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84 results for “Research monitoring”
Towers Forestry Plot, Long-term Vegetation Monitoring in a 1-ha old-growth Rainforest, La Selva Research Station, OTS, Sarapiquí, Heredia, Costa Rica, 2010–2020
The Towers Plot is a 1-hectare permanent vegetation plot established in 2010 under the canopy towers at La Selva Research Station, Sarapiquí, Heredia, Costa Rica. The plot was created by the Organization for Tropical Studies (OTS) to monitor long-term changes in forest structure, composition, and dynamics in an old-growth tropical rainforest. All woody stems with a diameter at breast height (DBH) of 10 cm or greater—including trees, palms, and lianas—were tagged, mapped, and measured following standardized procedures. Censuses were conducted between 2010 and 2020 to document growth, mortality, and recruitment. The dataset includes taxonomic identifications, stem diameter measurements, spatial coordinates within the plot, and metadata describing field methods and species composition. The plot was established beneath three canopy towers that had been previously constructed through the NSF-funded Major Research Instrumentation (MRI) project, NSF 0722741, which provided key infrastructure for canopy and environmental research at La Selva. This proximity created a valuable opportunity to integrate vegetation monitoring with existing environmental instrumentation. Johana Hurtado, coordinator of the Tropical Ecology, Assessment and Monitoring (TEAM) project at La Selva, collaborated with OTS staff in the establishment of the plot, ensuring methodological consistency with other tropical forest monitoring sites. This dataset provides a comprehensive record of woody plant diversity and forest structure in a lowland old-growth Neotropical rainforest. It supports research on forest dynamics, carbon storage, and ecosystem change. The overall monitoring project is ongoing; this data package contains observations from 2010 through 2020.
Woody vegetation composition and structure at long-term monitoring plots on the Stevenson-Hamilton Research Supersite, Kruger National Park, South Africa (2012)
This dataset contains measurements of woody vegetation composition and structural attributes collected in 2012 from long-term ecological monitoring plots located on the Stevenson-Hamilton Research Supersite in the Kruger National Park, South Africa. The study region is characterized by granitic soils, broad-leaved savanna vegetation, and a long history of fire, herbivory, and climate-driven ecological dynamics. Vegetation surveys were conducted in sixteen 0.25-ha sampling plots to quantify woody species composition, stem density, and size structure. Additional measurements of vegetation structure were collected, including grass biomass, canopy cover, canopy height, and canopy diversity, providing a broader assessment of both woody and herbaceous layers. These data establish an important baseline for monitoring ecological change, evaluating woody vegetation dynamics under variable fire and herbivore regimes, and supporting ongoing research on savanna ecosystem functioning within the Kruger National Park.
Long-term monitoring and research of the ecology of the Tres Rios constructed treatment wetland, Phoenix, Arizona, USA, ongoing since 2011
# Project Description In order to better understand the water, nutrient and treatment dynamics of aridland constructed treatment wetlands, we have developed datasets tracking primary productivity (aboveground and belowground), nutrient and water budget dynamics, soils, and aquatic metabolism at the Tres Rios wetlands, operated by the City of Phoenix Water Services Department, since July 2011. The 3-cell Tres Rios Wetlands were completed in 2010 and are associated with the 91st Avenue Wastewater Treatment Plant, the largest in Phoenix. This project is focused on the largest of the three wetlands treatment cells which was the first to be planted and became operational in Summer 2010. The wetland cells are bounded by roads (the "shoreline"), and the system we study is 42 ha in size, approximately half of which is open water and half of which is fringing vegetated marsh. Water depth is relatively consistent across the marsh (approximately 25cm) and effluent inflow to the cell varies seasonally from 95,000 to over 270,000 m3 d-1. Measurements are taken along two gradients representing the two hydraulic pathways of the system: The whole-system, from inflow to outflow, within the vegetated marsh itself. # Abstract Constructed treatment wetlands (CTW) provide cost effective and ecosystem-service based solutions to the problem of urban wastewater treatment. They are a particularly attractive option for water reuse in arid cities, where water resources are scarce, and understanding CTW function in these environments is critical to facilitating sustainable water use practices. Although CTW are well established and studied in mesic climates, how they function in and respond to hot, arid climates is comparatively not well understood. Specifically, large atmospheric water losses via evaporation and plant transpiration comprise a much larger component of the whole-system water budget than in mesic climates. Additionally, given the primary role that emergent macrophytes play in nit
National Open Access Monitor, Ireland - Research product metadata
<p>This dataset contains the foundational data for the National Open Access Monitor under an open license. OpenAIRE will routinely provide monthly data dumps to Zenodo, encompassing a comprehensive set of data and indicators related to the Open Access Monitor. This collaborative effort ensures accessibility and openness in sharing the data, promoting transparency and facilitating its use for research and analysis purposes.<br>The dataset comprises of the metadata of the research products metadata stored in the parquet format. The file contains two columns ("id", "xml"), the first of which is the OpenAIRE identifier of the research product and the the second the xml representation of the metadata. The schema of the metadata can be found in https://www.openaire.eu/schema/1.0/oaf-1.0.xsd and a detailed description of the contents can be found in https://graph.openaire.eu/docs/ and https://zenodo.org/records/2643199</p>
Monitoring open access publishing of NWO funded research (2015-2021) data set
<p>This is the dataset underlying the report "Monitoring open access publishing of NWO funded research" (<a href="https://doi.org/10.5281/zenodo.7041897">https://doi.org/10.5281/zenodo.7041897</a>)</p> <p>The report presents statistics on the extent to which publications from the period 2015–2021 funded by NWO are available in Open Access. The analyses presented in this report also cover publications funded by the Netherlands Organisation for Health Research and Development ZonMw. This report builds on two earlier reports, published in <a href="https://zenodo.org/record/4446042">2020</a> and <a href="https://zenodo.org/record/5056043">2021</a>, covering publications from the period 2015–2018 and 2015-2020, respectively.</p>
The evolution of gender monitoring and its challenges in Research and Innovation in Europe: She Figures reports analysis dataset
<p><span>The article delves into the European Commission's flagship initiative on gender monitoring in science and innovation, offering a responsible metrics perspective and drawing on equality policy literature. Over two decades, the initiative has evolved from competitiveness-related justifications to more transformative objectives related to equality policy evaluation, with the measurement areas and policy focus also undergoing changes. While there has been notable progress, the article points out a logic of invisibility in how dimensions and indicators are conceptualised and their data sources and interpretation. However, it also highlights a significant improvement in the information available. The article suggests that the contextualisation of the process could be enhanced to better integrate it into the policy-making cycle, a crucial area for further research. It concludes with proposals for future gender monitoring science and innovation. The aim is to offer an encouraging vision of monitoring that counts more on who is monitored and in opening up the debates instead of closing them.</span></p>
Long-term moss monitoring network for atmospheric deposition in Germany, link to research data and scientific software
<p>Research data and scientific software related to a study that aims to restructure a long-term monitoring network using moss as biomonitor for atmospheric deposition in Germany. Data from the European Moss Survey 2005 and a statistically based methodology including a decision support system were used to design the spatial network for the 2005 survey.</p>
Aventa AV-7 ETH Zurich Research Wind Turbine SCADA and high frequency Structural Health Monitoring (SHM) data
<p><strong>General description of wind turbine: </strong>The ETH owned wind turbine is Aventa AV-7, manufactured by Aventa AG in Switzerland and was commissioned in December 2002. The turbine is operated via a belt-driven generator and a frequency converter with a variable speed drive. The rated power of the Aventa AV-7 is 7 kW, beginning production at a wind speed of 2 m/s and having a cut-off speed of 14 m/s. The rotor diameter is 12.8 m with 3 rotor blades, and a hub height is 18m. The maximum rotational speed of the turbine is 63 rpm. The tower is a tubular steel-reinforced concrete structure, supported on concrete foundation, while the blades are made of glassfiber with a tubular steel main-spar. The turbine is regulated via a variable-speed and variable pitch control system.</p> <p><strong>Location of site: </strong>The wind turbine is located in Taggenberg, about 5 km from the city centre of Winterthur, Switzerland. This site is easily accessible by public transport and on foot with direct road access right next to the turbine. This prime location reduces the cost of site visits and allows for frequent personal monitoring of the site when test equipment is installed. The coordinates of the site are: 47°31'12.2"N 8°40'55.7"E.</p> <p><strong>Control and measurement systems and signals: </strong>The turbine is regulated via a variable-speed and collective variable pitch control system.</p> <p><strong>SHM Motivation: </strong>Designed and commissioned in 2002, the Aventa wind turbine in Winterthur is soon reaching its end of design lifetime. In order to assess the various techniques of predicting the remaining useful lifetime, a Structural Health Monitoring (SHM) campaign was implemented by ETH Zurich. The monitoring campaign started in 2020, and is still ongoing. In addition, the setup is used as a research platform on topics such as system identification, operational modal analysis, faults/damage detection and classification. We analyze the influence of operational and environmental conditions on the modal parameters and to further infer Performance Indicators (PIs) for assessing structural behavior in terms of deterioration processes.</p> <p><strong>Data Description: </strong>The tower and nacelle have been instrumented with 11 accelerometers distributed along the length of the tower, nacelle main frame, main bearing and generator. Two full bridge strain gauges are installed on the concrete tower based measuring fore-aft and side-side strain (and can be converted to bending moments) – all acceleration and strain signals sampled at 200Hz. Temperature and humidity are measured at the tower base – 1Hz data. In additional we are collecting operational performance data (SCADA), namely: wind speed, nacelle yaw orientation, rotor RPM, power output and turbine status – SCADA signals are sampled at 10Hz. See appendix for further details of the sensors layout.</p> <p>The measurements/instrumentation setup, type and layout is provided in the pdf files.</p> <p><strong>The data:</strong> the data is provided in zip files corresponding to four use-cases as follows:</p> <ul> <li>Normal operation data for system identification</li> <li>Aerodynamic imbalance on one blade</li> <li>Rotor icing event</li> <li>Failure of the flexible coupling of the linear drive of the collective pitch system</li> </ul> <p>The data for each of the four uses-cases is organized in zip files. The content of each zip file is as follows:</p> <ul> <li>Time-series data in HDF5 format</li> <li>Metadata: <ul> <li>Turbine specification (Aventa-AV-7.json and Aventa-AV-7.yaml)</li> <li>Sensor specification (Aventa_sensors.json )</li> <li>Unstructured description of the Aventa Turbine and the installed sensors (Aventa_Sensors_Specs.xlsx)</li> </ul> </li> <li>Semantic artifacts: <ul> <li>WindIO Wind Turbine YAML schema describing turbine specifications (IEAontology_schema.yaml)</li> <li>Sensor specification JSON schema (sensors_schema.json)</li> </ul> </li> <li>Media: Pictures of leading edge roughness and a clip of wind turbine operation</li> <li>Code: Jupyter notebook containing example code to load metadata from JSON and data from HDF5 files (example.ipynb)</li> </ul> <p>Additional data is available upon request, please contact:</p> <ul> <li>Prof. Dr. Eleni Chatzi (chatzi@ibk.baug.ethz.ch)</li> <li>Dr. Imad Abdallah (ai@rtdt.ai , abdallah@ibk.baug.ethz.ch)</li> </ul> <p>For further details or questions, please contact:</p> <p>Prof. Dr. Eleni Chatzi<br> Chair of Structural Mechanics & Monitoring</p> <p>ETH Zürich<br> <a href="http://www.chatzi.ibk.ethz.ch/">http://www.chatzi.ibk.ethz.ch/</a></p>
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>
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>
Global indicators framework for socially responsible research and innovation (RRI): How to monitor public and researcher perspectives (Supplemental material)
<p>This data upload provides a detailed account of indicators that can be used to measure RRI progress around the world against the UNESCO Recommendation for Science and Scientific Researchers at the level of individual researchers and public opinion. This data upload provides supplemental material for an article in Open Research Europe entitled, 'Global indicators framework for socially responsible research and innovation (RRI): How to monitor public and researcher perspectives'.</p> <p>Abstract for main article:</p> <p>As calls for more socially responsible research and innovation (RRI) policies and practices grow more insistent, the need for high quality indicators that can be used to evaluate progress is becoming increasingly important. Given the global nature of science, such indicators need to be relevant to countries across all world regions. Moreover, the methodological quality of indicators is critical to provide a strong foundation for long-term comparative measurement of the impacts of different kinds of policy intervention. There is a practical challenge in this effort relating to the uneven mechanisms for data collection and analysis available in different countries. There is also a geopolitical challenge in gaining buy-in from countries with very different, and sometimes competing, agendas. Here, the 2017 UNESCO-led Recommendation on Science and Scientific Researchers is highlighted as an existing vehicle that can enable cooperation on globally comparative measurement of socially responsible research and innovation. In particular, the quadrennial monitoring of the implementation of this wide-ranging global policy instrument that has been ratified by 195 countries affords a unique opportunity to add value for these countries by linking RRI to the 2017 Recommendation while establishing benchmark indicators for RRI more generally. As a practical and methodological contribution to the global community of science and innovation policymakers, researchers and research and innovation stakeholders committed to socially responsible research, this report contains specific, detailed survey questions and response options focusing on the public opinion and individual researchers’ level of measurement. It provides details of sources of benchmark survey data that have readily available open data that can be used to benchmark the development of socially responsible research and innovation over time from the vantage points of the public and researchers around the world. The aim of this kind of indicators framework is to enable evidence-based practice in socially responsible research and innovation. Robust and practical RRI measurement on a global scale can reduce the risk that well-intentioned but ill-conceived RRI policy and practice interventions create undetected, unchecked, and unreformed negative impacts in practice.</p>
Data from "Participatory research to monitor lake water pollution"
<p>Data from <strong>Participatory research to monitor lake water pollution</strong></p> <p>Authors: Rachel Aronoff(1*), Anna Dussuet(1,2**) Roger Erismann(1,3), Shannon Erismann(3), Luc Patiny(1,2) and Carlos Vivar-Rios(1,4)</p> <p>Affiliations: </p> <p>1) Hackuarium, Ecublens, Switzerland.</p> <p>2) EPFL, Lausanne, Switzerland.</p> <p>3) Hammerdirt, La Tour-de-Peilz, Switzerland.</p> <p>4) UNIL, Lausanne, Switzerland.</p> <p>* corresponding author: biolab@hackuarium.ch</p> <p>** currently at Integra LifeSciences</p> <p> </p> <p>Article DOI: 10.1002/2688-8319.12094</p> <p>Article: Participatory research to monitor lake water pollution<br> Journal: Ecological Solutions and Evidence </p> <p> </p> <p>This Zenodo Repository includes data for:</p> <p>images - microbial plates and more, from 2016, 2017 and 2020.</p> <p>sequences - in particular the 16S results to ID bacteria, provided in a zipped folder: MCBP_Sequences.zip</p> <p>analysis tool items for quantitation and statistics and the data repository from the GitHub repo: </p> <p>https://github.com/Hackuarium/montreux-water-quality</p> <p> </p> <p> </p> <p> </p> <p> </p>
Monitoring open access publishing of NWO funded research (data)
<p>This is the dataset underlying the report "Monitoring open access publishing of NWO funded research" (https://doi.org/10.5281/zenodo.5055609). </p> <p>The report presents statistics on the extent to which publications from the period 2015–2020 funded by NWO are available in Open Access . The analyses presented in this report also cover publications funded by the Netherlands Organisation for Health Research and Development ZonMw.</p> <p>This report builds on an <a href="https://doi.org/10.5281/zenodo.4446042">earlier report</a> published in 2020 covering publications from the period 2015–2018.</p> <p> </p>
Research data supporting "Renal clearable catalytic gold nanoclusters for in vivo disease monitoring"
<p>Raw research data suppoorting the publication:</p> <p>Loynachan C., et al., 2019, Nature Nanotechnology, DOI: https://doi.org/10.1038/s41565-019-0527-6</p>
Biosensor Dataset: Supporting research on biosensor technology for overall water quality monitoring
<p>Dataset regarding vision-based and MEMS-based biosensor technology validation within ASTRAL IMTA labs.</p>
Crypto Price Monitoring Dataset for On-chain Derivatives Research
<p># Crypto Price Monitoring Repository</p> <p>This repository contains two CSV data files that were created to support the research titled "Price Arbitrage for DeFi Derivatives." This research is to be presented at the IEEE International Conference on Blockchain and Cryptocurrencies, taking place on 5th May 2023 in Dubai, UAE. The data files include monitoring prices for various crypto assets from several sources. The data files are structured with five columns, providing information about the symbol, unified symbol, time, price, and source of the price.</p> <p>## Data Files</p> <p>There are two CSV data files in this repository (one for each date):</p> <p>1. `Pricemon_results_2022_11_01.csv`<br> 2. `Pricemon_results_2022_11_08.csv`</p> <p>## Data Format</p> <p>Both data files have the same format and structure, with the following five columns:</p> <p>1. `symbol`: The trading symbol for the crypto asset (e.g., BTC, ETH).<br> 2. `unified_symbol`: A standardized symbol used across different platforms.<br> 3. `time`: Timestamp for when the price data was recorded (in UTC format).<br> 4. `price`: The price of the crypto asset at the given time (in USD).<br> 5. `source`: The name of the price source for the data.</p> <p>## Price Sources</p> <p>The `source` column in the data files refers to the provider of the price data for each record. The sources include:</p> <p>- `chainlink`: Chainlink Price Oracle<br> - `mycellium`: Built-in oracle of the Mycellium platform<br> - `bitfinex`: Bitfinex cryptocurrency exchange<br> - `ftx`: FTX cryptocurrency exchange<br> - `binance`: Binance cryptocurrency exchange</p> <p>## Usage</p> <p>You can use these data files for various purposes, such as analyzing price discrepancies across different sources, identifying trends, or developing trading algorithms. To use the data, simply import the CSV files into your preferred data processing or analysis tool.</p> <p>### Example</p> <p>Here's an example of how you can read and display the data using Python and the pandas library:</p> <p> import pandas as pd<br> <br> # Read the data from CSV file<br> data = pd.read_csv('Pricemon_results_2022_11_01.csv')<br> <br> # Display the first 5 rows of the data<br> print(data.head())` </p> <p> </p> <p>## Acknowledgements</p> <p>These datasets were recorded and supported by <a href="https://datamint.ai">Datamint</a> company (value-added on-chain data provider) and its team.</p> <p><br> ## Contributing</p> <p>If you have any suggestions or find any issues with the data, please feel free to contact authors.</p>
Indigenous Peoples and environmental research and monitoring within the Laurentian Great Lakes Basin: A systematic map
Open the record for dataset details and reuse information.
Greenland Ecosystem Monitoring - Location and Description of Research Stations
<p>This shapefile contains locations and descriptions of the research stations that are part of the Greenland Ecosystem Monitoring Program - https://www.g-e-m.dk .</p> <p>Greenland Ecosystem Monitoring (GEM) is an integrated monitoring and long-term research programme on ecosystems and climate change effects and feedbacks in the Arctic. Since 1995 the programme has established a coherent and integrated understanding of the functioning of ecosystems in a highly variable climate, which is based upon a comprehensive, long-term inter-disciplinary data collection carried out by Danish and Greenlandic monitoring and research institutions. </p> <p>The GEM Programme put around 75 scientists in the field annually to collect data on ecosystem and climate change in Greenland. The data base currently covers data from monitoring programmes from Zackenberg (1995-), Kobbefjord at Nuuk (2007-) and Disko (2017-). The well over 1000 parameters are freely available via the GEM Database and used by GEM participants and external scientists to produce scientific papers, scientific assessments, advisory reports, etc.</p> <p>The monitoring data can be downloaded via https://data.g-e-m.dk</p>
Supplementary material 3 from: Tanaka S (2017) Locusta migratoria (Orthoptera: Acrididae) embryos monitor neighboring eggs for hatching synchrony. Journal of Orthoptera Research 26: 103-115. https://doi.org/10.3897/jor.26.20935
Supplementary material 3 from: Tanaka S (2017) Locusta migratoria (Orthoptera: Acrididae) embryos monitor neighboring eggs for hatching synchrony. Journal of Orthoptera Research 26: 103-115. https://doi.org/10.3897/jor.26.20935
Supplementary material 1 from: Tanaka S (2017) Locusta migratoria (Orthoptera: Acrididae) embryos monitor neighboring eggs for hatching synchrony. Journal of Orthoptera Research 26: 103-115. https://doi.org/10.3897/jor.26.20935
Supplementary material 1 from: Tanaka S (2017) Locusta migratoria (Orthoptera: Acrididae) embryos monitor neighboring eggs for hatching synchrony. Journal of Orthoptera Research 26: 103-115. https://doi.org/10.3897/jor.26.20935
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.