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NWO and ZonMw Open Access Monitor 2023 dataset

This is the full dataset of the NWO and ZonMw Open Access Monitor 2023 report and accompanying Appendix 'Estimating Costs of Open Access Publishing'.  DOI of NWO and ZonMw Open Access Monitor report: <a href="https://doi.org/10.5281/zenodo.12685800">https://doi.org/10.5281/zenodo.12685800  </a> DOI to Appendix ‘Estimating costs of open access publishing’: <a href="https://doi.org/10.5281/zenodo.13885012">https://doi.org/10.5281/zenodo.13885012</a>

STAR4BBS D3.1 Report on sustainability indicators for the monitoring system based on LCA_Appendix C2

This appendix presents the full set of LCA indicators identified in D3.1 for the economic pillar. These 22 indicators are the result of the consultation of various books and scientific articles aligned with the LCC and TEA methodology (specified in D3.1) and were used for the final selection according to pre-established criteria.

STAR4BBS D3.1 Report on sustainability indicators for the monitoring system based on LCA_Appendix C4

This appendix includes some recent research reports that have applied LCA methodology and circularity analysis to different sectors of the bioeconomy. A total of 30 research articles covering the environmental, social and economic pillars of sustainability, as well as circularity assessments, were analyzed.

Succession in abandoned fields: Chronosequence data verified by monitoring of semi-permanent plots - Dataset underlying paper

In task 3.1 of the WILDCARD project (www.wildcard-project.eu), we performed the analyses to identify the similarity between successional patterns in permanent plots and chronosequences on abandoned fields. This data is needed to know the accuracy of a chronosequence approach to reveal general successional vegetation pattens compared to the results  obtained from semi-permanent plots. Raw data was provided by external collaborators. Results can be applied to other types of successional studies and restoratin projects, they verified the reliability of chronosequence approach. Rewiew process (3 reviewers plus coordinationg editor)

STAR4BBS D1.4 Report on existing monitoring schemes_Annex A2

This dataset contains the full list of identified monitoring tools from the grey literature reveiw that is part of the  STAR4BBS deliverable D1.4 (Annex A2). The review of the identified 23 different assessment systems represents the findings, of which 19 monitoring tools were used for the further review of their characteristics, methods and results intepretation. It forms the basis of the further in-depth analysis and is included for transparency.

opencc-by-4.0Sep 2021View details →

Liquid Resin Infusion (LRI) manufacturing and Spring_In monitoring by FBGs, DCs and 3D CMM meassurements

ELADINE project is aiming to implement a numerical tool that can reduce reoccurring costs of low-volume production in composite manufacturing of primary structural elements and thus reducing overall manufacturing effort and carbon emissions. A primary goal of this project is to eliminate tolerance non-compliancy in the manufactured structures caused by natural and unavoidable post-manufacturing distortions, typical for composite materials. These distortions might render otherwise qualitative components unusable due to their final geometry. Objectives of the Numerical model validation are: <ul> <li>To understand the dominant factors which affects the spring-in phenomenon.</li> <li>To provide the simulation tool with the required values of the properties that influence on spring-in.</li> <li>To verify the simulation tool ability to predict spring-in for a variety of conditions.</li> <li>To develop a procedure of adapting and embedding sensors (dielectric and fiber optic) to obtain proper, useful and accurate signals of the manufacturing parameters (T, degree of cure, strain).</li> <li>To develop interpretation procedures of the signal/curves of sensors to obtain on-line process monitoring information.</li> </ul> To obtain the data to feed and develop the numerical tool able to estimate the component distortions after its manufacturing, a combination of Fiber Optic Sensors (FOS) based on Fiber Bragg Grating (FBG) technology, Dielectric Curing sensors (DC) and 3D scanning were used to monitor the composite coupon manufacturing and the distortions the days after being demoulded. During the manufacturing process embedded FBGs and DC sensors were used to monitor the coupon temperature and strain distribution and resin curing evolution. After the manufacturing and the demolding, the distortions evolution were monitored by the embedded FBGs and by 3D CMM measurements. In the ELADINE project, the distortion monitoring was made to two Out-of-Autoclave manufacturing technologies: liquid resin infusion (LRI) and oven cured pre-preg. For both material systems, slightly curved coupons and C-shaped coupons were the geometries selected as representative for the Skin and spars of the wing box. The Skin coupon  was curved panel with a 1475 mm radius (with edge rise of 7,65 mm)  that was thought to best replicate the wing profile geometry. The C-spar coupon geometry selected for the study was a non-tapered spar section with two different angle with radius of curvature of 5mm and 12mm. This geometry was chosen to simplify measuring and comparisons with wing demo. Furthermore, three different thickness are studied for the Skin coupons and two for the C-spar coupons which were selected from different zones along the wing. Moreover, a C-spar coupon with variable thickness was studied, as a simulation of the transition between zones with different thickness in the wing. In this dataset, the data from the FBGs, DCs and 3D CMM meassurements for the LRI manufacturing process and spring_in distortions monitoring is included.

opencc-by-4.0Nov 2022View details →

Datensatz Bodenchemie zu den Büchern "Monitoring des Zustands von Waldböden in Hessen, Niedersachsen, Sachsen-Anhalt und Schleswig-Holstein von 1966 bis 2021"

In den vier CSV-Dateien sind die Messwerte aller Proben enthalten, die in den Büchern "Monitoring des Zustands von Waldböden in Hessen, Niedersachsen, Sachsen-Anhalt und Schleswig-Holstein von 1966 bis 2021" verwendet wurden.

opencc-by-4.0Dec 2022View details →

Telegraf evaluation for AI-SPRINT Monitoring Subsystem

Performance impact evaluation of the AI‑SPRINT Monitoring Subsystem on a system deployment running the AI‑SPRINT Framework with AI applications

opencc-by-4.0Jan 2023View details →

Rainfall data monitored by acoustic sensors in Zurich and Milan during spring and summer 2022

The database contains rainfall information obtained from acoustic sensors and rain gauges (meteoblue AG) in the cities of Zurich (Switzerland) and Milan (Italy) during field work conducted in spring and summer 2022. Zurich: Continuous rainfall data is provided at 15 min intervals for April 2022; data_acoustic_Zurich.csv - number of rain drops, data_meteoblue.csv - rainfall depth (mm). Milan: Data is provided for 5 rain events in June 2022 at 1 min intervals; data_acoustic_Milan.csv - number of rain drops, data_meteoblue.csv - rainfall depth (mm). The locations of the acoustic sensors and rain gauges can be find in the metadata files: Metadata_acoustic.xlsx and Metadata_meteoblue.xlsx The presented-data passed only a primilinary quality control. Further infromation about the senor networks in Milan and Zurich can be found here: https://doi.org/10.5194/nhess-2022-257

opencc-by-4.0Mar 2023View details →

Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments

ContextThis dataset is part of the paper "Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments" presented at Oceans 2022, Hampton Roads, DOI: <a href="https://doi.org/10.1109/OCEANS47191.2022.9977024">10.1109/OCEANS47191.2022.9977024</a>This dataset consists of paired camera and multi-beam sonar images of technical divers performing different underwater tasks in two locations: an indoor test basin and a lake. The general goal is to assist emergency operators that monitor the safety of divers operating in bad visibility conditions.This data was used to train image-to-image translation models in order to generate realistic optical-like images given only sonar images as input or a combination of a sonar image and a dark or turbid optical image. ContentThis repository contains three .zip folders each containing data collected in a different lab or field trial.<ul><li>'basin-dataset-1.zip' and 'basin-dataset-2.zip' contain data that were collected in an indoor testing facility at DFKI - Robotics Innovation Center, Bremen, Germany.</li><li>'lake-dataset-1.zip' and 'lake-dataset-2.zip' contains data collected at lake Kreidesee, Hemmoor, Germany.</li></ul>Each .zip file contains two subfolders labelled as 'camera' and 'sonar', each containing the images in png format. Data files under these subfolders with matching names composes a pair of time-synchronized images. For example, 'camera/0001.png' corresponds to 'sonar/0001.png'. The acquisition timestamp represented in seconds since epoch for every data file is recorded in 'sample.csv' include in each .zip file.For more details and meta-information on the collected data please refer to "data_description.json" included in this repository.Additional tools for handling and preparing the data can be found under <a href="https://github.com/DeeperSense/oceans_2022">https://github.com/DeeperSense/oceans_2022</a> AcknowledgementsThe data in this repository were collected as a joint effort between the German Center for Artificial Intelligence (DFKI), the German Federal Agency for technical Relief (THW), and Kraken Robotics GmbH. This work is part of the project DeeperSense that received funding from the European Commission. Program H2020-ICT-2020-2 ICT-47-2020 Project Number: 101016958.The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their initiative within the framework of the NFDI4Ing consortium (German Research Foundation (DFG) - project number 442146713).

opencc-by-4.0Mar 2023View details →

Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2021 in China (v2.0)

The Enhanced Vegetation Index (EVI), Land Surface Temperature (LST) and Precipitation (P) were used as new data sources based on the spatial distance model to construct an optimized multi-source remote sensing dryness index named Temperature-Vegetation-Precipitation Dryness Index based on the shortcomings of the TVPDIorigin (i.e., TVPDIo) data source. The TVPDIn of the long time series was also compared and analyzed with the classical drought index - Standardized Precipitation Evapotranspiration Index (SPEI-3) on a 3-month scale, different drought response level products of Solar-Induced Chlorophyll Fluorescence (SIF), soil moisture (SM) from ESA CCI (European Space Agency's Climate Change Initiative), and total crop yield, then the sensitivity and validity of the TVPDIn for wetness and dryness monitoring were synthesized and validated. On this basis, here are the results of the verification: (1) Compared with the original data source TVPDIo using the new multi-source remote sensing data source of precipitation and vegetation index to construct TVPDIn, the overall correlation between the two and SPEI-3 was good, with a maximum of 0.57 and 0.56, respectively (p< 0.1), but the overall TVPDIn constructed in this study had a better fit compared to the original data source TVPDIo and was more sensitive to the monitoring of dry and wet conditions. (2) According to the comparison of TVPDIn with ESA CCI sm, TVPDIn showed a high correlation of more than 0.9 with soil water content, which proved that TVPDIn was highly consistent with soil moisture; compared with SIF, 54.5% of the regional correlation coefficients were greater than 0.8 (p< 0.01), and spatially, the correlation results were better in the northwest than in the east, indicating that the response of TVPDIn to vegetation productivity is more agile in regions with continental climate such as the northwest. The results of correlation with grain yield comparison showed that good positive correlations were presented with TVPDIn in Liaodong Peninsula, northern North China Plain, and most of Qilian Mountains, southern edge of Qinling Mountains, middle and lower reaches of Yangtze River, and South China, indicating that TVPDIn has a high consistency in the changes of agricultural grain production in the above mentioned regions, and also proving the index in monitoring agricultural aridity and guiding agricultural production The good performance of the index in monitoring agricultural aridity and guiding agricultural production.  This dataset is version 2.0, and covers all of China's territory, but the temperature-vegetation- precipitation dryness index of the open water surface are often set to a null value. Note:The data format is "TIF", the spatial resolution is "1 km", the time resolution is "1 month" and dimensionless. The pixel value is the NTVPDI value, and the closer the pixel value is to 0, the drier it is, and the larger the data, the wetter the land surface. The practical utility of this dataset is to compare the degree of dryness and wetness of China's land, to monitor short-term and medium-term droughts, and to substitute model parameters related to soil moisture. This is of great value to the impartial formulation of China's environmental and economic policies, regular monitoring and evaluation of drought and flood conditions.  This product will be freely available to all users worldwide and will be continuously improved to suit new goals and needs.

opencc-by-4.0Mar 2023View details →

Supplementary dataset to publication: "Elevated platforms with integrated weighing beams allow automatic monitoring of usage and activity in broiler chickens"

The dataset supplements the journal article "Elevated platforms with integrated weighing beams allow automatic monitoring of usage and activity in broiler chickens" by H. Schomburg, J. Malchow, O. Sanders, J. Knöll and L. Schrader, that appeared in Smart Agricultural Technology 3 (2023), https://doi.org/10.1016/j.atech.2022.100095. The file archives trial1.zip and trial2.zip contain csv files with platform weighing system data measured from June 19, 2019 to July 22, 2019 (trial 1) and from September 9, 2019 to October 14, 2019 (trial 2) in a broiler chicken barn at Friedrich-Loeffler-Institut, Institute of Animal Welfare and Animal Husbandry, Celle. A detailed description of data structure is given in 00_hl_weighing_system_data_overview.txt.

opencc-by-4.0May 2023View details →

Dataset for 'Room-temperature monitoring of CH4 and CO2 using a metal-organic framework-based QCM sensor showing inherent analyte discrimination'

Associated data for the manuscript 'Room-temperature monitoring of CH4 and CO2 using a metal-organic framework-based QCM sensor showing inherent analyte discrimination' (doi://10.26434/chemrxiv-2023-djhp2)

opencc-by-4.0Apr 2023View details →

SCShores: time-series of shorelines from Spanish Sandy beaches from citizen-science monitoring program.

This repository contains 5 years of sandy beaches shorelines deriverd from a citizen-science monitoring program in the Spanish coast. The methodology and the dataset are described in: González-Villanueva, R., Soriano-González, J., Alejo, I., Criado-Sudau, F., Plomaritis, T., Fernàndez-Mora, À., Benavente, J., Del Río, L., Nombela, M. Á., and Sánchez-García, E.: SCShores: a comprehensive shoreline dataset of Spanish sandy beaches from a citizen-science monitoring programme, Earth System Science Data. V. 15, 4613-4629 , <a href="https://essd.copernicus.org/articles/15/4613/2023/essd-15-4613-2023.html">https://doi.org/10.5194/essd-15-4613-2023</a>, 2023.  The shoreline dataset is provided in 1 GEOJSON file: SCShores.geojson. This dataset covers five sandy beaches located on the Atlantic and Mediterranean coasts of Spain where CoastSnap stations were available, and it includes a total of 1721 shorelines. The coordinate system for the geospatial layer is WGS84. <ul> <li>SCShores.geojson: this layer contains the sandy shorelines . Each feature in this layer is a multipoint with the following attributtes: <ul> <li>site: CoastSnap station name id, e.g. agrelo, samarador, cadiz, ….</li> <li>date: date and time of the shoreline, yyyyy-mm-dd hh:mm:ss</li> <li>timezone: Coordinated Universal Time, UTC</li> <li>timestampQuality: quality flag indicating the confidence in the date-time indicated by the image provider, e.g. 1, 2</li> <li>imageSource: source of the original image from which the shoreline has been derived, e.g. Instagram, Twitter, Facebook, Email, CoastSnapApp</li> <li>elevation_m: same as Z coordinate, defined by the observed tide and the tidal offset, in meters, Tide+tide offset</li> <li>verticalDatum: mean sea level in Alicante, which is considered the zero topographic reference in the Spanish territory, NMMA</li> <li>geometry: type of geometry used in the file, MultiPoint</li> <li>coordinates: Geographic WGS84 coordinates for each point in the geometry, longitude, latitude, Z</li> </ul> </li> </ul>

openCC (other)Jun 2025View details →

Interagency Ecological Program: Benthic invertebrate monitoring in the Sacramento-San Joaquin Bay-Delta, collected by the Environmental Monitoring Program, 1975-2024.

The Interagency Ecological Program’s (IEP) Environmental Monitoring Program (EMP) was initiated in compliance with the Water Right Decision D-1379 (now mandated by Water Right Decision D-1641) and has monitored benthic invertebrate macrofauna in the upper San Francisco Estuary (SFE) since 1975. The objectives of the EMP are to obtain consistent and accurate monthly data at established monitoring stations and to report this information for the purpose of management and conservation of the upper San Francisco Estuary. While the EMP also collects discrete and continuous water quality data, along with phytoplankton and zooplankton data, this dataset only includes the benthic invertebrate data collected by the EMP from 1975-2024. EMP monitors invertebrate communities in the benthos of the SFE by collecting dredge samples with a Ponar sampler. Sediment and particles smaller than 0.5mm are removed from the sample using a sieve table. The invertebrates present are preserved in formalin, identified to the lowest possible taxonomic level, and enumerated. Currently, samples are collected monthly at 10 sites across the range of salinities found in the SFE. Four replicate dredge samples are collected at each site. The frequency of sampling, number and identity of sampling sites, and number of replicate samples has changed through the 45+ years of monitoring effort, in response to changes in perceived need for data. Links to other EMP datasets can be found on the EMP website: https://emp-des.github.io/emp-reports/data-links.html, or can be found on EDI for searching for "Environmental Monitoring Program" and "San Francisco".

openCC (other)Dec 2021View details →

Sacramento trawl, Delta Juvenile Fish Monitoring Program, Genetic Determination of Population of Origin 2017-2021

Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is often difficult to distinguish individuals from the different Evolutionarily Significant Units. As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to race; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program monitoring programs. The population-of-origin was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central Valley salmonid monitoring

openCC (other)Dec 2021View details →

Chipps Island trawl, Delta Juvenile Fish Monitoring Program, Genetic Determination of Population of Origin 2017-2021

openCC (other)Aug 2024View details →

Fish abundance in the San Francisco Estuary (1959-2024), an integration of 10 monitoring surveys.

The San Francisco Estuary (SFE) is simultaneously a central hub of water delivery in California and home to commercially important and endangered fishes, such as Chinook Salmon, Green Sturgeon, and Delta and Longfin Smelt. Extensive ecological monitoring has been conducted for over 50 years, mainly under the auspices of the Interagency Ecological Program for the San Francisco Estuary (https://iep.ca.gov/). We integrated fish catch and length data from 10 long-term monitoring surveys in the SFE. The integrated database contains survey-level data such as environmental variables and sampling effort in addition to the fish-level species, lengths, and counts. Zero catches have been filled in for any species not caught in a sample. The geographic scope includes San Francisco Bay through the upper estuary, and the timeseries spans 1959 to 2024. Sampling methods, gear, fish length metric, and other factors differ among the component surveys. Sampling designs (locations and temporal frequency) have also changed over time. Thus, it is highly recommended to inspect the documentation of the component surveys for more information on their methods.

openCC (other)Jan 2025View details →

Interagency Ecological Program: Zooplankton and water quality data in the San Francisco Estuary collected by the Summer Townet and Fall Midwater Trawl monitoring programs.

The Interagency Ecological Program’s (IEP) Summer Townet Survey (STN) and Fall Midwater Trawl (FMWT) are two long-term monitoring projects conducted by the California Department of Fish and Wildlife (CDFW) to monitor fish abundance and distribution trends in the San Francisco Estuary (SFE) since 1959 and 1967, respectively. Starting in 2005, zooplankton monitoring was added and paired with fish tows to investigate food availability for young fishes. Food limitation has been a long-term issue and a focus of the Pelagic Organism Decline (POD) studies that began in 2005. By 2011, STN routinely conducted zooplankton monitoring at 40 stations, and FMWT at 32 stations in the upper SFE from Carquinez Strait to the Sacramento Deep Water Ship Channel and into the South Delta. STN samples every other week from June to August and FMWT samples once monthly from September to December. Both projects collect mesozooplankton samples using a modified Clarke-Bumpus (CB) net to target copepods and cladocerans, and FMWT also samples macrozooplankton (i.e. mysids and amphipods) using a mysid net. Flowmeters are used to measure the volume sampled to determine zooplankton catch per unit effort. Environmental variables such as water temperature, turbidity, secchi, and electrical conductivity are collected with each zooplankton sample. Concurrent fish and zooplankton tows conducted by STN and FMWT have allowed for comparisons of fish diet to the available zooplankton prey at the time of collection.