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903 results for “domestic”
Biomass Domestic Material Consumption by Country and over Time
<p>Biomass domestic material consumption in thousand tons, and tons per capita for European countries.</p> <p>Our dataset has a 10.4% larger congruent dataset (to be used in various supervised or unsupervised learning models, such as machine learning) than the original Eurostat dataset after imputation, backcasting, forecasting. It has overall 18% more observations after processing than the dataset at source. </p>
Inter-Chemical Correlation results for the study: HHEARx2017-1982 (Domestic Indoor PM and Childhood Asthma Morbidity (DISCOVER Study))
Title: Domestic Indoor PM and Childhood Asthma Morbidity (DISCOVER Study) <br>Species: Homo sapiens <br>Number of samples: 732 <br>Number of named analytes: 26 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=35 <br>
Src and Memory: A Study of Filial Imprinting and Predispositions in the Domestic Chick
<p>These files contain the data used for generating the results presented in:</p> <p>Meparishvili M, Chitadze L, Lagani V, McCabe B and Solomonia R (2021) Src and Memory: A Study of Filial Imprinting and Predispositions in the Domestic Chick. Front. Physiol. 12:736999. doi: 10.3389/fphys.2021.736999 </p>
Gross domestic product at market prices
<p>Gross domestic product at market prices</p> <p>Dataset created from the Eurostat dataset [<a href="http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=tec00001&lang=en">tec00001</a>] using linear approximation, backcasting and forecasting missing values. The information gain is a 33% increased non-missing matrix for regression or machine learning models.<br> </p>
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments
<p>The problem of training a deep neural network with a small set of positive samples is known as few-shot learning (FSL). It is widely known that traditional deep learning (DL) algorithms usually show very good performance when trained with large datasets. However, in many applications, it is not possible to obtain such a high number of samples. In the image domain, typical FSL applications are those related to face recognition. In the audio domain, music fraud or speaker recognition can be clearly benefited from FSL methods. This paper deals with the application of FSL to the detection of specific and intentional acoustic events given by different types of sound alarms, such as door bells or fire alarms, using a limited number of samples. These sounds typically occur in domestic environments where many events corresponding to a wide variety of sound classes take place. Therefore, the detection of such alarms in a practical scenario can be considered an open-set recognition (OSR) problem. To address the lack of a dedicated public dataset for audio FSL, researchers usually make modifications on other available datasets. This paper is aimed at providing the audio recognition community with a carefully annotated dataset for FSL and OSR comprised of 1360 clips from 34 classes divided into pattern sounds and unwanted sounds. To facilitate and promote research in this area, results with two baseline systems (one trained from scratch and another based on transfer learning), are presented.</p> <p> </p>
Data and R Code for 'Domestication via the commensal pathway in a fish-invertebrate mutualism'
<p>This document contains all data and R code required to replicate the analyses in 'Domestication via the commensal pathway in a fish-invertebrate mechanism' as published in Nature Communications. The R Markdown provided includes descriptions of all variables and the code used for the analysis of the following eight datasets:</p> <p>1. Transects<br> 2. Census of farms<br> 3. Paired choice experiments<br> 4. Predation experiment 1<br> 5. Predation experiment 2<br> 6. Timed observations<br> 7. Farm algae composition<br> 8. Longfin damselfish body condition</p> <p>In addition, the R Markdown also includes descriptions of all variables for two additional datasets:</p> <p>9. Estimates of mysid swarm density<br> 10. Mysid waste excretion and nutrient availability</p> <p>A PDF version of the R Markdown with all output is also provided. Please see the methods section of the associated manuscript for further information on data collection and analysis procedures.</p> <p>Author contributions to data collection and analysis: RMB, JMC, ZLC, TLS & WEF collected the data; WEF, RMB, JMC, ZLC & AM implemented the analyses. </p> <p>Correspond with: rohan.m.brooker@gmail.com</p>
Great Britain's primary substation service areas and annual domestic energy statistics
<p>This geospatial data is a combination of Great Britain's 4436 primary substation service areas which have been parsed into a single shapefile for energy systems analysis. The original component datasets were provided by the six distribution network operator (DNO) companies in Great Britain (National Grid Electricity Distribution, Electricity North West Ltd, Scottish and Southern Electricity Networks, UK Power Networks, Scottish Power Energy Networks and Northern Power Grid). Attribution is given to the original data owners at each of these six DNOs and the resulting dataset from this work has been created and published under an open licence with each DNO's permission. </p><p>The data is available to download as two geojson files in the WGS84 coordinate system. One is a streamlined version which just contains the polygons along with a unique primary identifier (UPID), primary substation name, DNO licence area and local authority. The other contains the polygons along with richer energy data which was aggregated to the primary substation level from publicly available Department for Energy Security and Net Zero, Office for National Statistics and National Grid ESO datasets. This data is also available to download in tabular form as a csv file. The meter numbers and consumption values are the means of those reported from 2015-2020. The substation polygons were those as received or publicly available as of the time period of this study (2021-22).</p><p>The pre-print manuscript of the methodology used to create this dataset can be found on arXiv at:</p><p>https://doi.org/10.48550/arXiv.2311.03324</p><p>Funding to support this work was received from the Engineering and Physical Sciences Research Council (EP/W008726/1) under the Gas Net New project and the Alan Turing Institute's Science of Cities and Regions Programme. Thanks are also given to the contributors of QGIS and the Geopandas Python library, both of which were used in this analysis. </p>
French domestic bilateral transport costs between districts c. 1789
<p>Downloading the data will provide you with a .zip file.</p> <p>The data include bilateral transport costs between French districts around 1789, as computed for the paper See Daudin, G. (2010). Domestic Trade and Market Size in Late-Eighteenth-Century France. <em>The Journal of Economic History,</em> 70(3), 716-743. doi:10.1017/S0022050710000598</p>
Labeled songs of domestic canary M1-2016-spring (Serinus canaria)
<p><strong>Labeled songs of domestic canary M1-2016-spring (Serinus canaria)</strong></p> <p><em>J. Giraudon*<sup>123</sup>, N. Trouvain*<sup>123</sup>, A. Cazala<sup>4</sup>, C. Del Negro<sup>4</sup>, X. Hinaut<sup>123</sup></em></p> <p><sup>1</sup> Inria Bordeaux Sud-Ouest, France</p> <p><sup>2</sup> LaBRI, Bordeaux INP, CNRS, UMR 5800, France</p> <p><sup>3</sup> Institut des Maladies Neurogégénératives, Université de Bordeaux, CNRS, UMR 5293, France</p> <p><sup>4 </sup>Paris-Saclay University, UMR 9197 CNRS, Paris-Saclay Institute of Neuroscience, France </p> <p><em>* these authors participated equally to this work.</em></p> <p><strong>General information</strong></p> <p>This dataset contains ~3h of labeled songs (459 songs) of one male canary (called M1) recorded between May 24th and June 15th 2016. Songs were recorded in a sound-isolation chamber using a RODE M3 microphone, an external sound card for microphone amplification (M-Audio Fast Track Ultra 8R), and the software Sound Analysis Pro 2011 (SAP). SAP parameters were set with conservative thresholds (software threshold to 4-6) in order to record the initiation of canary's songs which can be low in volume.</p> <p>Songs were hand labelled by one human expert using Audacity. They were then checked and corrected by another human expert assisted by an automated program based on recurrent neural networks (see References).</p> <p><strong>Dataset description</strong></p> <p>Canary songs are labeled using 27 different identified syllable classes + 1 "call" class identifying simple off-song calls + 1 "TRASH" class for irrelevant sounds (very rare vocalizations or non-bird sounds) + 1 "SIL" class for silence between vocalizations. Songs are annotated at the phrase level: a phrase consists of a repetition of a single syllable type and each phrase type is assigned a label.</p> <p>Annotations are provided in CSV format in the "M1-2016-spring_csv_annotations.zip" archive. There is one file per song, containing:</p> <ul> <li>a "wave" column indicating the song's audio filename;</li> <li>"start" and "end" columns indicating the temporal delimitation of the label from the begining of the song, in seconds;</li> <li>a "syll" column indicating the labels.</li> </ul> <p>Annotations are also provided in <a href="https://manual.audacityteam.org/man/importing_and_exporting_labels.html">Audacity TXT format</a> in the "M1-2016-spring_audacity_annotations.zip" archive. There is one file per song, containing three tabulation-separated columns. The first two column indicates the temporal delimitation (start and end) of the phrase from the begining of the song. The thrid one contains the associated label. Annotations filenames match corresponding song audio filename.</p> <p>Songs are provided in WAV format (44kHz sampling rate) in the "M1-2016-spring_audio.zip" archive. There is one file per song: audio filenames match corresponding annotation filenames.</p> <p><strong>References</strong></p> <p>This dataset was used in:</p> <p>N. Trouvain, X. Hinaut (2021) Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs. HAL preprint <a href="https://hal.inria.fr/hal-03203374">⟨hal-03203374⟩</a></p>
Great Britain (GB) Domestic Electricity Usage by Low Carbon Technology by Season
<p><strong>Important</strong>: As an research not-for-profit organisation, if you found this dataset useful we would appreciate your time in filling out <a href="https://docs.google.com/forms/d/e/1FAIpQLSfqCAoQt4AzuGH8Th5tJjnkGP956Fgc6O8T6wJaM7Nhd_nRdg/viewform?usp=pp_url&entry.1276408097=10.5281/zenodo.6576108">this short survey</a>.</p> <p> </p> <p>This dataset contains 3 aggregate datasets from the electricity smart meter data of over 25,000 customers in Great Britain (GB) from March 2021 - March 2022.</p> <p>For each consumer, we know (via a survey) what low carbon technologies (LCTs) they own. The potential LCT options are: Solar PV, Heat Pump (Air Source, or Ground Source), Electric Vehicle, Battery, Electric Storage Heaters.</p> <p>For simplicity, this dataset contains only customers with one type of LCT (with the exception of Solar PV, where we include Solar PV + Battery customers as is common in GB). We do not include customers with multiple LCTs (for example home battery + EV)</p> <p>We include quantiles of usage for each half hour (the "profile") for each type of LCT ownership "archetype", both overall (when season=None) and by season. As is common in the literature, we normalise by the square meterage of the house using open EPC data in GB (https://epc.opendatacommunities.org/) to get the watt hours per square meter. You can also find the raw, unnormalised, kwh values by quantile in this release. These two datasets have the quantiles for each half hour period. In addition, we release the daily quantiles of electricity consumption, in kwh per square meterage, by LCT type.</p> <p>In summary the data we are releasing, aggregated over 25,000 customers over 1 year of usage from March 2021 - March 2020 is:</p> <ul> <li>daily_elec_consumption_quantiles_by_lct_ownership.csv - The daily quantiles of usage [kWh/m2] by LCT</li> <li>lct_elec_consumption_profiles.csv - The half hourly quantiles of usage [Wh/m2] by LCT by season</li> <li>lct_elec_consumption_profiles_kwh.csv - The half hourly quantiles of usage [kWh] by LCT by season</li> </ul> <p>We believe this data will be useful for modelling efforts, as customers with different types of LCTs use energy at different times of the day, and by different amounts daily. By releasing this data openly, we hope forecasting scenarios for the future energy system are more accurate. We have a supporting blog post on our website at https://www.centrefornetzero.org/res/lessons-from-early-adopters-electricity-consumption-profiles/.</p>
Cellular and Humoral Immune Responses after Immunisation with Low Virulent African Swine Fever Virus in the Large White Inbred Babraham Line and Outbred Domestic Pigs
<p>Raw data for manuscript. Contains temperature, clinical scores, qPCR, blood cell numbers and immune responses over time for two groups of pigs immunised with low virulent African swine fever virus and challenged with highly virulent virus. Data for each panel or figure is displayed on a separate worksheet in the file. The readme worksheet contains a brief description of each figure. The majority of data is displayed in an XY table format, with the number of days post immunisation with low virulent virus indicated.</p>
MALDI-TOF-MS reference spectra and sequence data for domesticated equids (horse and donkey) collagen for Zooarchaeology by Mass Spectrometry (ZooMS)
<p>MALDI-TOF-MS spectra of extracted collagen from modern reference and archaeological bone samples to develop markers for Zooarchaeology by Mass Spectrometry (ZooMS) to distinguish between Equus species. For each sample digestions were done in both trypsin and chymotrypsin separately. Information about the species of the samples can be found in 'sample metadata.csv' file. Information on the extraction and digestion protocol can be found in the associated manuscript. The sequence data contains alignments of the proteins COL1A1 and COL1A2 for available Equus collagen protein sequences. More information on these files can be found in the corresponding manuscript to this dataset.<br> </p>
Turkiye, Regional Domestic Input Output Tables, 2022, 26 Region, 62 X 62 Matris
<p>Regional data is crucial for conducting inter-regional economic analyses, understanding productivity, wages, and minimum wage dynamics, assessing the impact of unforeseen events like natural disasters, comparing sectors, and evaluating development levels. Supply-use tables, employment, and demographic statistics are the most commonly used data sources for these analyses.</p> <p>To address these needs and for my own economic analyses, I have created regional supply-use tables at the province level. Additionally, I have generated domestic input-output tables using the NUTS 2 regional classification for conducting regional and inter-regional analyses in Turkiye.</p> <h2>Scope of the Research</h2> <p>This research presents domestic input-output tables for 62 industries in 26 NUTS 2 regions of Turkiye for the year 2022.</p> <h2>Methodology</h2> <p>Regional accounts are published at the A10 level for every province in Turkey. In the Input-Output tables, which consist of 62 sectors, the input-output distribution has been conducted on a sector basis to derive the total at the A10 level. The estimations at the provincial level are automatically derived from regional accounts data.</p> <p>The NUTS 2 level regional input-output data have been published and disseminated with an A62 matrix. Additionally, province-level input-output data have been published and disseminated with an A62 matrix through the application. After summations the regional input output tables have been estimated.</p> <h2><strong>Application</strong></h2> <p>The link to the relevant application is attached:</p> <h3><a href="https://turkiye.streamlit.app/" target="_blank" rel="noopener ugc nofollow">https://turkiye.streamlit.app/</a></h3> <p> </p>
The impact of domestic combustion of biomass pellets on the environment and human health: Example from Poland
<p><strong>Submitted data was used to write an article: </strong>Drobniak, A., Jelonek, Z., Mastalerz, M., Jelonek, I., Widziewicz-Rzońca, K., The impact of domestic combustion of biomass pellets on the environment and human health: Example from Poland – in preparation.</p> <p> </p> <p><strong>Funding acknowledgments: </strong>The project is co-financed by the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), the National Science Center, Poland (2022/01/1/ST10/00024), and the research activities co-financed by the funds granted under the Research Excellence Initiative of the University of Silesia in Katowice, Poland. </p> <p> </p> <p><strong>Article Abstract:<br></strong></p> <p><span>In the context of the European Union's intensified efforts to curb greenhouse gas emissions and meet climate targets, wood pellets have emerged as a pivotal element in the renewable energy strategy. Yet, biomass pellet combustion has been linked to a range of pollutants impacting air quality and public health. As biomass utilization gains popularity as a fuel for residential heating, it is important to determine this impact and enhance sustainable practices throughout the entire biomass energy production cycle. </span></p> <p><span>This study investigates the intricate dynamics of biomass pellet properties on their combustion emissions, with a specific focus on the differences observed between pellets of woody and non-woody origins. The data reveal a variation in pellet characteristics, especially regarding their ash and fines contents, mechanical durability, and impurity levels, and significant differences in the type and amount of utilization emissions. The results highlight potential health risks posed by the combustion of biomass fuels, particularly non-woody (agro) pellets, due to elevated concentrations of emitted particulate matter (PM), carbon monoxide (CO), nitrogen dioxide (NO<sub>2</sub>), hydrogen sulfide (H<sub>2</sub>S), ammonia (NH<sub>3</sub>), chlorine (Cl<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), and formaldehyde (HCHO), all surpassing recommended limits.</span></p> <p><span>Moreover, the study reveals that emissions from pellet combustion could be partially predicted by analyzing pellet characteristics. Statistical analysis identified several key variables—including bark content, fines content, mechanical durability, bulk density, heating value, net calorific value, sulfur, and nitrogen content—that impact emissions of CO, NO<sub>2</sub>, H<sub>2</sub>S, SO<sub>2</sub>, HCHO, and respiratory tract irritants. These findings underscore the need for proactive measures, including the implementation of stricter standards for fuel quality and emissions, alongside public education initiatives promoting the cleanest and safest fuels possible. </span></p> <p><strong> </strong></p>
Dataset for: Owner-ascribed personality profiles distinguish domestic cats that capture and bring home wild animal prey
<p>Dataset allowing repetition of the analyses in the above paper, comprising personality scores and predation data, with details of cat characteristics. See readme.txt file.</p>
LOCATION DOMESTIC ARCHITECTURE AUGUSTA EMERITA (MERIDA, SPAIN)
<p>This dataset is based on houses from the Roman colony of <em>Augusta Emerita </em>(ca. B.C 25- A.D. 382)<sup> <a href="#_ftn1"><sup>[1]</sup></a></sup>. These dates are not chosen randomly: 25 B.C. saw the foundation <em>ex nihilo </em>of the settlement, according to Cass. Dio., <em>Hist</em>. 53.26.1<a href="#_ftn2"><sup><sup>[2]</sup></sup></a>, and A.D. 382, the last time a name of a <em>vicarius</em> is attested in the epigraphy<a href="#_ftn3"><sup><sup>[3]</sup></sup></a>. The remains from <em>Augusta Emerita </em>are particularity well suited to GIS analysis of this type because the site was occupied for more than two thousand years and was never abandoned, while also being occupied by several cultures over that time.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> All documentation based on La arquitectura doméstica de Augusta Emerita (2015 Phd) https://dehesa.unex.es/handle/10662/2670# </p> <p><a href="#_ftnref2">[2]</a> The Duoviri's first couple is evidenced ca.20 B.C. from the fragment of the Fasti duovirales: A. Stylow and A. Ventura, “Los hallazgos epigráficos”, in R. Ayerbe, T. Barrientos and F. Palma (edd.), <em>El foro de Augusta Emerita. </em><em>Génesis y evolución de sus recintos monumentales</em> (Mérida 2009) 453-523.</p> <p><a href="#_ftnref3">[3]</a> L. Hidalgo and G. Méndez, “Octavius Clarus, un nuevo Vicarius Hispaniarum en Augusta Emerita”, <em>Mérida. Excavaciones Arqueológicas </em>8 (2005) 547-64.</p>
Genomic insight into domestication of rubber tree
<p>we upgraded H. brasiliensis genome assembly (contig N50 of 11.21 megabases), presented a map of genome variations by resequencing 335 accessions and revealed domestication-related molecular signals and a major domestication trait, the higher number of laticifer rings. HbPSK5, encoding the small-peptide hormone phytosulfokine (PSK), was a key domestication gene. It was closely associated with the major domestication trait by positively controlling laticifer differentiation from vascular cambia. The transcriptional activation of HbPSK5 by MYC members linked JA signaling to PSK signaling in enhancing laticifer formation.</p> <p>cite:https://www.nature.com/articles/s41467-023-40304-y</p>
FIG. 3 in A model for llama (Lama glama Linnaeus, 1758) domestication in the southern Andes
FIG. 3. — Representation of regional archaofaunas (% NISP), and time-trends for Camelidae, and Chinchillidae. Camelids increase over time, meanwhile the other animal resources diminishes.
FIG. 9. — Size comparison within 2800-1700 in Horse size and domestication: Early equid bones from the Czech Republic in the European context
FIG. 9. — Size comparison within 2800-1700 BC. Statistics for Vlíněves and other Únětice c. Czech sites are in Table 3. Statistics for Wattendorf-Motzenstein based on Becker (2008); for Rennweg and Csokor-gasse on Czeika (2010); for Csepel-Háros on Benecke & Driesch (2003); for Békés-Városerdö on Bökönyi (1974); for Föllik on Amschler (1949); re-dated to Litzenkeramik in Benkovsky-Pivovarová et al. (1987). Others as in Figs 3, 6. Abbreviations: CR, Czech Republic; Vl,, Vlíněves site.
FIG. 1 in Horse size and domestication: Early equid bones from the Czech Republic in the European context
FIG. 1. — Map with Czech archaeological sites dated from the beginning of the Neolithic to EBA (Únětice c.) which provided equid bones. A, site locations, sorted alphabetically (chronological order in Table 2); B, position of the Czech Republic in Europe (Bohemia, dark area; Moravia, light area; grey lines, state borders); C, geomorphology of central Europe (http://open-data.europa.eu). Numbers: 1, Baba (district Prague); 2, Blučina-Cezavy; 3, Březno; 4, Bylany; 5, Čelákovice; 6, Černý Vůl; 7, Chotěbudice; 8, Cimburk, 9, Ďáblice (distr. Prague); 10, Dobroměřice; 11, Dřevčice; 12, Homolka; 13, Hostěnice-Brozany; 14, Hostivice; 15, Jezeřany-Maršovice; 16, Kobeřice; 17, Kšely, 18, Kutná Hora-Denemark; 19, Litovice; 20, Makotřasy; 21, Mikulov-Jelení louka; 22, Moravská Nová Ves; 23, Roztoky; 24, Starý Lískovec (distr. Brno); 25, Stránská skála (distr. Brno); 26, Těšetice-Kyjovice; 27, Toušeň-Hradišťko; 28, Tuchoměřice; 29, Úholičky; 30, Ve- drovice; 31, Velké Přílepy; 32, Vlíněves; 33, Vyškov; 34, Žádovice.
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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.