoir ID, sampling location within the reservoir, the voltage measurements of the three respective low-cost sensor heads for sensor setups A and B, the averaged voltage, and the turbidimeter measured turbidity value in nephelometric turbidity units (NTU).</p> <p>The study is available in (please cite):</p> <div> <div>Steinbach, S., Rienow, A., Chege, M.W., Dedring, N., Kipkemboi, W., Thiong’o, B.K., Zwart, S.J., Nelson, A., 2024. Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment. <em>IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing</em> <em>17</em>, 8490–8508. <a href="https://doi.org/10.1109/JSTARS.2024.3381756">https://doi.org/10.1109/JSTARS.2024.3381756</a></div> </div> <p>This research was supported in part by the German Federal Ministry of Education and Research (BMBF) through the Project “Participatory Approach to Environmental Conservation of the Muringato Catchment Area for Sustainable Management and Enhanced Ecosystem Health” (CITGI4Muringato) under Grant Agreement No. 01DG20022.</p>
Telblokken voor de monitoring van patrijs in Vlaanderen
<p>Deze kaartlaag deelt de open ruimte voor patrijs (<em>Perdix perdix</em>) op in telblokken. Elk telblok omvat de open ruimte van eenzelfde jachtgebied. Indien de open ruimte binnen eenzelfde jachtgebied groter is dan 150 ha of de afmetingen zijn te groot om het bij een schaal 1:10.000 op en A4 af te drukken, splitsen we het jachtgebied in meerdere telblokken. Hiervoor gebruiken we grenzen die op het terrein duidelijk zichtbaar zijn zoals wegen en waterlopen.</p>
Dataset for Earth Sciences at Freie Universität Berlin: Open Access, Licenses and Persistent Identifiers Monitoring
<p>In <em>Version 4</em>, <strong>publishers </strong>and <strong>journals</strong> names has been extended.</p> <p>In <em>Version 3</em>, new entries have been added for both <strong>journal </strong>and <strong>non-journal article outputs</strong>, specifically including data from the year <strong>2023</strong>. Minor adjustments were also made to URLs and open access (OA) statuses.</p> <p><em>Note</em>: Data for journal and non-journal article outputs from the year 2023 were unavailable at the time of preparing the <strong>short paper</strong> presenting the results, findable under <a href="https://doi.org/10.5281/zenodo.14170751" target="_blank" rel="noopener">10.5281/zenodo.14170751</a> [1]).</p> <p><br>Started in 2021, Berlin University Alliance (BUA) Open Science Dashboards, followed by the BUA Open Science Magnifiers projects, seek to investigate Open Science (OS) practices across different research domains and communities. A primary focus of these initiatives lies in the development of OS indicators, tailored to discipline specific ones, alongside their visualisation for monitoring.</p> <p>Collaborating closely with the Department of Earth Sciences at Freie Universität Berlin (FU), one of the project's key objectives is the implementation of an Open Science Dashboard for Earth Sciences FU. The visualisation of the first OS metrics is already available under <a href="https://quest-open-earthsciences.charite.de/">https://quest-open-earthsciences.charite.de/</a>.</p> <p>The datasets utilized include the outputs from the Department of Earth Sciences at FU, i.a. on Open Access (OA) categorisations and statuses, persistent identifiers (PIDs) and Open Licences (Creative Commons) availability, published between 2016-2023. These datasets consist of (i) <strong>"journal_articles_v3.csv"</strong> and (ii) <strong>"non_journal_articles_outputs_v3.csv"</strong>, the latter including “book”, “book chapter”, “conference paper”, “conference abstract”, and “other research outputs” (e.g. book reviews, project reports, book chapters in school books, or electronic supplementary material).</p> <p>Data for the dashboard was obtained from the FU university bibliography (<a href="https://frub-berlin.primo.exlibrisgroup.com/">https://frub-berlin.primo.exlibrisgroup.com/</a>), but coverage of PID information was incomplete, OA category information was incomplete and often erroneous, and copyright/open licence information was missing in this data set. Therefore, the data set was <strong>enriched with manually researched information</strong>. Data enrichment was different for journal articles and for non-journal-article publications. For <strong><em>journal articles</em></strong>, <em>copyright/open licence</em> information was added, and <em>open access category</em> information was checked and added or corrected. For <strong><em>non-journal-article outputs</em></strong>, missing <em>PIDs</em> were added and <em>open access category</em> information was checked and added or corrected. </p> <p>The "<em>data_dictionary_earth_sciences_v3.csv"</em> table documents all variables of each data file containing here.</p> <p>Both for the dashboard, and in our following publications, we categorized <strong>"bronze"</strong> OA outputs as closed access. Although such publications are openly available on the publisher's websites, they lack licence information and thus cannot be openly reused, and presumably even change its openness status at any time. Following the methodology of Charité Dashboard on Responsible Research (<a href="https://quest-dashboard.charite.de/#tabStart">https://quest-dashboard.charite.de/#tabStart</a>) we only include "gold", "hybrid" and "green" OA as true OA. Further details about the enrichment process conducted on these datasets can be found under <a href="https://doi.org/10.5281/zenodo.1099821" target="_blank" rel="noopener">10.5281/zenodo.1099821</a>9 [2]</p> <p> </p> <p>[1] Duine, M., Iarkaeva, A., & Hübner, A. (2024, November 15). Initiating discipline-specific Open Science Monitoring with the Open Science Dashboard for Earth Sciences. 28th International Conference on Science, Technology and Innovation Indicators (STI2024), Berlin, Germany. <a href="https://doi.org/10.5281/zenodo.14170751" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14170751</a><br>[2] Duine, M., Hübner, A., & Iarkaeva, A. (2024). Enrichment of university bibliography data for open science monitoring. Zenodo. <a href="https://doi.org/10.5281/zenodo.10998219">https://doi.org/10.5281/zenodo.10998219</a></p>
Carbon Monitor - Global Daily CO2 Emissions in Near-Real-Time
<p><strong><em>Carbon Monitor: A near-real-time global daily CO2 emission dataset</em></strong></p> <p>Carbon dioxide (CO<sub>2</sub>) emissions from the use of fossil fuels and the production of cement are the main driving force of climate change. Carbon Monitor is an international initiative providing for the first time regularly updated, science-based estimates of daily CO<sub>2</sub> emissions.</p> <ul> <li>Website:</li> </ul> <p><a href="https://carbonmonitor.org">https://carbonmonitor.org</a></p> <ul> <li>Citation:</li> </ul> <p>Liu, Z., Ciais, P., Deng, Z. <em>et al.</em> Near-real-time monitoring of global CO<sub>2</sub> emissions reveals the effects of the COVID-19 pandemic. <em>Nat Commun</em> <strong>11, </strong>5172 (2020). https://doi.org/10.1038/s41467-020-18922-7</p> <ul> <li>Data file description:</li> </ul> <table> <thead> <tr> <th scope="col">Field</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>country</td> <td>Brail, China, EU27 & UK, France, Germany, India, Italy, Japan, ROW, Russia, Spain, UK, US, WORLD *</td> </tr> <tr> <td>co2</td> <td>CO2 emissions from fuel combustion and cement production process (unit: kt CO2)</td> </tr> <tr> <td>sector</td> <td>Power, Industry, Residential, Ground Transport, Domestic Aviation, International Aviation, International Shipping, Total **<sup>,</sup>***</td> </tr> <tr> <td>date</td> <td>From 2019/1/1, every day</td> </tr> </tbody> </table> <p>* WORLD = China + US + EU27 & UK + India + Russia + Japan + Brazil + ROW + International Aviation (WORLD) + International Shipping (WORLD)</p> <p>** Total (country level) = Power + Industry + Residential + Ground Transport + Domestic Aviation</p> <p>** Total (WORLD) = Power + Industry + Residential + Ground Transport + Domestic Aviation + International Aviation + International Shipping</p>
Free WiFi to monitor flow in Hanoian traditional markets
<p>Despite being the main source of fresh, convenient, and affordable food for 80% of Hanoi’s population, food flows within traditional markets remain largely invisible due to a lack of tracing systems and environmental conditions which make traditional tracking approaches challenging.</p> <p>By providing free internet to a series of wholesalers and markets in the Cau Giay and Dong Anh districts of Hanoi, Vietnam, this project will put in place the first pieces of tracking system that will characterize and monitor food flows between traders, retailers, and consumers.</p> <p>Research has found that 10-40% of traditional market food is contaminated with microbes or parasites which cause foodborne illnesses. As shoppers become increasingly concerned about food safety and large-scale retailers that can offer food safety certification expand rapidly, this project aims to equip traditional market actors with data that could prevent their marginalization through urban policy decisions that may favor organized retailers, as well as improve the safety of traditional market goods.</p> <p>The collected food flow data will allow for improved linkages among key traditional market actors and help identify better policy and planning options for improving distribution channels in ways that benefits under-resourced communities.</p> <p>To implement the project, the Alliance of Bioversity International and CIAT and the General Statistics Office (GSO) of Vietnam survey actors and track space and time data points on all devices within the range of the WiFi routers and signal amplifiers, whether connected to the internet or not.</p> <p>The pilot system ran on three layers of data:</p> <p><strong>Layer One</strong></p> <p>Every smartphone has a unique media access control (MAC) address that the WiFi routers installed in the markers use to identify how many MAC addresses visit the markets over time, how many return to the market and how often, and how markets differ on these metrics. This data is collected even if the smartphone is not connected to the WiFi network.</p> <p><strong>Layer Two</strong></p> <p>When a smartphone user connects to the free WiFi, they are prompted to answer a series of questions depending on their user type (vendor, customer, etc.). For example, a user that identifies as a vendor is asked questions regarding sales of specific commodities which will allow for sales to be characterized across time and space.</p> <p><strong>Layer Three</strong></p> <p>To validate findings in Layer One and Two, in-person surveys were conducted with vegetable, pork and rice sellers in five traditional markets in Hanoi</p> <ul> <li><strong>mac</strong>: An anonymized version of the MAC. All the MAC address were anonymized through a SHA-3 256 hashing function. The hashed mac ensure anonymity while is consistent across all markets and during the whole period of the analysis. We can therefore ensure that a given mac found in two different dataset will correspond to the same phone.</li> <li><strong>market</strong>: The name of the market where the phone was seen</li> <li><strong>role</strong>: Self-identified role if the user connected to the wifi and filled-out the layer 2 form</li> <li><strong>gender</strong>: Self-identified role if the user connected to the wifi and filled-out the layer 2 form</li> <li><strong>median_first_seen: </strong>The median time when the user is first seen in the markets (in minutes starting at 0 from midnight) (e.g. the time the user entered the market)</li> <li><strong>median_last_seen: </strong>The median time when the user is last seen in the markets (in minutes starting at 0 from midnight) (e.g. the time the user left the market)</li> <li><strong>average_time_day: </strong>The average number of time the user visited the market. A period of time of at least 2 hours between two consecutive observation of the user in the market is needed to be counted as a different visit.</li> <li><strong>average_duration_day: </strong>The average duration spent on the market daily.</li> <li><strong>average_day_week: </strong>The average number of visits per week.</li> <li><strong>average_total_day_seen: </strong>The total number of days a user was seen on the market.</li> <li><strong>total_durantion: </strong>Total duration spent by a single user on the market.</li> </ul>
December 2021 Eclipse Monitoring of WWV at 5 MHz and 10 MHz by K1FR Using FlexRadio 6700/GPSDO and fldigi
<p>This dataset contains observations of WWV at 5 and 10 MHz gathered from December 1 through 10, 2021 as part of the study of the effects of solar eclipses on the ionosphere. The data was collected by amateur radio station K1FR located at 38.828, -77.104 using a FlexRadio 6700 with GPSDO connected to a long wire antenna. The fldigi application was used to analyze and record the received frequency of the WWV station at one second intervals. The dataset includes .CSV files of these measurements.</p> <p>Three independent receivers were created within the 6700 - one for 5 MHz and two for 10 MHz. The first two receivers' data are included in this dataset. Files named analysis_xxxxx-K1FR-1 are for 10 MHz, and those named analysis_xxxxx-K1FR-2 are for 5 MHz. The third independent receiver was used to capture realtime audio from WWV at 10MHz which will be included in a separate dataset.</p>
December 2021 Eclipse Monitoring of WWV at 10 MHz by K1FR Using FlexRadio 6700/GPSDO and Audacity
<p>This dataset contains observations of WWV 10 MHz gathered from December 1 through 10, 2021 as part of the study of the effects of solar eclipses on the ionosphere. The data was collected by amateur radio station K1FR located at 38.828, -77.104 using a FlexRadio 6700 with GPSDO connected to a long wire antenna. The Audacity application was used to record the realtime audio of the receiver which was in the USB mode and tuned to 9999.000 kHz thus producing a 1000 hz tone at the carrier frequency of WWV. The AGC on the receiver was set to “ON-medium.” <strong>PLEASE NOTE:</strong> due to operator error, the metadata in the Audacity generated .WAV files show that the AGC was “OFF”.</p> <p>See DOI 10.5281/zenodo.5775043 for data taken via the fldigi application on the same audio stream simultaneously. That dataset has .CSV files of the apparent doppler of WWV 10 MHz, and these files are named “analysis_xxxxx_K1FR-1.CSV.</p>
Datasets for paper "Evaluating the PurpleAir monitor as an aerosol light scattering instrument"
<p>The data sets included will allow the user to reproduce the plots and analyses described in Ouimette et al. (2022). The Collocated*csv file contains data from multiple collocated PurpleAirs that sampled for a few days. The data in this file was used in the precision analysis in section 2.2.9 of the paper. The other files contain nephelometer and PurpleAir data from Mauna Loa (MLO) and Table Mountain (BOS) and DMPS size distribution files from BOS. Their contents are described in the README.TXT file.</p>
Piezoresistive sensor fiber composites based on silicone elastomers for the monitoring of the position of a robot arm
<p>Combining conductive fillers like carbon black with elastomers allows the development of soft elastomer strain sensors that can reach very large elongations, an important requirement for many robotic applications. However, when the conductive filler is introduced in the polymer, significant stiffening occurs, affecting the mechanical properties, e.g. Young’s Modulus, of the soft structure. In this attempt, single piezoresistive fiber composites were successfully fabricated, without drastically increasing the stiffness. Two silicone elastomers that are widely used in robotic applications were examined as matrix materials. Furthermore, modeling the stresses exerted on the fiber inside the composite was successfully used to predict the detachment of fiber inside the matrix, observed by visual inspection. For the PDMS based composite, pre-straining improved sensor properties, which could be confirmed for the monitoring of the movement of the crane robot. The results showed that the pre-strained piezoresistive sensor fiber-matrix composites positions of the robot crane can be monitored even at low strains.</p>
Dataset for "LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225"
<p>This excel file contains the raw data used in the paper " LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225 " In particular it comprises sensor measurements and pollutant data from the automated air quality monitoring stations in the Tarragona area.</p>
Habitat Protection Indexes - new monitoring measures for the conservation of threatened marine habitats - Datasets and supporting files
<p>The supporting datasets, scripts, and supplementary information for the manuscript, "Habitat Protection Indexes - new monitoring measures for the conservation of threatened marine habitats," are available within this repository.</p> <p>We conduct an analysis on the coverage of protected areas that cover six threatened marine and coastal and developed two indexes, the Local Proportion of Habitat Protected Index and the Global Proportion of Habitat Protected Index, describing the protection of these habitats locally and globally. The habitats considered are the following: cold corals, warm water corals, knolls and seamounts, mangroves, saltmarshes, and seagrasses.</p> <p>The index scores of each jurisdiction are made available for download in the dataset: <em>habitat_protection_indexes_average.csv</em></p> <p>The habitat specific index scores for each jurisdiction are made available for download in the dataset: <em>habitat_protection_indexes.csv. </em></p> <p>Column name descriptions are available in the text file: <em>Column_name_descriptions_20220301</em></p> <p>The scripts used to run the workflow to calculate the indexes, create figures, and calculate statistics for the manuscript are also included. The script <em>01_Workflow sources</em> the first 9 scripts in the <em>scripts</em> folder to calculate the indexes which relies on the functions script within the functions folder. The rest of the scripts in the folder create the figures and calculate the statistics for the manuscript.</p> <p>A readme pdf file is included here to ease with reproducing the workflow, but we strongly suggest to please visit our github (<a href="https://github.com/jkumagai96/Marine_Habitat_protection">https://github.com/jkumagai96/Marine_Habitat_protection</a>) to reproduce the entire calculation where we provide detailed information on how to run the workflow and package management.</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.