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Metadata for the urbisphere-Paris campaign during 2022-2024: fieldwork maintenance log [L1]
<p>Machine-readable, formatted and redacted electronic fieldwork logs from the urbisphere-Paris observation campaign conducted between 2022-09-05 and 2024-07-22 in Paris, France. Provided in text format with comma separated columns (.csv) and in Microsoft Excel (.xlsx) format.</p> <p>The fieldwork logs are created from raw google form data submitted by campaign managers, scientists, technicinas and students. The formatting process is detailed in https://github.com/Urban-Meteorology-Reading/urbisphere-paris-fieldwork-log-format. The GitHub output has then been manually edited and adjusted.</p> <p>Contains maintenance information for the following observational sites operated as part of the urbisphere Paris campaign 2022 - 2024:</p> <table> <tbody> <tr> <td>PAARBO</td> <td>Paris – Arboretum de Vallée-aux-Loups </td> </tr> <tr> <td>PAAUNA</td> <td>Paris – Aunay-sous-Auneau</td> </tr> <tr> <td>PABOBI</td> <td>Paris – Bobigny</td> </tr> <tr> <td>PABONN</td> <td>Paris – Bonniel</td> </tr> <tr> <td>PABPAC</td> <td>Paris – Balloon Parc Andre Citroën</td> </tr> <tr> <td>PACHAM</td> <td>Paris – Chamant</td> </tr> <tr> <td>PACHAN</td> <td>Paris – Changis-sur-Marne </td> </tr> <tr> <td>PACHEM</td> <td>Paris – Chemin Vert Bobigny</td> </tr> <tr> <td>PACOMP</td> <td>Paris – Compiègne</td> </tr> <tr> <td>PACOUR</td> <td>Paris – Courdimanche-sur-Essonne</td> </tr> <tr> <td>PACRET</td> <td>Paris – Créteil</td> </tr> <tr> <td>PADENF</td> <td>Paris – Denfert Rocherau </td> </tr> <tr> <td>PADROU</td> <td>Paris – Droue Sur Drouette</td> </tr> <tr> <td>PAHOTE</td> <td>Paris – Hôtel de Ville</td> </tr> <tr> <td>PAJUSS</td> <td>Paris – Jussieu </td> </tr> <tr> <td>PALUPD</td> <td>Paris – Université Paris Diderot (LISA Platform)</td> </tr> <tr> <td>PAMEUD</td> <td>Paris – Meudon</td> </tr> <tr> <td>PANANG</td> <td>Paris – Nangis</td> </tr> <tr> <td>PANATI</td> <td>Paris – Rue Nationale</td> </tr> <tr> <td>PAPRUN</td> <td>Paris – Prunay-le-Temple</td> </tr> <tr> <td>PAROIS</td> <td>Paris – Roissy</td> </tr> <tr> <td>PAROMA</td> <td>Paris – Romainville</td> </tr> <tr> <td>PASIRT</td> <td>Paris – SIRTA Observatory Palaiseau</td> </tr> <tr> <td>PASTFE</td> <td>Paris – Saint Félix</td> </tr> <tr> <td>PAWYDT</td> <td>Paris – Wy-dit-Joli-Village</td> </tr> </tbody> </table> <p> </p>
FULFILL dataset - housing policy acceptability - framing experiment Latvia
<p>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.</p> <p>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.</p> <p>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.</p>
FULFILL dataset - housing policy acceptability - framing experiment Italy
<p>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.</p> <p>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.</p> <p>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.</p>
FULFILL dataset - housing policy acceptability - framing experiment France
<p>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.</p> <p>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.</p> <p>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.</p>
FULFILL dataset - housing policy acceptability - framing experiment Germany
<p>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.</p> <p>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.</p> <p>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.</p>
FULFILL dataset - housing policy acceptability - framing experiment Denmark
<p>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.</p> <p>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.</p> <p>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.</p>
Drone onboard multi-modal sensor dataset for complex outdoor scenarios
<p>The Data acquisition missions were designed and executed using DJI Pilot 2’s flight route planning feature. The missions encompassed five distinct geometric patterns: 1. triangular, 2. circular, 3. rectangular, 4. linear, and 5. multi-dimensional. Each mission was configured as a waypoint flight path, allowing precise customization of parameters such as altitude, speed, and turning angle for each waypoint. The dataset consists of 3D space flight data such as take-off, landing and varying altitude to introduce the z-axis changes. It must be noted that data was logged at a frequency of 10 Hz.</p> <p>To ensure consistency within the data, identical parameters were maintained across all data acquisition missions. The dataset comprises 20 distinct flights, with each flight path repeated multiple times, resulting in approximately 30 minutes of flight time per mission. The dataset is structured as time-series data, with each flight uniquely identified by a flight number and corresponding timestamp. The drone's spatial position is represented by the variables <strong>position_x, position_y, position_z </strong>while its orientation is captured by the variables <strong>orientation_x, orientation_y, orientation_z, orientation_w</strong>. Additionally, the drone's velocity and angular velocity are represented by the variables <strong>velocity_x, velocity_y, velocity_z, angular_x, angular_y, angular_z </strong>respectively. The linear acceleration is described by the variables <strong>linear_acceleration_x, linear_acceleration_y, linear_acceleration_z</strong>. The dataset also includes environmental data such as <strong>wind_speed, wind_angle </strong>using the TriSonica Mini Wind and Weather Sensor as well as information regarding the drone's battery status, including <strong>battery_voltage, battery_current.</strong></p> <p><strong>Data Acquisition Paths: <a href="https://ucy-my.sharepoint.com/:i:/g/personal/ygrigo01_ucy_ac_cy/EYAgdcLGCWxPloO1NMnsF-8Btf390Kmx854IuDe9R3E1ig?e=3Trbuk">Data acquisition paths</a></strong></p> <p>The dataset includes labels for various operational states of the drone, such as IDLE_HOVER, ASCEND, TURN, HMSL and DESCEND. These labels can be utilized to classify the drone's current activity. Moreover, the annotated dataset can be applied in multi-task learning to predict the drone's trajectory.</p> <p>The DJI Matrice 300 RTK is utilized as the primary platform for data acquisition, leveraging its compatibility with onboard development kits to facilitate the extraction of data from its integrated sensors and flight controller. To execute the developed software the NVIDIA Jetson Xavier NX serves as the embedded computing device. Utilizing the Onboard software development kit the Jetson Xavier NX enables real-time access and processing of data from the drone's sensors and flight controller.</p>
6C dataset for Mw 7.4 Hualien earthquake on 2024-04-02 at station locations MDSA0 and NA01.
<p>This repository contains the 6-component data set recording the Hualien Mw 7.4 earthquake on 2024-04-02. It contains the data for two station locations MDSA0 and NA01. For both locations there are 3 component rotation rates and 3 component accelerations. The instrument type and the coresponding response is in the file instrument_response.txt. It also includes the coordinates of both stations.</p> <p>After removing the response the acceleration data will be in units 'm/s/s' and the rotation data will be in 'rad/s'. </p> <p>The MDSA0 station is situated in Hualien, Taiwan. One blueSeis-3A rotational sensor is collocated with a Nanometrics Titan accelerometer at depth of 0.5 m in a vault structure.</p> <p>The NA01 station is situated in Nanao, Taiwan. One blueSeis-3A rotational sensor is collocated with a Kinemetrics EpiSensor accelerometer at depth of 2 m in a vault structure.</p>
Interlaboratory study: Testing reproducibility of solid biofuels component identification using reflected light microscopy
<p><strong>Submitted data was used to write an article: </strong>Drobniak, A., Mastalerz, M., Jelonek, Z., Jelonek, I., Adsul, T., Andolšek, N., Ardakani, O.H., Congo, T., Demberelsuren, B., Donohoe, B.S., Douds, A., Flores, D., Ganzorig, R., Ghosh, S., Gize, A., Goncalves, P.A., Hackely, P., Hatcherian, J., Hower, J.C., Kalaitzidis, S., Kędzior, S., Knowles, W., Kuś, J., Lis, K., Lis, G., Liu, B., Luo, Q., Du, M., Mishra, D., Misz-Kennan, M., Mugerwa, T., O'Keefe, J., Park, J., Pearson, R., Petersen, H., Reyes, J., Ribeiro, J., Niedzwiedzkas, J.L., de la Rosa Rodriguez, G., Sosnowski, P., Valentine, B., Varma, A., Wojtaszek-Kalaitzidi, M., Xu, Z., Zdravkov, A., Ziemianin, K., Interlaboratory study: Testing reproducibility of biomass fuels component identification using reflected light microscopy. International Journal of Coal Geology 277, 104331. <a href="https://doi.org/10.1016/j.coal.2023.104331">https://doi.org/10.1016/j.coal.2023.104331</a>.</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: </strong>Considering global market trends and concerns about climate change and sustainability, increased biomass use for energy is expected to continue. As more diverse materials are being utilized to manufacture solid biomass fuels, it is critical to implement quality assessment methods to analyze these fuels thoroughly. One such method is reflected light microscopy (RLM), which has the potential to complement and enhance current standard testing, leading to improving fuel quality assessment and, ultimately, preventing avoidable air pollution. An interlaboratory study (ILS) was conducted to test the reproducibility of biomass fuels component identification using a reflected light microscopy technique. The exercise was conducted on thirty photomicrographs showing biomass and various undesired components (like plastics or mineral matter), which were purposely added (by the ILS organizers) to contaminate wood pellets and charcoal-based grilling fuels. Forty-six participants had various levels of difficulty identifying the marked components, and as a result, the percentage of correct answers ranged from 52.2 to 94.4%. Among the most difficult components to distinguish were petroleum products and inorganic matter. Various reasons led to the misidentification, including insufficient morphological descriptions of the components provided to participants, ambiguities of the nomenclature, limitations of the analytical and exercise method, and insufficient experience of the participants. Overall, the results indicate that RLM has the potential to enhance the quality assessment of biomass fuels. However, they also demonstrate that the petrographic classification used in this exercise requires further refinement before it can be standardized. While a new simplified classification of solid biomass fuels components was created as an outcome of this study, future research is necessary to refine the nomenclature, develop a microscopic morphological description of the components, and verify the accuracy of component identification with a follow-up ILS.</p>
Concentration of nanoparticles per mL for water samples collected from Venice Lagoon
<p>The concentration of nanoparticles from surface seawater collected from the three sites of Venice Lagoon, Venice-Lido Port Inlet, Grand Canal under Rialto Bridge, and Saint Marc basin was analyzed via the Nanoparticle Tracking Analysis technique. Five replications were tested for each sample. Sampling locations: Venice-Lido Port Inlet, GPS coordinates: latitude: 45.431508, longi-tude: 12.406952; Grand Canal under Rialto Bridge, GPS coordinates: latitude: 45.438350, longitude: 12.336311; and Saint Marc basin, GPS coordinates: latitude: 45.431962, longitude: 12.340953.</p>
Unveiling the potential of redox chemistry to form size tunable, high index silicon particles
<p>In the present work, the effect of changing the precursor ratio of silicon between sodium silicde and a hexacoordinated silicon complex to form various sizes of particles is studied. TEM images show the size difference between particles produced with different ratios. Particles produced with a 1:1 ratio are 45 nm in diameter and up to a 1:4 precursor ratio is used to make 230 nm particles. X-ray diffraction patterns confirm the presence of crystalline silicon for all sizes, while Raman spectroscopy shows how different degree of oxidation occurs thanks to different particle sizes, shifting the Raman peak. The surface chemistry is also studied to evidence the growth mechanism.</p>
The Interplay between Hebbian and homeostatic plasticity in the Adult Visual cortex
<p>Data linked to the article "The interplay between Hebbian and homeostatic plasticity in the adult visual cortex", Journal of Physiology, DOI: <a href="https://doi.org/10.1113/JP287665">https://doi.org/10.1113/JP287665</a></p> <p>Data from binocular rivalry measurements and processed data from EEG Visual Evoked Potentials (VEP) are separated in different files.</p> <p>The ocular dominance index (ODI) files are split in two: the "ODI_values" file contains the raw measurements from participants, and the "change_from_baseline file" contains the same data normalized to baseline for each measurement.</p> <p>In both files, each column refers to a different measurement and condition:</p> <p>noHFS: data measured with the 17Hz HFS block before monocular deprivation<br>HFS: data measured with the 8.6Hz HFS block before monocular deprivation</p> <p>Baseline: Ocular dominance index measured at the start of the session, before any manipulation<br>Post_MD_1: first measurement after 60 minutes monocular deprivation (starting immediately after the end of deprivation)<br>Post_MD_2: second measurement after 60 minutes monocular deprivation (starting 11 minutes after the end of deprivation)<br>Post_MD_3: third measurement after 60 minutes monocular deprivation (starting 22 minutes after the end of deprivation)</p> <p>In VEP files, each column refers to a different condition:</p> <p>HFS: VEP recorded in the high-frequency stimulation condition, no monocular deprivation<br>HFS_MD: VEP recorded in the high-frequency stimulation condition with monocular deprivation<br>noHFS: VEP recorded in the condition where the HFS block was withheld, as a control for its role in our effect</p> <p>pre: first 500 measurements, before the High-Frequency Stimulation (HFS) block<br>post: last 500 measurements, after the HFS block (or after the break in the noHFS condition).</p>
2005-2099 High resolution bioclimatic variables for the surface and bottom of the Mediterranean Sea.
<p><em><span>This dataset provides annual statistical descriptors (mean, minimum, maximum, range and standard deviation) of key biogeochemical and physical variables for the Mediterranean Sea. It covers the period 2005-2099 under the RCP8.5 scenario, with a spatial resolution of 1/24 degree (~4km²). Variables include temperature, salinity, pH, water velocity, nutrients (NO3, PO4, NH4), dissolved inorganic carbon, oxygen, and net primary production. Data are available for both surface and at bathymetry level. The original projections were generated using OGSTM-BFM and MFS16 models at daily time and 1/16 degree grid resolution. We downscaled these to 1/24 degree and applied Quantile Delta Mapping bias correction using CMEMS reanalysis products for 2005-2020. The dataset is provided in a user-friendly format, making it accessible for various ecological and environmental modelling applications.</span></em></p>
Kathmandu Valley Single Hazards and Multi-Hazard Interrelationships Database
<p>This <em>Kathmandu Valley Single Hazards and Multi-Hazard Interrelationships Database</em> uses a systematic review of blended evidence types (academic literature, grey literature, media, databases, and social media) to compile single hazard and multi-hazard interrelationship exemplars of natural hazards in the context of Kathmandu Valley.</p> <p>We identify 58 sources of evidence for single hazard types and 21 sources of evidence for multi-hazard interrelationships. These sources evidence 21 single hazard types across six hazard groups, and 83 multi-hazard interrelationships that could influence Kathmandu Valley. Of these multi-hazard interrelationships, 12 have direct case study evidence of previous influence in Kathmandu Valley.</p> <p>This Excel database accompanies the paper Thompson et al. (2024).</p> <p>The <em>Kathmandu Valley Single Hazards and Multi-Hazard Interrelationships Database </em>comprises the following sheets: <br>A. Single Hazards Evidence <br>B. Hazard Interrelationships Evidence <br>C. Hazard Interrelationships Matrix <br>D. Matrix Evidence <br>E. Definitions (Source Types) <br>F. Definitions (Hazards) <br>G. Definitions (Interrelationships) <br>H. References </p> <p>In Sheet A, each row in the database describes a separate source of evidence of a single hazard influencing Kathmandu Valley. In each column, we describe the evidence using the qualifiers outlined below:</p> <ul> <li>Hazard type</li> <li>Source information and link</li> <li>Source content</li> <li>Hazard interrelationships and anthropogenic processes</li> <li>Video evidence</li> <li>Source reflections</li> <li>Major event typical frequency reflection</li> <li>Any other reflection on a single hazard</li> <li>Impact</li> </ul> <p>In Sheet B, each row in the database describes a separate source of evidence of a multi-hazard interrelationship influencing Kathmandu Valley. In each column, we describe the evidence using the qualifiers outlined below:</p> <ul> <li>Hazard type</li> <li>Source information and link</li> <li>Source content</li> <li>Hazard sequence</li> <li>Source reflections</li> <li>Impact</li> <li>Input from practitioner stakeholders</li> <li>Input from practitioner stakeholders - prioritisation</li> </ul> <p>We refer the reader to Thompson et al. (2024) for details of the methodology used to populate this database.</p> <p><strong>References</strong></p> <p>Thompson, H. E., Gill, J. C., Šakić Trogrlić, R., Taylor, F. E., and Malamud, B. D.: A methodology to compile multi-hazard interrelationships in a data-scarce setting: an application to Kathmandu Valley, Nepal, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2024-101, in review, 2024.</p>
EISCAT Svalbard radar Common Program data from February 26 to February 28 2023, which is processed by GUISDAP
<p>This is two-dimensional (time and altitude) ionospheric parameter data that contains electron density, electron temperature and ion temperature. It is estimated based on EISCAT Svalbard radar measurement implemeted as common program from February 26 to Feburuary 28, 2023 (https://portal.eiscat.se/) and processed by a software for incoherent scatter radar analysis, GUISDAP (https://gitlab.com/eiscat/guisdap9). The more detailed descriptions can be found as metadata in the uploaded netCDF file.</p>
Low-voltage Secondary Electron Emission Spectromicroscopy using a Scanning Auger Microscope
<p>Secondary electron emission is considered a well-established nano-scale probe for mapping the surface morphology of materials. It has also been demonstrated that secondary electrons (SE) emitted from materials can provide additional information on the local work function, bulk density of state (DOS), surface potential, charging/discharging characteristics, and elemental/chemical properties of bulk materials. The nano-scale lateral resolution and surface sensitivity of low-voltage scanning microscopes give them a unique advantage for the investigation of surfaces. However, the surface contamination caused by exposure to electron beams has always been a limiting factor for this purpose. Since the yield of SE emission is higher than that of Auger emission, the secondary electron emission spectromicroscopy (SEES) performed in an ultra-high vacuum chamber using a scanning Auger microscope (SAM) can be a very powerful tool for surface characterization, especially in the case of ultra-thin materials.</p> <p>We adapt our scanning auger microscope (SAM), equipped with a cylindrical mirror analyzer (CMA) and operated in an ultra-high vacuum, to SEES by tilting the sample holder and applying a negative bias to the sample. We also presented SEES signals of Chromium thin film at low voltages of 500 and 1000 V.</p>
Dataset on the Index of Sustainable Economic Welfare for the EU27 and beyond.
<div>This dataset contains data about two ISEWs for the EU27, its individual Member States (MS), the UK and the US. Following Van der Slycken and Bleys (2023) (1), two variants of the ISEW are presented in this dataset: the ISEW_BCE accounts for the benefits and costs of the present and pasts activities experienced in the present and within a specific country (Benefits and Costs Experienced); the ISEW_BCPA accounts for the benefits and costs of present activities experienced in the present and in the future, both domestically and internationally (Benefits and Costs of Present economic Activities).</div> <div> </div> <div>This document contains different datasets. Two datasets contain a summary of the values of the ISEWs and their components in ‘per capita’ terms. One summary presents the results for the EU27 (and MS) and the other one presents the results for the UK and the US (Non-EU countries). Additionally, each component is presented in some details in different pages, allowing to see the value of the different subcomponents included in each component (and even the value of some items with subcomponents for some components).</div> <div> </div> <div>The period covered by this dataset is 1995-2020.</div> <div> </div> <div>All the components are described in the accompanying table and in the report.</div> <div> </div> <div> </div> <div>(1) Van der Slycken, J. and Bleys, B. (2023). Towards ISEW and GPI 2.0: Dealing with Cross-Time and Cross-Boundary Issues in a Case Study for Belgium. <em>Social Indicators Research</em>, 168(1):557-583.</div>
FULFILL dataset round 2 Latvia
<p>This dataset and codebook correspond to the second round of survey data gathered in Latvia in 2023, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. </p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. The first round of the survey, consisted of recruiting a representative sample of approximately 2000 households in each country. In this second survey round, we recruit around 500 respondents from the initial survey round, ensuring representativity is maintained.</p> <p>This survey is very similar to the survey in the first round and includes a lot of identical items, including a quantitative assessment of the carbon footprint in the housing, mobility, and diet sectors, socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.</p> <p>New for this second round, we have incorporated questions regarding the measures respondents adopted in response to the 2022 energy crisis.</p>
FULFILL dataset round 2 Italy
<p>This dataset and codebook correspond to the second round of survey data gathered in Italy in 2023, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. </p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. The first round of the survey, consisted of recruiting a representative sample of approximately 2000 households in each country. In this second survey round, we recruit around 500 respondents from the initial survey round, ensuring representativity is maintained.</p> <p>This survey is very similar to the survey in the first round and includes a lot of identical items, including a quantitative assessment of the carbon footprint in the housing, mobility, and diet sectors, socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.</p> <p>New for this second round, we have incorporated questions regarding the measures respondents adopted in response to the 2022 energy crisis.</p>
FULFILL dataset round 2 Germany
<p>This dataset and codebook correspond to the second round of survey data gathered in Germany in 2023, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. </p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. The first round of the survey, consisted of recruiting a representative sample of approximately 2000 households in each country. In this second survey round, we recruit around 500 respondents from the initial survey round, ensuring representativity is maintained.</p> <p>This survey is very similar to the survey in the first round and includes a lot of identical items, including a quantitative assessment of the carbon footprint in the housing, mobility, and diet sectors, socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.</p> <p>New for this second round, we have incorporated questions regarding the measures respondents adopted in response to the 2022 energy crisis.</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.