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1,902 results for “consumption”
FIG. 4. — Fishing with net from a in Food worthy of kings and saints: fish consumption in the medieval monastery Studenica (Serbia)
FIG. 4. — Fishing with net from a boat, detail of the 13th century fresco from Mileševa Monastery (courtesy of the BLAGO Fund).
FIG. 3 in Food worthy of kings and saints: fish consumption in the medieval monastery Studenica (Serbia)
FIG. 3. — Representation of various fish species, detail of the 14th century fresco from Gračanica Monastery (courtesy of the BLAGO Fund).
Repository: Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade-linked multilevel life cycle assessment
<p>This repository contains the input data, codes and results of the model developed in the paper "Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade-linked multilevel life cycle assessment" published in the Journal of Industrial Ecology (2020) by Alexandre Milovanoff, I. Daniel Posen, Heather L. MacLean.</p>
VR360 Consumption DataSet - ImAc Open Pilot
<p>This dataset has been created under the framework of the EU H2020 ImAc project<br> (<a href="https://www.imac-project.eu/">https://www.imac-project.eu/</a>). It contains traces from Quality of Service (QoS) and Quality of Experience (QoE) related</p> <p>metrics when watching 360º videos augmented with access services (mainly subtitles, and sign language),<br> by using different branches of the ImAc player (<a href="http://imac.i2cat.net/player/">http://imac.i2cat.net/player/</a>) in an open environment.</p> <p>The dataset is comprised of two folders related to two pilots launched in the ImAc project, led by a German and a Spanish broadcaster. In total, it contains traces from more than 800 video watching sessions.</p> <p>[More details in the README file.]</p> <p>CONTACT INFO: <a href="mailto:media.team@i2cat.net">media.team@i2cat.net</a></p> <p> </p>
Data from: Climate drives the geography of marine consumption by changing predator communities
<p>The global distribution of primary production and consumption by humans (fisheries) is well-documented, but we have no map linking the central ecological process of consumption within food webs to temperature and other ecological drivers. Using standardized assays that span 105° of latitude on four continents, we show that rates of bait consumption by generalist predators in shallow marine ecosystems are tightly linked to both temperature and the composition of consumer assemblages. Unexpectedly, rates of consumption peaked at midlatitudes (25 to 35°) in both Northern and Southern Hemispheres across both seagrass and unvegetated sediment habitats. This pattern contrasts with terrestrial systems, where biotic interactions reportedly weaken away from the equator, but it parallels an emerging pattern of a subtropical peak in marine biodiversity. The higher consumption at midlatitudes was closely related to the type of consumers present, which explained rates of consumption better than consumer density, biomass, species diversity, or habitat. Indeed, the apparent effect of temperature on consumption was mostly driven by temperature-associated turnover in consumer community composition. Our findings reinforce the key influence of climate warming on altered species composition and highlight its implications for the functioning of Earth's ecosystems.</p>
Accounting for New Types of Resource Consumption in a Federated Cloud
<p>As infrastructures and cloud services evolve, resource consumption is more flexible and users are often allowed to reserve resources without actual consumption. Relevant standardization bodies have developed new types of accounting record specifications, and new or updated tools are required to keep track of resource usage. The reaction to this is the development of a new accounting tool – GOAT.</p> <p>GOAT – GO Accounting Tool – is a service running in the background and waiting for a connection from a compatible client. The client connects to a cloud management framework, extracts computing data about projects, servers, networks, and storages, filters them accordingly, and sends them to a server for further processing. Multiple clients can use the server at once. When the server receives accounting data, it is transformed into the configured format and writes them to the destination file. The consumer collects data into a central accounting database where it is processed to generate statistical summaries.<br> For now, the GOAT project supports two cloud computing platforms on the client side – OpenNebula and Openstack. Thanks to its use of standard accounting record formats it can work with different consumers. The ones used in the real world are APEL and Prometheus.</p> <p>This Demonstration shows how the GOAT client extracts accounting data from a cloud management platform, sends them to the GOAT server where they are transformed, and how Prometheus and Grafana process them and present various views of resource usage.</p>
Supporting data and code for "Brown AW, Bohan Brown MM, Onken KL, Beitz DC. Short-term consumption of sucralose, a nonnutritive sweetener, is similar to water with regard to select markers of hunger signaling and short-term glucose homeostasis in women. Nutr Res. 2011 Dec;31(12):882-8. doi: 10.1016/j.nutres.2011.10.004. PMID: 22153513."
<p>Data and code to support the publication, Brown AW, Bohan Brown MM, Onken KL, Beitz DC. Short-term consumption of sucralose, a nonnutritive sweetener, is similar to water with regard to select markers of hunger signaling and short-term glucose homeostasis in women. Nutr Res. 2011 Dec;31(12):882-8. doi: 10.1016/j.nutres.2011.10.004. PMID: 22153513.</p> <ul> <li>Code was updated 2020 NOV 24 to add comments, but otherwise remains unchanged from 2011.</li> <li>Data file was updated to include a data dictionary, but otherwise remains unchanged from 2011.</li> </ul> <p>Treatment identifiers for the four-arm crossover are clarified in the SAS code.</p> <p>Other details are available in the published article.</p>
FIG. 6 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 6. — Triangle plot presenting variations in the frequency of the three main species between the different archaeological features on the site of Villeneuve-Saint-Germain.
FIG. 7 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 7. — Plan of the southern ditch sector on the site of Villeneuve-Saint-Germain: A, general plan of the site of Villeneuve-Saint-Germain (from Pion 1996); B, southern ditch sector on the site of Villeneuve-Saint-Germain (from Ruby & Auxiette 2010). Scale bar: 10 m.
FIG. 2 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 2. — Development of herd composition during the Iron Age. BCP, Berry-au-Bac " le Chemin de la Pêcherie "; BGM, Bucy-le-Long " le Grand Marais "; LLF, Limé " les Fussis "; MDV = Menneville " Derrière le Village "; BFM, Bucy-le-Long " le Fond du Petit Marais "; LLP, Limé " La Prairie "; BAR, Baranton; BET, Bétheny " les Equiernolles "; BVC, Bazoches-sur-Vesles " les Chantraînes "; CLB, Ciry-Salsogne " le Bruy "; MNV, Mont-Notre-Dame " Vaudigny "; DRF, Damary " le Ruisseauds Fayau "; CSS, Condé-sur-Suippe; RD, Reims-Durocortorum; AC, Acy-Romance; SFR, Sermoise " les Fausses Rues "; VSE, Villeneuve-Saint-Germain " les Etomelles "; VSG, Villeneuve-Saint-Germain.
FIG. 8 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 8. — Simplified chart of butchery cuts from bovinae according to observations carried out on the site of Villeneuve-Saint-Germain.
FIG. 2 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 2. — Development of herd composition during the Iron Age. BCP, Berry-au-Bac " le Chemin de la Pêcherie "; BGM, Bucy-le-Long " le Grand Marais "; LLF, Limé " les Fussis "; MDV = Menneville " Derrière le Village "; BFM, Bucy-le-Long " le Fond du Petit Marais "; LLP, Limé " La Prairie "; BAR, Baranton; BET, Bétheny " les Equiernolles "; BVC, Bazoches-sur-Vesles " les Chantraînes "; CLB, Ciry-Salsogne " le Bruy "; MNV, Mont-Notre-Dame " Vaudigny "; DRF, Damary " le Ruisseauds Fayau "; CSS, Condé-sur-Suippe; RD, Reims-Durocortorum; AC, Acy-Romance; SFR, Sermoise " les Fausses Rues "; VSE, Villeneuve-Saint-Germain " les Etomelles "; VSG, Villeneuve-Saint-Germain.
FIG. 7 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 7. — Plan of the southern ditch sector on the site of Villeneuve-Saint-Germain: A, general plan of the site of Villeneuve-Saint-Germain (from Pion 1996); B, southern ditch sector on the site of Villeneuve-Saint-Germain (from Ruby & Auxiette 2010). Scale bar: 10 m.
FIG. 5. — A in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 5. — A, Plan of one of the forge workshops on the site of Condé-sur-Suippe; B, General plan of the sites of Condé-sur-Suippe "la Sucrerie" (P. Pion 1987).
[Database] Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets
<p>This file contains the complete catalog of datasets and publications reviewed in: Di Mauro A., Cominola A., Castelletti A., Di Nardo A.. <em>Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets.</em> Water 2021.The <strong>complete catalog</strong> contains:</p> <ul> <li>92 state-of-the-art water demand datasets identified at the district, household, and end use scales;</li> <li>120 related peer-reviewed publications;</li> <li>57 additional datasets with electricity demand data at the end use and household scales.</li> </ul> <p>The following <strong>metadata</strong> are reported, for each <strong>dataset</strong>:</p> <ul> <li>Authors</li> <li>Year</li> <li>Location</li> <li>Dataset Size</li> <li>Time Series Length</li> <li>Time Sampling Resolution</li> <li>Access Policy.</li> </ul> <p>The following <strong>metadata </strong>are reported, for each <strong>publication</strong>:</p> <ul> <li>Authors</li> <li>Year</li> <li>Journal</li> <li>Title</li> <li>Spatial Scale</li> <li>Type of Study: Survey (S) / Dataset (D)</li> <li>Domain: Water (W)/Electricity (E)</li> <li>Time Sampling Resolution</li> <li>Access Policy</li> <li>Dataset Size</li> <li>Time Series Length</li> <li>Location</li> </ul> <p><strong>Authors:</strong><br> Anna Di Mauro - Department of Engineering | Università degli studi della Campania Luigi Vanvitelli (Italy) | <a href="mailto:anna.dimauro@unicampania.it">anna.dimauro@unicampania.it</a>;<br> Andrea Cominola - Chair of Smart Water Networks | Technische Universität Berlin - Einstein Center Digital Future (Germany) | <a href="mailto:andrea.cominola@tu-berlin.de">andrea.cominola@tu-berlin.de</a>; <br> Andrea Castelletti - Department of Electronics, Information and Bioengineering | Politecnico di Milano (Italy) | <a href="mailto:andrea.castelletti@polimi.it">andrea.castelletti@polimi.it</a><br> Armando Di Nardo -Department of Engineering | Università degli studi della Campania Luigi Vanvitelli (Italy) | <a href="mailto:armando.dinardo@unicampania.it">armando.dinardo@unicampania.it</a></p> <p><strong>Citation and reference:</strong></p> <p>If you use this database, please consider citing <a href="https://www.mdpi.com/2073-4441/13/1/36">our paper</a> </p> <p>Di Mauro, A., Cominola, A., Castelletti, A., & Di Nardo, A. (2021). Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets. Water, 13(1), 36, https://doi.org/10.3390/w13010036</p> <p><strong>Updates and Contributions:</strong></p> <p>The catalogue stored in this public repository can be collaboratively updated as more datasets become available. The authors will periodically update it to a new version. </p> <p>New requests can be submitted to the authors, so that the dataset collection can be improved by different contributors. Contributors will be cited, step by step, in the updated versions of the dataset catalogue.</p> <p><strong>Updates history:</strong></p> <ol> <li>March 1st, 2021 - Pacheco, C.J.B., Horsburgh, J.S., Tracy, J.R. (Utah State University, Logan, UT - USA) --- The dataset associated with paper <a href="https://doi.org/10.3390/s20133655">Bastidas Pacheco, C.J.; Horsburgh, J.S.; Tracy, R.J.. A Low-Cost, Open Source Monitoring System for Collecting High Temporal Resolution Water Use Data on Magnetically Driven Residential Water Meters. Sensors 2020, 20, 3655.</a> is published in the HydroShare repository, where it is available as an OPEN dataset. Data can be found here: <a href="https://doi.org/10.4211/hs.4de42db6485f47b290bd9e17b017bb51">https://doi.org/10.4211/hs.4de42db6485f47b290bd9e17b017bb51</a></li> </ol>
NATCONSUMERS - main factors and attitudes behind energy consumption
<p><strong>NATCONSUMERS</strong>’ key aim is to develop an economically and technologically feasible, advanced and complex user-centred framework to help decrease domestic energy consumption.</p> <p>Within this framework, the project has conducted surveys to find the most relevant drivers behind energy saving attitude.</p> <p>Four countries were chosen for the survey: the UK, Hungary, Italy and Denmark. In each of the four countries, a sample of 1,000 individuals aged 18-65 were surveyed. This sample size was deemed the most cost effective to provide a nationally representative sample. People living in shared accommodation – those in student campuses, residential care homes, sheltered housing or military barracks – were excluded from the sample as these people have limited or no control over energy use in their residence. Anyone working for the advertising or marketing industry or within the energy industry was also excluded from the sample as this could provide a conflict of interests which would bias their responses. Data collection happened in March and April 2016, conducted by Ipsos in the United Kingdom, Denmark and Italy, and by NRC in Hungary.</p> <p> </p> <p>The project D3.3 deliverable summarizes the main findings from the survey.</p> <p>http://natconsumers.eu/?wpdmdl=1582</p> <p> </p> <p>The project D4.2 deliverable presents how this data could be used in NATCONSUMERS counselling.</p> <p>http://natconsumers.eu/?wpdmdl=1586</p> <p> </p> <p>The project final deliverable D7.4 gives a summary how these data sources need to be processed and applied in a user-centred energy advice system.</p> <p>http://natconsumers.eu/?wpdmdl=1709</p> <p> </p> <p> </p>
Life cycle inventory database for consumption in Quebec - Personal hygiene
<p>These inventory datasets are essential for calculating the environmental impacts of an individual’s consumption in Quebec.</p> <p>Led by the CIRAIG, in collaboration with ESG-UQAM, this project aims to develop an inventory database of the life cycle of consumption in Quebec. These inventory datasets are essential for calculating the carbon footprint of an individual’s consumption in Quebec. The inventory is developed with a life cycle approach. Ultimately, it allows for evaluating carbon footprints at every step of the consumption life cycle (extraction of primary sources, transformation, transport, use of goods and services, end of life). The inventory is developed in a modular fashion for the different areas of individual consumption as Food; Transport; Housing; Clothing; Travel; Communications; Entertainment and Culture; Financial and Administrative Management; Health, Hygiene, and Beauty. These areas are developed and detailed as a priority, as they contribute most to an individual’s carbon footprint in Quebec. Other non-priority areas are roughly modelled in order to provide a complete (but more uncertain) portrait of individual consumption. The project is underway and the deliverables will be made available online as things progress. It is not, however, an objective of the project to create a carbon footprint calculation tool at the moment.</p> <p>https://ciraig.org/index.php/project/life-cycle-inventory-database-for-consumption-in-quebec/ </p>
Figure 1 in Influence of temperature and prey type on life-table parameters and consumption rate of Stethorus gilvifrons (Mulsant) (Coleoptera: Coccinellidae) on three tetranychid mites
Figure 1. Age-stage-specific survival rate (lx) and age-specific fecundity (mx) curves of Stethorus gilvifrons on different prey types and different temperatures.
Cyber-Physical System power Consumption
<h1>Files</h1> <p>This dataset is comprised of 5 CSV files contained in the data.zip archive. Each one represents a production machine from which various sensor data has been collected. The average cadence for collection was 5 measurements per second. The monitored devices where used for hydroforming.</p> <p>The collection period covered the period from 2023-06-01 until 2023-08-05.</p> <h2>Data</h2> <p>These files represent a complete data dump from the data available in the time-series database, InfluxDB, used for collection. Because of this some columns have no semantic value for detecting production cycles or any other analytics.</p> <p>Each file contains a total of 14 columns. Some of the columns are artefacts of the query used to extract the data from InfluxDB and can be discarded. These columns are: results, table _start, _stop</p> <ul> <li><em>results</em> - An artefact of the InfluxDB query, signifies postprocessing of results in this dataset. It is "mean".</li> <li><em>table</em> - An artefact of the InfluxDB query, can be discarded.</li> <li><em>_start</em> and <em>_stop</em> - Refers to ingestion related data, used in monitoring ingestion. </li> <li><em>_field</em> - An artefact of the InfluxDB query, specifying what field to use for the query.</li> <li><em>_measurement</em> - An artefact of the InfluxDB query, specifying what measurement to use for the query. Contains the same information as device_id.</li> <li><em>host</em> - An artefact of the InfluxDB query, the unique name of the host used for the InfluxDB sink in Kubernetes.</li> <li><em>kafka_topic</em> - Name of the Kafka topic used for collection.</li> </ul> <p> </p> <p>Pertinent columns are:</p> <ul> <li><strong><em>_time</em></strong> - Denotes the time at which a particular event has been measured, it is used as index when creating a dataframe.</li> <li><em><strong>_time.1</strong></em> - Duplicate of _time for sanity check and ease of analysis when _time is set as index</li> <li><em><strong>_value</strong></em> - Represents the value measured by each sensor type.</li> <li><em><strong>device_id </strong></em>- Unique identifier of the manufacturing device, should be the same as the file name, i.e. B827EB8D8E0C.</li> <li><em><strong>ingestion_time</strong></em> - Timestamp when the data has been collected and ingested by influxDB.</li> <li><em><strong>sid</strong></em> - Unique sensor ID; the power measurements can be found at sid 1.</li> </ul> <p> </p> <h1>Annotations</h1> <p>There are two additional files which contain annotation data: </p> <ul> <li><em><strong>scamp_devices.csv</strong></em> - Contains mapping information between the dataset device ID (defined in column "<em>DeviceIDMonitoring</em>") and the ground truth file ID (defined in column "<em>DeviceID</em>")</li> <li><em><strong>scamp_report_3m.csv </strong></em>- Contains the ground truth, which can be used for validation of cycle detection and analysis methods. The columns are as follows: <ul> <li><strong><em>ReportID</em></strong> - Internal unique ID created during data collection. It can be discarded.</li> <li><em><strong>JobID</strong></em> - Internal Scheduling Job unique ID.</li> <li><em><strong>DeviceID</strong></em> - The unique ID of the devices used for manufacturing needs to be mapped using the <em>scamp_device.csv</em> data.</li> <li><em><strong>StartTime</strong></em> - Start time of operations</li> <li><em><strong>EndTime</strong></em> - End time of operations</li> <li><em><strong>ProductID</strong></em> - Unique identifier of the product being manufactured.</li> <li><em><strong>CycleTime</strong></em> - Average length of cycle in seconds, added manually by operators. It can be unreliable.</li> <li><em><strong>QuantityProduced</strong></em> - Number of products manufactured during the timeframe given by <em>StartTime</em> and <em>EndTime</em>.</li> <li><em><strong>QuantityScrap</strong></em> - Number of scraped/malformed products in the given timeframe. These are part of the <em>QuantityProduced</em><em>,</em><strong> </strong>not in addition to it.</li> <li><em><strong>IntreruptionMinuted</strong></em> - Minutes of production halt.</li> </ul> </li> <li><em><strong>scamp_patterns.csv</strong></em> - Contains the start and end timestamp for selected example production cycles. These where chosen based on expert users.</li> </ul> <h1>Jupyter Notebook</h1> <p>We have provided a sample Jupyter notebook (<em>verify_data.ipynb</em>), which gives examples of how the dataset can be loaded and visualised as well as examples of how the sample patterns and ground truth can be addressed and visualised.</p> <h2>Note</h2> <p>The Jupyter Notebook contains an example of how the data can be loaded and visualised. Please note that both data should be filtered based on sid; the power measurements are collected by sid 1. See Notebook for example.</p>
Upcycling food ingredients from orange by-products by hot air-microwave drying. Impact on energy consumption.
<p>Currently industrial citrus by-products represent a relevant environmental issue. The main aim of this work was the chemical characterization of the different bioactive compounds obtained after hot air-microwave drying (HAD+MW) of orange by-products, and their further conversion into three <strong>upcycled </strong>ingredients with health-related benefits: aqueous extract, ethanolic extract and <strong>dietary fibre</strong>. Total phenolics, antioxidant capacity, individual phenolic acids, flavonoids, limonin and carotenoids were monitored during blanching and colour extraction steps by analysing fresh by-products and process co-products: an aqueous extract rich in polyphenols and an ethanolic extract rich in carotenoids. After drying, the resulting fibre was characterized in terms of chemical composition, soluble and insoluble dietary fibre content and particle size. Technological properties and colour were compared to those of commercial citrus fibre. Energy and time consumption were compared with conventional hot air drying (HAD). Most polyphenols (50-65 %) and limonin (70 %) were extracted during the blanching step. 86 % of carotenoids were removed by soaking in ethanol. The orange fibre obtained had 71.9 g DF/ 100 g and antioxidant properties (205 mg TE/ Kg<sub>dm</sub>). Whiteness, water retention capacity and oil retention capacity were similar to commercial citrus fibre. HAD+MW reduced drying time and energy consumption by up to 50 % compared to HAD.</p>
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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.