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2,727 results for “Cost”
Construction Costs of Carnivorous and Non-Carnivorous Plants at Harvard Forest 2006-2008
Leaf traits, including photosynthetic rates, leaf mass area, and leaf nutrient content covary in a coordinated way for a wide range of plant taxa. This covariation results from trade-offs between the costs of constructing plant tissues and the benefits accrued from photosynthesis. Carnivorous plants have been found to be outliers in the "universal spectrum of leaf traits" because they have very low photosynthetic rates for the amount of nitrogen in their leaves and traps. But no studies have measured simultaneously the actual construction costs of carnivorous traps and rates of photosynthesis to determine the amortization required to recover the investment (the "payback time") and thereby calculate the "marginal gain" of "investing" in carnivorous structures. The objective of this study was to measure construction costs (CCmass, grams of glucose required to build 1g of ash-free dry mass of tissue) and photosynthesis (Amass, nmol CO2 g-1 s-1) for traps, leaves, roots, and rhizomes of 15 carnivorous plant species with differing mechanisms of prey capture and consumption (pitfall traps, snap-traps, sticky pads) grown under greenhouse conditions. Payback time (h) was calculated as the quotient of CCmass and Amass after conversion to nmol of carbon per gram of ash-free dry mass. There were highly significant differences amongst species for CCmass of traps but there were no significant differences for CCmass amongst traps, roots and rhizomes. Mean (+- SD) CCmass for traps (1.14 +- 0.24 g glucose g-1) was significantly lower than the mean CCmass of leaves of 267 non-carnivorous plant species (1.47 +- 0.17 g glucose g-1). However, all 15 carnivorous plants examined in this study had low Amass and thus, the marginal gain of carnivory is small with a long payback time (524-1641 h). Our results of low CCmass for carnivorous traps is contrary to the oft-stated expectation of a high cost to construct elaborate carnivorous traps. Payback time integrates traits used to assess leaf
Exploring the total cost of whole fresh, fresh-cut and pre-cooked vegetables
<p>Abstract. Purpose: The food industry should evolve towards new business models which take into account the damage cost in decision making, considering the impact that its products generate on the natural and human environment. Hence, the present study aims to calculate the damage cost caused by the production of whole fresh (as average of potatoes, aubergines, and broccoli), and processed vegetables (fresh-cut and pre-cooked). Methods: The environmental life cycle approach was carried out per kilogram of assessed products (from cradle to the entrance of the market). The foreground Life Cycle Inventory was obtained from engineering procurement and construction projects of the whole fresh and processed vegetables industries. The Ecoinvent 3.8 and Agribalyse 3.0.1 databases were used for the background inventory. The ReCiPe 2016 method was used with a hierarchical perspective, evaluating eighteen midpoint categories as well as the endpoint categories (human health, ecosystems, and resources). The monetisation of these environmental impacts was then calculated using the endpoint monetisation factors developed by Ponsioen et al. (Monetisa- tion of sustainability impacts of food production and consumption. Wageningen Economic Research, Wageningen, 2020) for each product. It should be noted that this study does not include a comparative assessment. This study does not intend to compare the results for the three vegetable groups. Results and discussion: The damage costs were 0.16 €/kg for whole fresh vegetables, 0.37 €/kg for fresh-cut vegetables and 0.41 €/kg for pre-cooked vegetables. The agricultural production stage contributed most to these total damage costs due to the impact produced on land use and global warming in midpoint categories and human health and ecosystems in endpoint categories. In addition, the damage cost due to fossil resource scarcity (midpoint) and resource scarcity (endpoint) was mainly caused by the plastic packaging of fresh-cut and pre-cooked vegetables. The total cost was 1.02 €/kg for whole fresh vegetables, 2.99 €/kg for fresh-cut vegetables, and 3.43 €/kg for pre-cooked vegetables. Conclusions: These results suggest that some efforts should be made to reduce both environmental impacts and damage costs. For instance, to improve agricultural production, special attention should be paid to fertilisation and water consumption. Additionally, new packaging options should be explored as well as the inclusion of renewable sources in the electricity grid, and finally, on transporting the finished products to the market, by using trucks that run on cleaner fuels.</p>
Extended data for the paper: "SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters"
<p>Extended data 1 to 4 for the software article:<br>SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters. </p> <p>The extended data is tables and a Figure output and input from/to SentemQC runs relevant for the SentemQC paper.</p>
Cost Analysis TBI
Open the record for dataset details and reuse information.
Austrian Science Fund (FWF) Publication Cost Data 2014
<p>Following 2013 (http://dx.doi.org/10.6084/m9.figshare.988754), the Austrian Science Fund (FWF) makes its publication costs spent in 2014 (esp. for Open Access) publically available.</p> <p>The dataset includes payments for publications of authors funded by the Austrian Science Fund (FWF) via following programmes:</p> <p>"Peer-Reviewed Publications": https://www.fwf.ac.at/en/research-funding/fwf-programmes/peer-reviewed-publications/</p> <p>"Stand-Alone Publications": https://www.fwf.ac.at/en/research-funding/fwf-programmes/stand-alone-publications/</p> <p>In addition to 2013, this dataset includes also costs for Open Access books and other venues.</p>
AirMLP - SPS30 low-cost sensors and Tecora reference station PM 2.5 data
<p>The information below describes a dataset related to a study conducted in Turin, Italy, involving low-cost laser-scattering SPS30 sensors placed by Wiseair SRL and a Tecora reference station placed by Arpa Piemonte (Italian Air Quality Agency). This dataset spans two different time periods in 2022, specifically from March 1, 2022, to April 29, 2022, and from October 26, 2022, to December 30, 2022. The data in this dataset pertains to the mass concentration of PM2.5 (particulate matter with a diameter of 2.5 micrometres or less).</p><p> </p><p>The reference station's data is divided into two periods and is provided in files named "rf_x.csv." These files contain hourly data and timestamps in GMT+1. Each file has three columns:</p><ul><li>"valid_at" (in Rome local hour, GMT+1)</li><li>"valore_originale" (PM 2.5 raw mass concentration values recorded by the reference station)</li><li>"pm2p5" (PM 2.5 mass concentration validated values by the air quality agency)</li></ul><p>The low-cost sensors, referred to as "ari_xxxx.csv," provide data at approximately 15-minute frequency. These files contain the following columns:</p><ul><li>"valid_at" (in GMT)</li><li>"pm2p5" (PM 2.5 raw mass concentration measured by the SPS30 sensor)</li><li>"relative_humidity" (expressed as a percentage)</li><li>"temperature" (in degrees Celsius)</li><li>"pressure" (in hPA)</li><li>"wind_speed" (in meters per second)</li><li>"cloud_coverage" (expressed as a percentage)</li></ul><p>Notably, the "relative_humidity" and "temperature" values are gathered from sensors placed within a device containing the SPS30 low-cost sensor.</p><p> </p><p>Here's a summary of the specific data files in this dataset:</p><ul><li>"<strong>rf_1.csv</strong>": Hourly data provided by the Air Quality Agency for the first period.</li><li>"<strong>rf_2.csv</strong>": Hourly data provided by the Air Quality Agency for the second period.</li><li>"<strong>arpa_1727.csv</strong>," "<strong>arpa_1952.csv</strong>," and "<strong>arpa_1953.csv</strong>": Three low-cost sensors placed by Wiseair, which refer to the first period.</li><li>"<strong>arpa_1885.csv</strong>" and "<strong>arpa_2049.csv</strong>": Two low-cost sensors placed by Wiseair, that refer to the second period.</li></ul>
High-resolution images from a low-cost imaging device for hyphae in soil
<p>This dataset contains high-resolution images produced by a low-cost imaging device for hyphae in soil called <em>Hyphascope</em>. Using a digital microscope camera (DMC; 600× magnification),<em> </em>the device takes detailed images (0.83 × 0.62 mm imaged area) of a soil profile from evenly spaced camera positions within a user-defined volume. Repeated imaging of a soil profile with <em>Hyphascope</em> enables researchers to observe and quantify changes in the amount, distribution, and morphology of hyphae.</p> <p>Individual images were combined using the <em>Grid/Collection stitching</em> plugin of the <em>Fiji</em> distribution of <em>imageJ</em> (Preibisch et al. 2009). All images are supplied in the JPG format to limit their file size. For more details on the assembly and application of <em>Hyphascope</em>, see <a href="https://doi.org/10.17504/protocols.io.bp2l6xo3zlqe/v1">this protocol on protocols.io</a>. For information on the development, limitations, and expected outcomes of the protocol, see <a href="https://doi.org/10.1371/journal.pone.0318083">this article</a> published in PLOS ONE. </p> <p> </p> <div> <h2>Image set 1: 10 × 10 mm soil profile area at 20 - 30 mm soil depth</h2> <p>Imaged at 0.65 μm px<sup>-1</sup> (39200 dpi)* in a <em>Quercus serrata</em> grove on 2023/05/25 during a period of high hyphal density in the soil.</p> <h3>Individual images (18 rows × 14 images each)</h3> <ul> <li> <p><em>set1_foc00000.zip</em> (focus depth 0 mm)</p> </li> </ul> <h3>Combined images</h3> <ul> <li> <p><em>set1_foc00000_combined.jpg</em> (focus depth 0 mm)</p> </li> </ul> <h2>Image set 2: 5 × 5 mm soil profile area at 100 - 105 mm soil depth</h2> <p>Imaged at 0.52 μm px<sup>-1</sup> (49000 dpi) in a <em>Quercus serrata</em> grove on 2023/09/25.</p> <h3>Individual images (9 rows × 7 images each)</h3> <ul> <li> <p><em>set1_foc00000.zip</em> (focus depth 0 mm)<em><br></em></p> </li> <li> <p><em>set1_foc00025zip</em> (focus depth 0.025 mm)</p> </li> <li><em>set1_foc00050.zip</em> (focus depth 0.05 mm)</li> </ul> <h3>Combined images</h3> <ul> <li> <p><em>set1_foc00000_combined.jpg</em> (focus depth 0 mm)</p> </li> <li> <p><em>set1_foc00025_combined.jpg</em> (focus depth 0.025 mm)</p> </li> <li><em>set1_foc00050_combined.jpg</em> (focus depth 0.05 mm)</li> </ul> <h2>Image set 3: 5 × 5 mm soil profile area at soil surface level</h2> <p>Imaged at 0.52 μm px<sup>-1</sup> (49000 dpi) in a <em>Quercus serrata</em> grove on 2023/10/14 during a rain event.</p> <h3>Individual images (9 rows × 7 images each)</h3> <ul> <li> <p><em>set2_foc00000.zip</em> (focus depth 0 mm)<em><br></em></p> </li> <li> <p><em>set2_foc00025zip</em> (focus depth 0.025 mm)</p> </li> <li><em>set2_foc00050.zip</em> (focus depth 0.05 mm)</li> </ul> <h3>Combined images</h3> <ul> <li> <p><em>set2_foc00000_combined.jpg</em> (focus depth 0 mm)</p> </li> <li> <p><em>set2_foc00025_combined.jpg</em> (focus depth 0.025 mm)</p> </li> <li><em>set2_foc00050_combined.jpg</em> (focus depth 0.05 mm)</li> </ul> <p> </p> <p> </p> <p><em>*Units of imaging resolution: </em></p> <ol> <li><em>pixel width (μm px-1), i.e. the horizontal or vertical distance on the imaged surface covered by a single pixel; </em></li> <li><em>dots per inch (dpi), i.e. the number of pixels along a horizontal or vertical distance of 25.4 mm on the imaged surface.</em></li> </ol> </div>
Recommended food alternatives (healthier, eco-friendly, and cost-effective)
<p>It includes recommended food alternatives (healthier, eco-friendly, and cost-effective) for items selected from receipts. This dataset is valuable for research in consumer food science, as it captures the food choices of a small group of consumers over 21 days. It is also useful for machine learning training. All food items are linked to NAct ontology.</p> <p> </p>
Data for "emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves"
<p>This dataset contains codes, data, tables, andd figures (high resolution) related to the following publication: Xiong, W., K. Tanaka, P. Ciais, D. J. A. Johansson, M. Lehtveer (2022) emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves. Submitted to arXiv on 23 December 2022.</p>
The potential of low-cost UAVs and open-source photogrammetry software for high-resolution monitoring of alpine glaciers: A case study from the Kanderfirn (Swiss Alps)
<p>This dataset contains high-resolution orthophotos (5 x 5 cm) and digital surface models (25 x 25 cm) of the Kandernfirn Glacier located in the Swiss Alps. Aerial images were aquired with a self-developed fixed-wing Unmanned Aerial Vehicle during ten surveys on five different days in 2017 and 2018. The open-source photogrammetry software OpenDroneMap (version 0.4.1) was used for image processing.</p> <p>The orthophotos and digital surface models were validated through dGNSS point measurements of ground control points. Please refer to the corresponding paper for information on the horizontal and vertical accuracy of the files.</p>
Respectable Standards of Living: The Alternative Lens of Maintenance Costs, Britain 1270-1860
<p>Data set and code book. Replication materials for paper accepted in Economic History Review, April 2024.</p> <p>Abstract </p> <p><span>This paper argues that in all societies there is considerable agreement about what goods and services are needed to provide a decent living, and that this standard can be measured by the expense involved in maintaining people of good standing.<span> </span><span> </span>Maintenance costs include two components of living costs that are neglected in conventional approaches.<span> </span>First, in contrast to the usual focus on a fixed basket of commodities, maintenance costs capture changes in the composition and quality of the goods required for a respectable lifestyle.<span> </span>Second, unlike the conventional accounting they include the costs of the household services required to turn the basket commodities into livings. Ignored in the conventional methodology, the inclusion of these costs represents a core innovation. More than 4600 observations, drawn mainly from primary sources, trace levels and trends in maintenance costs for Britain, 1270-1860. <span> </span>These can be compared with established cost of living indicators to offer a complementary perspective on real consumption that accommodates aspirational goods and the input of household labour.<span> </span>The struggle to support families at respectable standards emerges as driving industriousness and motivating prudence among a class that played a major role in economic development.<span> </span><span> </span></span></p> <p> </p>
Air Traffic Management hotspots in Europe with airline cost functions
<p>This dataset contains data related to Air Traffic Management hotspots. Hotspots are created in the European airspaces when capacity for some pieces of airspace are foreseen to be infringed due to weather, congestion, strikes, etc. This anonymised dataset records around 5900 hotspots happening at 22 major European airports. These hotspots are generated through a simulator called Mercury that is fed with real data (in particular, real capacity reduction that happened in Europe for over a year, schedules etc) and simulates a day of operation, randomising events like delays, cancellation etc. More details on mercury can be found here [1] and [2].</p> <p>The data, anonymised in terms of airports and airlines, is a dictionary which is structured as follows:</p> <p>- the top level key is the id of the airport, the value is list a of all regulations available for this airport.</p> <p>- each item of the list is a dictionary, with keys:</p> <p> -- 'slot_times': list of all slots available to flights for this hotspot/regulation, in minutes since midnight.</p> <p> -- 'etas': list of initial estimated arrival times of flights involved in the regulation, in minutes since midnight.</p> <p> -- 'flight_ids': list of flight ids (in the same order than etas)</p> <p> -- 'cost_vectors': list of cost vectors. Each item is a list itself, of length equal to the slot_times list. Each element of that list is the estimated cost that the airline owning the flight would incur, were the flight be assigned to this slot, in terms of: maintenance, crew, rebooking fees, market value loss, and curfew infringement, in 2014 euros. This cost is computed within the Mercury model and is based on [3].</p> <p> -- 'airlines_flights': dictionary whose keys are airline ids and values are lists of ids of flights owned by the airline.</p> <p>[1] https://www.sciencedirect.com/science/article/abs/pii/S0968090X21003600 </p> <p>[2] G. Gurtner, L. Delgado, and D.Valput, “An agent-based model for air transportation to capture network effects in assessing delay management mechanisms”, Transportation Research Part C: emerging Technologies, 2021.</p> <p>Pre-print available here: <a href="https://westminsterresearch.westminster.ac.uk/item/v956w/an-agent-based-model-for-air-transportation-to-capture-network-effects-in-assessing-delay-management-mechanisms">https://westminsterresearch.westminster.ac.uk/item/v956w/an-agent-based-model-for-air-transportation-to-capture-network-effects-in-assessing-delay-management-mechanisms</a></p> <p>[3] A. J. Cook and G. Tanner, “European airline delay cost reference values - updated and extended values (Version 4.1),” University of Westminster, London, 2015a</p>
Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI) [raw datasets]
<p>Raw datasets accompanying the analysis in "Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI)"</p> <p>The datasets contain raw fluorescence microscopy images aimed to be processed in a SOFI analysis. They are acquired with different camera technologies, allowing for direct comparison of an industry-grade CMOS detector with both a scientific-grade sCMOS and emCCD detector.</p>
GNSS troposphere products from a network of low-cost GNSS receivers, Wroclaw, Poland, March-April 2021
<p>This dataset contains multi-GNSS troposphere products, i.e. ECEF coordinates, zenith total delay (ZTD), and horizontal gradients, together with their uncertainties. These products were obtained using 3 processing strategies:</p> <p>1) real-time (for details see https://link.springer.com/article/10.1007/s10291-020-01014-w, under to "advanced strategy" configuration, with the exception that only GPS and Galileo observations were considered);</p> <p>2) near real-time (NRT, for details see http://egvap.dmi.dk/);</p> <p>3) final (using CSRS online service, https://webapp.geod.nrcan.gc.ca/geod/tools-outils/ppp.php).</p> <p>Products are stored as standard Matlab MAT files. Each file contains a set of table arrays (Matlab format). Each table array contains the selected set of estimated parameters for a single station. Table columns are labeled and self-explanatory. A comma-delimited text file can be obtained using the in-build Matlab function "writetable.m".</p> <p>For convenience, the same information is stored in alternative data formats:</p> <p>1) for NRT and Final products: troposphere SINEX v1 (TRO / TRP)</p> <p>2) for real-time products: semicolon-delimited text files, with a self-explanatory header line; each file contains daily products for one station.</p>
Costs and Benefits of Energy Communities - Collection of Literature
<p>The files contain references to studies of different impacts of energy communities, based on the collection reviewed in Berka & Creamer (2018) and with some additions. The typology of impacts differs from that used by Berka and Creamer.</p>
Input data to replicate "The social cost of tropical cyclones"
<p>Input data for the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a> that replicate the results of <a href="https://doi.org/10.1038/s41467-023-43114-4">Krichene et al. 2023</a>.</p> <p>To run the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a>, the following files from this repository need to be placed in the <code>./data/input/</code> subdirectory of the project folder containing the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a>:</p> <ul> <li><code>GMT.nc</code>: Global mean temperature time series as used by the <a href="https://gitlab.pik-potsdam.de/tovogt/tc_emulator">tropical cyclone emulator</a>.</li> <li><code>GrowthClimateDataset.dta</code>: The <a href="https://purl.stanford.edu/wb587wt4560">input data</a> of <a href="https://dx.doi.org/10.1038/nature15725">Burke et al. 2015</a>.</li> <li><code>IHME_GLOBAL_GDP_ESTIMATES_1950_2015.csv</code>: Historical GDP per capita data from <a href="https://doi.org/10.1186/1478-7954-10-12">James et al. 2012</a> (downloaded from <a href="https://ghdx.healthdata.org/record/ihme-data/gross-domestic-product-gdp-estimates-country-1950-2015">IHME</a>).</li> <li><code>mean_temperature_gswp3-w5e5.csv</code>: Population-weighted average national temperature time series for the historical period.</li> <li><code>pulse_response_ricke_caldeira_2014.csv</code>: The global mean temperature response of an additional emission pulse according to <a href="https://dx.doi.org/10.1088/1748-9326/9/12/124002">Ricke & Caldeira 2014</a>.</li> <li><code>tcdata/TCE-DAT_historic-exposure_1950-2015.csv</code> and <code>tcdata/TotalPopulation.csv</code>: Historical (national) numbers of people affected by tropical cyclones according to <a href="https://doi.org/10.5880/pik.2017.011">TCE-DAT</a> with the corresponding total population counts.</li> <li><code>tcdata/emulator/</code>: Projected (national) shares of people affected by tropical cyclones according to the <a href="https://gitlab.pik-potsdam.de/tc_cost/tc_emulator">tropical cyclone emulator</a> as computed by the scripts in the <a href="https://gitlab.pik-potsdam.de/tc_cost/tc_people_affected">corresponding repository</a>.</li> <li><code>wid_all_data.zip</code>: A bulk data set from the <a href="https://wid.world/bulk_download/wid_all_data.zip">World Inequality Database</a>.</li> </ul> <p>For more information, see <a href="https://doi.org/10.1038/s41467-023-43114-4">Krichene et al. 2023</a> and the README file provided with the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a>.</p>
Evaluation and Calibration of a Low-cost Particle Sensor in Ambient Conditions Using Machine Learning Methods
<p>Particle sensing technology has shown great potential for monitoring particulate matter (PM) with very few temporal and spatial restrictions because of its low-cost, compact size, and easy operation. However, the performance of low-cost sensors for PM monitoring in ambient conditions has not been thoroughly evaluated. Monitoring results by low-cost sensors are often questionable. In this study, a low-cost fine particle monitor (Plantower PMS 5003) was co-located with a reference instrument, named Synchronized Hybrid Ambient Real-time Particulate (SHARP) monitor, in Calgary Varsity air monitoring station from December 2018 to April 2019. The study evaluated the performance of this low-cost PM sensor in ambient conditions and calibrated its readings using simple linear regression (SLR), multiple linear regression (MLR), and two more powerful machine learning algorithms using random search techniques for the best model architectures. The two machine learning algorithms are XGBoost and feedforward neural network (NN). Field evaluation showed that the Pearson r between the low-cost sensor and the SHARP instrument was 0.78. Fligner and Killeen (F-K) test indicated a statistically significant difference between the variances of the PM<sub>2.5 </sub>values by the low-cost sensor and by the SHARP instrument. Large overestimations by the low-cost sensor before calibration were observed in the field and were believed to be caused by the variation of ambient relative humidity. The root mean square error (RMSE) was 9.93 when comparing the low-cost sensor with the SHARP instrument. The calibration by the feedforward NN had the smallest RMSE of 3.91 in the test dataset, compared to the calibrations by SLR (4.91), MLR (4.65), and XGBoost (4.19). After calibrations, the F-K test using the test dataset showed that the variances of the PM<sub>2.5</sub> values by the NN and the XGBoost and by the reference method were not statistically significantly different. From this study, we conclude that feedforward NN is a promising method to address the poor performance of the low-cost sensors for PM<sub>2.5</sub> monitoring. In addition, the random search method for hyperparameters was demonstrated to be an efficient approach for selecting the best model structure.</p>
Raw SNR data for Manuscript "GPS Interferometric Reflectometry : Using a Low Cost Antenna to Measure Water Levels"
<p>Raw GPS L1 SNR (and ancillary) data for an experiment to use a low-cost GPS antenna/receiver to measure water levels using the GNSS - Interferometric Reflectometry technique.</p> <p>The data were recorded at the RNLI lifeboat station in Sligo, Ireland (N 54<sup>o </sup>18' 17.8'', W 8<sup>o</sup> 34' 5.4'' ) using a Globalsat BU353S4 USB puck that uses a SirfStar IV receiver with patch antenna (2018 data) and a Maestro A2200A SirfStar IV module (2019 data). Both systems were mounted to a radio mast at around 16m above sea level.</p> <p>The data are stored in daily files with the naming convention sligDDD0.YY.TNR.gz where DDD is the Day of Year and YY is the year in short format (18,19). Each file is gzipped. </p> <p>The files are flat text files with fixed width columns in the following order</p> <p>1) PRN GPS satellite code</p> <p>2) Elevation (degrees)</p> <p>3) Azimuth (degrees)</p> <p>4) Seconds of Day</p> <p>5) change in elevation angle with time (degrees/second) : needed for reflector height change corrections</p> <p>6) Blank</p> <p>7) S1 SNR signal (dB-Hz)</p> <p>8) Blank reserved for S2 SNR signal</p> <p>9) Blank reserved for S5 SNR signal</p>
Result data related to "Tröndle et al (2020) -- Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe"
<p>The dataset contains aggregated result data of our study. See `README.md` for more information.</p> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 1.2 (2020-09-28)</p> <p>* Add location name to scenario results.<br> * Add scenario results in CSV format, next to already existing NetCDF format.<br> * Remove capacity factors from scenario results.</p> <p>Version 1.1 (2020-07-17)</p> <p>* Remove macOS resource forks cluttering the zip file.</p> <p> </p>
Least cost network data for ancient camel transportation in the Eastern desert of Egypt - Desert Networks HiSoMA CNRS
<p>This repository contains the data necessary for the realization of a least cost network for camel transport during antiquity (Ptolemaic and Roman period) in the Egyptian eastern desert. The details of the network construction and data processing can be found in the the associated paper and datapaper.</p> <p>Study paper:<br> Manière, L., Crépy, M., Redon, B. (2020) Building a Model to reconstruct the Hellenistic and Roman Road Networks of the Eastern desert of Egypt, a Semi-Empirical Approach Based on Modern Travelers’ Itineraries. DOI : <a href="http://doi.org/10.5334/jcaa.67">http://doi.org/10.5334/jcaa.67</a></p> <p>Datapaper:<br> Manière, L., Crépy, M., Redon, B. (2020) Geospatial data from the “Modelling the Hellenistic and Roman Road Networks of the Eastern desert of Egypt, a Semi-Empirical Approach Based on Modern Travelers’ Itineraries” paper. DOI : <a href="http://doi.org/10.5334/joad.71">http://doi.org/10.5334/joad.71</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.