Find research datasets worth reusing

FULFILL dataset - housing policy acceptability - framing experiment Latvia

This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in Latvia in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level. The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.

FULFILL dataset - housing policy acceptability - framing experiment Italy

This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in Italy in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level. The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.

FULFILL dataset - housing policy acceptability - framing experiment France

This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in France in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level. The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.

FULFILL dataset - housing policy acceptability - framing experiment Germany

This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in Germany in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level. The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.

FULFILL dataset - housing policy acceptability - framing experiment Denmark

This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in Denmark in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level. The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.

opencc-by-4.0Nov 2020View details →

Greek Smart House Nanogrid Dataset

The dataset refers to measurements and data collected from and for the CERTH Smart House nanogrid infrastructure in Thessaloniki, Greece. This infrastructure is a living lab that belongs to the Centre for Research and Technology-Hellas (www.certh.gr) and has been designed, deployed and operated by the Information Technology Institute (www.iti.gr). Mainly energy-related aspects are included in the datasets uploaded which are in the form of .csv files, covering Electricity Energy Consumption, Generation, and Storage, as well as Weather and Electricity Price information from external APIs (a local weather station has just been installed and will be included in future versions).

opencc-by-4.0Mar 2024View details →

House complex. Now hotel restaurant "El Buffi". 1929. Modernism.

opencc-by-4.0May 2022View details →

Data from Test House in Porto

Project: Hybrid-BioVGE The Hybrid – BioVGE project is proposed with the primary objective to develop, design and demonstrate a highly integrated solar/biomass hybrid air conditioning system for space cooling and heating of residential and commercial buildings that is affordable, operating with improved efficiency and with a strong market potential. Project details at https://hybrid-biovge.inegi.up.pt/index.asp File 01: Rawdata from PortoTestHouse: Hybrid-BioVGE_PortoTestHouse_RawData_WT7_INEGI_v1_31052022 File 02: Variable information and meta data

opencc-by-4.0Apr 2024View details →

Exploring Housing Affordability in Illinois: An In-Depth Study of the State's Real Estate Market

“Exploring Housing Affordability in Illinois: An In-Depth Study of the State’s Real Estate Market” focuses on the Illinois housing market from 2013 to 2022, mainly targeting housing affordability. Housing has been a cornerstone of stability in anyone’s life throughout history. Yet today, housing affordability has emerged as a critical societal issue impacting numerous individuals and families statewide. This study aims to get an overview of the trends of Illinois housing affordability over time across different counties in Illinois. It involves a comprehensive analysis of median home value and median incomes across Illinois counties, using data from two authoritative sources: the Census Bureau and Zillow. By providing insights, we can analyze and study the hidden factors that influence housing affordability over time and forecast future trends.

opencc-by-4.0Jul 2024View details →

Compilation of Digital Tools on Food Green House Gas Mitigation (CHOICE Project)

A compilation of digital tools to support behaviour change and action to food mitigation measures. The compilation was created for the CHOICE Horizon Europe project (Grant Agreement -101081617).

opencc-by-4.0Oct 2024View details →

Share and spatial concentration of social housing in Dutch urban areas

This dataset contains the amount of social housing units of the Netherlands per urban area, as well as the intensity of their spatial autocorrelation and its proportion compared to the total housing stock, for the year 2023. <a href="https://www.cbs.nl/nl-nl/dossier/nederland-regionaal/geografische-data/kaart-van-100-meter-bij-100-meter-met-statistieken">Original data</a> comes from Statistics Netherlands (Centraal Bureau voor de Statistiek) released for 100 m x 100 m grid cells covering a large share of the Dutch territory. Grid cells with missing values were excluded from the analysis. The spatial autocorrelation of social housing was calculated with urban area-level and U-style computations of Global Moran's I based on the share of social housing units compared to the total housing stock of every grid cell. Limits and definition of urban areas are extracted from <a href="https://www.oecd.org/en/data/datasets/oecd-definition-of-cities-and-functional-urban-areas.html">the OECD</a>. Data show considerable variation in the levels of social housing and its spatial concentration among Dutch urban areas.

opencc-by-4.0Nov 2022View details →

Schematic 3D reconstruction hypothesis of the house of the painter Gillis van Coninxloo at the Oude Turfmarkt and adjacent houses

This is a schematic, grey scale 3D reconstruction of the vanished house of the painter Gillis van Coninxloo and adjacent houses resulting from the research conducted in the framework of the Virtual Interiors project. The research questions that this 3D reconstruction aimed to explore relate to the identification of the exact location of the house on the Oude Turfmarkt and its internal spatial arrangement. Especially the references that are contained in Coninxloo's probate inventory to a ‘Coninxloos winckel’ and an ‘achter winckel’ on the first floor of his house were investigated with the 3D model.  An introduction to the Coninxloo case study and to the first phase of the 3D reconstruction project of his house is briefly presented in C. Piccoli and W. Li 2021. ‘Dealing with multidimensional uncertainty: The house of the painter Gillis van Coninxloo’, https://www.virtualinteriorsproject.nl/2021/08/19/dealing-with-multidimensional-uncertainty-the-house-of-the-painter-gillis-van-coninxloo/ (last accessed November 2022). An update on archival research and new insights on this and the neighbouring houses is given in C. Piccoli 2022. ‘The house of Gillis van Coninxloo at the Oude Turfmarkt: New insights’ https://www.virtualinteriorsproject.nl/2022/11/23/the-house-of-gillis-van-coninxloo-at-the-oude-turfmarkt-new-insights/ (last accessed November 2022). The sources that were used to propose this reconstruction hypothesis are listed in the *.csv file. Note: This 3D reconstruction is a provisional version and must be considered hypothetical. Aspects that could be clarified by further research include a possible difference in ground floor’s level between the front and the back in Coninxloo’s house, which would impact the spatial arrangement of the interior and require the presence of steps to bridge the two parts. Historical and archival research: Chiara Piccoli, Bart Reuvekamp, Frans Grijzenhout. 3D modelling: Chiara Piccoli 3D modelling software: Blender Acknowledgements: Virtual Interiors project, Gabri van Tussenbroek, Weixuan Li, Judith Brouwer, Madelon Simons.

opencc-by-4.0Jun 2022View details →

Der königlich sächsische Hausorden der Rautenkrone. Genese, Verfasstheit und Verleihungspraxis eines Hausordens des 19. Jahrhunderts (The Royal Saxon House Order of the Rue Crown. Origin, constitution and award practice of a house order of the 19th century.)

This data set was produced as part of a <a href="https://www.academia.edu/86314498/Der_königlich_sächsische_Hausorden_der_Rautenkrone_Genese_Verfasstheit_und_Verleihungspraxis_eines_Hausordens_des_19_Jahrhunderts">bachelor's thesis on the Royal Saxon House Order of the Rue Crown</a> (Orden der Rautenkrone) at the University of Greifswald. The thesis examines the award practices of the Grand Masters of the Order and attempts to draw conclusions about social circumstances.  For the work, a data set was created that includes all knights of the Order of the Rue Crown in the period from 1807 to 1918. The names of the beloved were expanded to include a standardised name (GND) and their life data, GND/Wikidata identifier and main geographical affiliation as well as rank and profession.  The data here are provided as Numbers and Excel files. Furthermore, the individual tables have been exported into CSV format (Note: in Excel, the CSV files may be displayed incorrectly despite UTF-8 encoding - especially with special characters and umlauts) The dates are not yet completely accurate. For example, in the case of the standardised names, since the persons concerned may have received the corresponding status (king, etc.) only later after the award. The data sets are in constant development. If you have additional information about an entry or have discovered an error, please feel free to contact me.

opencc-by-4.0Feb 2023View details →

Smart house measurements

Load Forecasting Dataset   Readme File   VARLAB – The Centre for Research & Technology, Hellas [CERTH] - Informatics and Telematics Institute [ITI] - <a href="https://varlab.iti.gr/">https://varlab.iti.gr/</a> Authors: Chrysovalantis-George Kontoulis, Georgios Stavropoulos, Dimosthenis Ioannidis   Publication Date: February -, 2023                                                                                                                                                                                                                   This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 957406 (TERMINET).   1.Introduction This dataset features information from a smarthome located at Greece, which features the Mediterranean climate. The building is utilized as a modern workplace that is being used for various every day activities. It is equipped with numerous smart devices and appliances, from smart lights to smart a elevator, while also featuring PVTs.   2.Dataset Overview 2.1Dataset Collection The system is built on multiple communication protocols including EnOcean, Zigbee, Modbus, BACnet, and, LTE/IEEE 802.15.4 at 2.4GHz. For the sensor data collection, a raspberry Pi microcontroller was used, and data were subsequently transmitted to the storage database. The extraction period of the data is between 2021-01-01 through 2022-12-20. Along this period there is a total of 66619 unique recordings and the time granularity of the data is set to 15 minutes for all devices.   2.2Data Peculiarities The place is occupied from Monday to Friday from 9:00 AM GMT+2 (Greenwich Mean Time) all the way through 5:00 PM GMT+2. Note that the building is not active during Greek public holidays, but some computers or servers might be on and consuming electrical energy. Also, there are some irregularities in the data reporting consistency at summer, Christmas & Easter as the building is not occupied for a long time of period. Timestamps of the dataset are in the GMT+2 timezone.   2.3Dataset Structure This dataset includes a total of six features and it can be used for Electrical, Thermal and Cooling Load forecasting. Electricity Consumption is the consumption of the whole house, Air-condition Status is either 1 or 0 for on and off, respectively, Luminance is how bright a space is, Light Dimming is the dimming of the lights in each room. Finally we have the Indoor Temperature for each room and the Outdoor Temperature. Data are extracted from four rooms in total. Note that in rooms 1 and 3, there is only one indoor temperature device, thus values are identical for temperature_room_1 and temperature_room_3. Note that sensors have some null values, which is generally either due to inactivity, e.g., the Light Dimming sensor and the Air-condition Status are event-based or due to potential system downtime. The provided dataset is stored in csv format. A brief overview of the dataset is presented at the Table 3.1.   Table 2.1 Dataset overview <table> <tbody> <tr> <td> Censor </td> <td> Symbolic Naming </td> <td> Measurement Unit </td> </tr> <tr> <td> Electricity Consumption </td> <td> KWh_S_total </td> <td> kWh </td> </tr> <tr> <td> Air-condition Status </td> <td> status_room_0 status_room_1 status_room_2 status_room_3 </td> <td> - </td> </tr> <tr> <td> Luminance </td> <td> luminance_room_0 luminance_room_1 luminance_room_2 luminance_room_3 </td> <td> Lux </td> </tr> <tr> <td> Light Dimming </td> <td> dimming_room_0 dimming_room_1 dimming_room_2 dimming_room_3 </td> <td> % </td> </tr> <tr> <td> Indoor Temperature </td> <td> temperature_room_0 temperature_room_1 temperature_room_2 temperature_room_3 </td> <td> °C </td> </tr> <tr> <td> Outdoor Temperature </td> <td> airTemperature </td> <td> °C </td> </tr> </tbody> </table>     2.4Descriptive Statistics Table 2.2 provides a brief overview of the key statistical characteristics of the data to. The table presents a summary of important metrics and measures, including measure of central tendency such as the mean, as well as measures of variability such as the standard deviation. Table 2.2 Descriptive Statistics <table align="center"> <tbody> <tr> <td> Symbolic Naming </td> <td> Values Count </td> <td> Mean </td> <td> Std </td> <td> Min </td> <td> Max </td> </tr> <tr> <td> KWh_S_total </td> <td> 62877 </td> <td> 71511,16 </td> <td> 52235,30 </td> <td> 2,22 </td> <td> 135494,70 </td> </tr> <tr> <td> status_room_0 status_room_1 status_room_2 status_room_3 </td> <td> 16689 14357 13302 13388 </td> <td> 0,38 0,15 0,27 0,26 </td> <td> 0,49 0,36 0,44 0,44 </td> <td> 0,00 0,00 0,00 0,00 </td> <td> 1,00 1,00 1,00 1,00 </td> </tr> <tr> <td> luminance_room_0 luminance_room_1 luminance_room_2 luminance_room_3 </td> <td> 31676 14727 6799 23993 </td> <td> 169,93 165,95 99.71 205,66 </td> <td> 294,60 267,69 157,40 304,14 </td> <td> 0.00 0.00 0.00 0.00 </td> <td> 1024,00 1024,00 1024,00 1024,00 </td> </tr> <tr> <td> dimming_room_0 dimming_room_1 dimming_room_2 dimming_room_3 </td> <td> 432 683 8 608 </td> <td> 1,95 41.29 15,00 42,40 </td> <td> 11,23 40.32 22,68 43,43 </td> <td> 0,00 0,00 0,00 0, 00 </td> <td> 100,00 100,00 50,00 100,00 </td> </tr> <tr> <td> temperature_room_0 temperature_room_1 temperature_room_2 temperature_room_3 </td> <td> 26915 33786 34778 33786 </td> <td> 27,50 24,28 23,89 24,28 </td> <td> 4,44 2,96 4,52 2,96 </td> <td> 17,54 13,95 7,95 13,95 </td> <td> 44,30 34,62 35,59 34,62 </td> </tr> <tr> <td> airTemperature </td> <td> 47647 </td> <td> 16,91 </td> <td> 8,62 </td> <td> -4,52 </td> <td> 40,28 </td> </tr> </tbody> </table>   3.Acknowledgment   This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 957406 (TERMINET).