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1600 years of modelled energy production and demand for European Countries (Norway, France, Italy, Spain, and Sweden)
<h3>Citation</h3> <p>When using this dataset, please cite the following paper: van der Most et al. Temporally compounding energy droughts in European electricity systems with hydropower, 10 January 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3796061/v1].</p> <h3>Description</h3> <p>This dataset contains daily renewable energy production and demand data used in the study "Temporally compounding energy droughts in European electricity systems with hydropower". The dataset includes production data for various renewable energy sources (offshore wind, onshore wind, solar photovoltaics, run-of-river, and hydropower reservoir inflow) and electricity demand. It was generated wit the use of 1600 years of climate model data and a daily renewable electricity production and demand modelling framework. The study focuses on five European countries with significant hydropower capacities: Norway, France, Italy, Spain, and Sweden.</p> <h3>Content</h3> <ul> <li> <p><strong>Energy Production Data</strong>:</p> <ul> <li>Offshore and Onshore Wind Power: Derived from 10 m wind speed data extrapolated to hub height, using power law equations and cubic power curves.</li> <li>Solar Photovoltaics (PV): Based on solar irradiance and temperature-dependent cell efficiency calculations.</li> <li>Hydropower: Includes inflow data for run-of-river and reservoir hydropower systems modelled with routed runoff data</li> <li>Hydropower dispatch is modelled at the national level using a linear optimization approach that aims to minimize the difference between demand and the sum of all renewable energy production over a year, directing the solution to following the load curves.</li> </ul> </li> <li> <p><strong>Energy Demand Data</strong>:</p> <ul> <li>Daily load data from ENTSO-E tranparancy fitted using a logistic smooth transmission regression approach to national mean, population-weighted daily near-surface temperatures from ERA5 reanalysis data.</li> <li>Demand curves account for weekdays and weekends but exclude cultural and socio-economic factors such as holidays.</li> </ul> </li> </ul> <h3>Methodology</h3> <p>The dataset is generated using the KNMI Large Ensemble Time Slice (KNMI-LENTIS) dataset, which includes 160 sets of 10-year physical climate model simulations of present-day climate (2000-2009). The simulations are conducted with the EC-Earth3 global climate model. The energy production and demand data are modeled to assess the impact of meteorological drivers on energy systems, with a focus on identifying periods of high residual loads (energy droughts). The model set-up has been validated with the use of ERA5 data in previous work. </p> <h3>Usage</h3> <p>This dataset is intended for researchers and policymakers interested in studying the impact of climate variability on renewable energy systems. It provides insights into how different meteorological conditions can lead to energy droughts and offers a basis for developing strategies to enhance the resilience of energy systems.</p> <p> </p>
Hågaån Catchment Continuous Sensor Data, Uppsala, Sweden, 2018-2023
This data package includes continuous sensor data used to model stream metabolism and carbon dioxide emissions from the Hågaån stream catchment in Uppsala, Sweden. Data is in 30 minute increments from 2018 to 2023 and includes discharge, water temperature, turbidity, specific conductivity, temperature, dissolved carbon dioxide, and dissolved oxygen. The data package is complete.
S78 | SLUPESTTPS | Pesticides and TPs from SLU, Sweden
<p>This is the collection associated with list S78 SLUPestTPs Pesticides and TPs from SLU, Sweden on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>Suspect list of pesticides and pesticide transformation products (TPs) from SLU, created based on Sweden’s national monitoring program and the pesticide properties database (PPDB) described in Frank Menger <em>et al </em>(2021) DOI: <a href="https://doi.org/10.1021/acs.est.1c00466">10.1021/acs.est.1c00466</a>.</p> <p>Updates: 27 Apr. 2021 - added new CIDs, replaced InChIKey file with *.txt version not *.inchikey. 10 May: added missing reference fields to transformations file. May 2023: updated 4 entries with new SMILES/CIDs according to feedback from <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/-/commit/c3e0e4549bc00f67cc266d7dfbf064ea858b453e">PubChem</a>. April 2025: reverted 1 CID back to a <a href="https://gitlab.com/uniluxembourg/lcsb/eci/pubchem/-/commit/c42e0e1dadb1317f29a32652e62850341e6b2e59">new (old) preferred form</a> due to annotation disappearing. 2 Jun 2025: adjusted TFA synonym.</p>
A comparative dataset on public perceptions of multiple risks during the COVID-19 pandemic in Italy and Sweden
<p>These datasets are the result of two nation-wide surveys conducted in Italy and Sweden in August 2020 and in november 2020. The surveys (which are identical in the two rounds) explore the respondents' risk perception, preparedness, knowledge, and experience regarding a set of hazards, namely: epidemics, floods, droughts, earthquakes, wildfires, terror attacks, domestic violence, economic crises, and climate change. </p> <p>The data files include the questionnaire survey (the Italian and Swedish versions as well as the English translation) and the two datasets of all the answers to the two surveys. Each column in the dataset refers to an item in the survey (e.g. a question or a sub-question), and each row represents a single respondent. </p> <p>For additional information on the August 2020 dataset, see <a href="https://www.nature.com/articles/s41597-020-00778-7">Mondino et al. (2020)</a>.</p>
HomogWS-se: A century-long homogenized dataset of near-surface wind speed observations since 1925 rescued in Sweden
<p>Creating a century-long homogenized near-surface wind speed (WS) observation dataset is essential to improve our knowledge about the uncertainty and causes of WS stilling and recovery. We rescued paper-based WS records dating back to the 1920s at 13 stations in Sweden and established a four-step homogenization procedure to generate the first 10-member centennial homogenized WS dataset (HomogWS-se) for community uses among climatology, ecology, hydrology and energy industry. HomogWS-se can be used to study the WS variability and change, assess climate reanalysis, and constrain climate simulations for better future projection of changes in the WS and wind energy potential. HomogWS-se contains 13 individual text files with 10-member century-long homogenized monthly WS series, as well as the member-mean series.</p>
Integrated Agent-based Modelling and Simulation of Transportation Demand and Mobility Patterns in Sweden
<h2>About</h2> <p><span>The Synthetic Sweden Mobility (SySMo) model provides a simplified yet statistically realistic microscopic representation of the real population of Sweden. The agents in this synthetic population contain socioeconomic attributes, household characteristics, and corresponding activity plans for an average weekday. This agent-based modelling approach derives the transportation demand from the agents’ planned activities using various transport modes (e.g., car, public transport, bike, and walking).</span></p> <div> <p>This open data repository contains four datasets: </p> <p>(1) Synthetic Agents, </p> </div> <div> <p>(2) Activity Plans of the Agents, </p> </div> <div> <p>(3) Travel Trajectories of the Agents, and </p> </div> <div> <p>(4) Road Network (EPSG: 3006)</p> <p><span>(OpenStreetMap data were retrieved on August 28, 2023, from https://download.geofabrik.de/europe.html, and GTFS data were retrieved on September 6, 2023 from https://samtrafiken.se/)</span></p> <p><span>The database can serve as input to assess the potential impacts of new transportation technologies, infrastructure changes, and policy interventions on the mobility patterns of the Swedish population.</span></p> </div> <h2>Methodology</h2> <p>This dataset contains statistically simulated 10.2 million agents representing the population of Sweden, their socio-economic characteristics and the activity plan for an average weekday. For preparing data for the MATSim simulation, we randomly divided all the agents into 10 batches. Each batch's agents are then simulated in MATSim using the multi-modal network combining road networks and public transit data in Sweden using the package pt2matsim (https://github.com/matsim-org/pt2matsim). </p> <p>The agents' daily activity plans along with the road network serve as the primary inputs in the MATSim environment which ensures iterative replanning while aiming for a convergence on optimal activity plans for all the agents. Subsequently, the individual mobility trajectories of the agents from the MATSim simulation are retrieved.</p> <p>The activity plans of the individual agents extracted from the MATSim simulation output data are then further processed. All agents with negative utility score and negative activity time corresponding to at least one activity are filtered out as the ‘infeasible’ agents. The dataset ‘<strong>Synthetic Agents</strong>’ contains all synthetic agents regardless of their <span>‘<em>feasibility</em>’ (0=excluded & 1=included in plans and trajectories). In the other datasets, only agents with feasible activity plans are included. </span></p> <p>The simulation setup adheres to the MATSim 13.0 benchmark scenario, with slight adjustments. The strategy for replanning integrates BestScore (60%), TimeAllocationMutator (30%), and ReRoute (10%)— the percentages denote the proportion of agents utilizing these strategies. In each iteration of the simulation, the agents adopt these strategies to adjust their activity plans. The "BestScore" strategy retains the plan with the highest score from the previous iteration, selecting the most successful strategy an agent has employed up until that point. The "TimeAllocationMutator" modifies the end times of activities by introducing random shifts within a specified range, allowing for the exploration of different schedules. The "ReRoute" strategy enables agents to alter their current routes, potentially optimizing travel based on updated information or preferences. These strategies are detailed further in W. Axhausen et al. (2016) work, which provides comprehensive insights into their implementation and impact within the context of transport simulation modeling. </p> <h2>Data Description</h2> <h3>(1) Synthetic Agents</h3> <p>This dataset contains all agents in Sweden and their socioeconomic characteristics. </p> <p>The attribute ‘<span><em>feasibility</em></span>’ has two categories: <em>feasible</em><em> agents </em>(73%), and <em>infeasible agents</em> (27%). <span>Infeasible agents are agents with negative utility score and negative activity time corresponding to at least one activity.</span> </p> <p>File name: 1_syn_pop_all.parquet</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>PId</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td>Deso</td> <td>Zone code of Demographic statistical areas (DeSO)<sup>1</sup></td> <td>String</td> <td>-</td> </tr> <tr> <td> <pre>kommun</pre> </td> <td>Municipality code</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>marital </pre> </td> <td>Marital Status (single/ couple/ child)</td> <td>String</td> <td>-</td> </tr> <tr> <td> <pre>sex </pre> </td> <td>Gender (0 = Male, 1 = Female)</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>age</pre> </td> <td>Age</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>HId</pre> </td> <td>A unique identifier for households</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>HHtype </pre> </td> <td>Type of households (single/ couple/ other)</td> <td>String</td> <td>-</td> </tr> <tr> <td> <pre>HHsize </pre> </td> <td>Number of people living in the households</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>num_babies</pre> </td> <td>Number of children less than six years old in the household</td> <td>Integer</td> <td>-</td> </tr> <tr> <td>employment</td> <td>Employment Status (0 = Not Employed, 1 = Employed)</td> <td>Integer</td> <td>-</td> </tr> <tr> <td>studenthood</td> <td>Studenthood Status (0 = Not Student, 1 = Student)</td> <td>Integer</td> <td>-</td> </tr> <tr> <td>income_class</td> <td>Income Class (0 = No Income, 1 = Low Income, 2 = Lower-middle Income, 3 = Upper-middle Income, 4 = High Income)</td> <td>Integer</td> <td>-</td> </tr> <tr> <td>num_cars</td> <td>Number of cars owned by an individual </td> <td>Integer</td> <td>-</td> </tr> <tr> <td>HHcars</td> <td>Number of cars in the household</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>feasibility</pre> </td> <td>Status of the individual (1=feasible, 0=infeasible)</td> <td>Integer</td> <td>-</td> </tr> </tbody> </table> <p>1 <a href="https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/">https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/</a></p> <h3>(2) Activity Plans of the Agents</h3> <p>The dataset contains the car agents’ (agents that use cars on the simulated day) activity plans for a simulated average weekday. </p> <p>File name: <span>2_plans_i.parquet, i = 0, 1, 2, ..., 8, 9. (10 files in total)</span></p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>act_purpose</p> </td> <td> <p>Activity purpose (work/ home/ school/ other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>PId</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>act_end </p> </td> <td> <p>End time of activity (0:00:00 – 23:59:59)</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:seco</p> <p>nd</p> </td> </tr> <tr> <td> <p>act_id</p> </td> <td> <p>Activity index of each agent</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>mode</p> </td> <td> <p>Transport mode to reach the activity location</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>POINT_X </p> </td> <td> <p>Coordinate X of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>metre</p> </td> </tr> <tr> <td> <p>POINT_Y</p> </td> <td> <p>Coordinate Y of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>metre</p> </td> </tr> <tr> <td> <p>dep_time </p> </td> <td> <p>Departure time (0:00:00 – 23:59:59)</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:seco</p> <p>nd</p> </td> </tr> <tr> <td> <p>score</p> </td> <td> <p>Utility score of the simulation day as obtained from MATSim</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>trav_time </p> </td> <td> <p>Travel time to reach the activity location</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:seco</p> <p>nd</p> </td> </tr> <tr> <td> <p>trav_time_min </p> </td> <td> <p>Travel time in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>act_time </p> </td> <td> <p>Activity duration in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>distance</p> </td> <td> <p>Travel distance between the origin and the destination</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>speed</p> </td> <td> <p>Travel speed to reach the activity location</p> </td> <td> <p>Float</p> </td> <td> <p>km/h</p> </td> </tr> </tbody> </table> <h3>(3) Travel Trajectories of the Agents</h3> <p>This dataset contains the driving trajectories of all the agents on the road network, <span>and the public transit vehicles used by these agents, including buses, ferries, trams etc. The files are produced by MATSim simulations and organised into 10 *.parquet’ files (representing different batches of simulation) corresponding to each plan file.</span></p> <p>File name: <span>3_events_i.parquet, i = 0, 1, 2, ..., 8, 9. (10 files in total)</span></p> <p> </p> <table> <tbody> <tr> <td> <div> <div> <p><strong>Column </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Description </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Data type </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Unit </strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>time </p> </div> </div> </td> <td> <div> <div> <p>Time in second in a simulation day (0-86399) </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>second </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>type </p> </div> </div> </td> <td> <div> <div> <p>Event type defined by MATSim simulation* </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>person </p> </div> </div> </td> <td> <div> <div> <p>Agent ID </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>link </p> </div> </div> </td> <td> <div> <div> <p>Nearest road link consistent with the road network </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>vehicle </p> </div> </div> </td> <td> <div> <div> <p>Vehicle ID identical to person </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>from_node </p> </div> </div> </td> <td> <div> <div> <p>Start node of the link </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>to_node </p> </div> </div> </td> <td> <div> <div> <p>End node of the link </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> <p>* One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart) </p> <h3>(4) Road Network</h3> <p>This dataset contains the road network.</p> <p>File name: 4_network.shp</p> <table> <tbody> <tr> <td> <div> <div> <p><strong>Column </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Description </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Data type </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Unit </strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>length </p> </div> </div> </td> <td> <div> <div> <p>The length of road link </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>metre </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>freespeed </p> </div> </div> </td> <td> <div> <div> <p>Free speed </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>km/h </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>capacity </p> </div> </div> </td> <td> <div> <div> <p>Number of vehicles </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>permlanes </p> </div> </div> </td> <td> <div> <div> <p>Number of lanes </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>oneway </p> </div> </div> </td> <td> <div> <div> <p>Whether the segment is one-way (0=no, 1=yes) </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>modes </p> </div> </div> </td> <td> <div> <div> <p>Transport mode </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>from_node </p> </div> </div> </td> <td> <div> <div> <p>Start node of the link </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>to_node </p> </div> </div> </td> <td> <div> <div> <p>End node of the link </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>geometry </p> </div> </div> </td> <td> <div> <div> <p>LINESTRING (SWEREF99TM) </p> </div> </div> </td> <td> <div> <div> <p>geometry </p> </div> </div> </td> <td> <div> <div> <p>metre </p> </div> </div> </td> </tr> </tbody> </table> <p> </p> <p><strong><span>Additional Notes</span></strong></p> <p><span>This research is funded by the RISE Research Institutes of Sweden, the Swedish Research Council for Sustainable Development (Formas, project number 2018-01768), and Transport Area of Advance, Chalmers.</span></p> <p><strong><span>Contributions</span></strong></p> <p><span>YL designed the simulation, analyzed the simulation data, and, along with CT, executed the simulation. CT, SD, FS, and SY conceptualized the model (SySMo), with CT and SD further developing the model to produce agents and their activity plans. KG wrote the data document. All authors reviewed, edited, and approved the final document.</span></p>
Network Measurements while Uploading 5.6 KB Files from Moving Buses to Cellular Networks in Varmland, Sweden.
<p>The dataset and the collection methodology are described and used in the following papers:</p> <ul> <li>Ben Abdesslem, Fehmi, Henrik Abrahamsson, and Bengt Ahlgren.<br> "<em><strong>Measuring Mobile Network Multi-Access for Time-Critical C-ITS Applications" </strong></em><br> Network Traffic Measurement and Analysis Conference (TMA'18), Vienna, Austria (2018).</li> <li>Ben Abdesslem, Fehmi, Henrik Abrahamsson, and Bengt Ahlgren.<br> "<em><strong>Cellular Network Multi-Access Measurements on the Roads of Värmland, Sweden.</strong></em>" <br> <em>arXiv preprint arXiv:1805.06814</em> (2018).</li> <li>Henrik Abrahamsson, Ben Abdesslem, Fehmi, Bengt Ahlgren, Anna Brunstrom, Ian Marsh and Mats Björkman.<br> "<em><strong>Connected Vehicles in Cellular Networks: Multi-access versus Single-access Performance</strong></em>" <br> 2nd Workshop on Mobile Network Measurement (MNM’18), Vienna, Austria (2018).</li> </ul> <p>The CSV file has the following columns:</p> <ul> <li>Index: Unique number for the transaction</li> <li>Timestamp: Time and date of the transaction</li> <li>Interface: Interface used by the transaction [op0, op1 or op2]</li> <li>TransactionTime: Duration of the transaction (in sec)</li> <li>Status: Result of the transaction [failed, senderror, timeout, or number of received bytes acknowledged]</li> <li>GpsTimestamp: Time and date of the GPS coordinates</li> <li>GpsLatitude: Last GPS latitude known</li> <li>GpsLongitude: Last GPS longitude known</li> <li>ModemTimestamp: Time and date of the modem properties</li> <li>ModemOperator: Name of the operator [op0, op1, op2]. The original names (Telia, Telenor, 3) have been replaced in a different order.</li> <li>ModemRSSI: RSSI (in dBm)</li> <li>ModemCID: Cell ID</li> <li>ModemDeviceMode: <ul> <li>UNKNOWN (0).</li> <li>DISCONNECTED (1).</li> <li>NO_SERVICE (2).</li> <li>2G (3).</li> <li>3G (4).</li> <li>LTE (5).</li> </ul> </li> <li>ModemDeviceSubmode: <ul> <li>UNKNOWN (0).</li> <li>UMTS (1).</li> <li>WCDMA (2).</li> <li>EVDO (3).</li> <li>HSPA (4).</li> <li>HSPA+ (5).</li> <li>DC HSPA (6).</li> <li>DC HSPA+ (7).</li> <li>HSDPA (8).</li> <li>HSUPA (9).</li> <li>HSDPA+HSUPA (10).</li> <li>HSDPA+ (11).</li> <li>HSDPA+HSUPA (12).</li> <li>DC HSDPA+ (13).</li> <li>DC HSDPA + HSUPA (14).</li> </ul> </li> <li>ModemLAC: Location Area Code</li> <li>ModemRSRP: RSRP (in dBm)</li> <li>ModemFrequency: Frequency in Mhz</li> <li>ModemRSRQ: RSRQ (in dBm)</li> <li>ModemBand: LTE band</li> <li>ModemPCI: LTE Physical Cell ID</li> <li>ModemECIO: Ec/Io</li> <li>ModemENODEBID: eNodeB ID</li> <li>ModemRSCP: RSCP (in dBm)</li> <li>bus: Bus number (head node number in Monroe)</li> <li>country: Country of operation [Sweden]</li> <li>protocol: protocol used [UDP, TCP or HTTPS]</li> <li>experiment: Experiment ID (one hour experiments)</li> <li>diff: Max time difference between the three simultaneous uploads (in ms)</li> <li>TransactionTime200: Transaction duration if timeout=200ms</li> <li>TransactionTime1000:Transaction duration if timeout=1000ms</li> <li>TransactionTime6000: Transaction duration if timeout=6000ms</li> <li>bestAvailability: Best availability over the whole experiment ID (%)</li> <li>bestAvailability200: Best availability over the whole experiment ID (%) if timeout=200ms</li> <li>bestAvailability1000: Best availability over the whole experiment ID (%) if timeout=1000ms</li> <li>best: Best duration (in sec)</li> <li>best1000: Best duration (in sec) if timeout=1000ms</li> <li>availability: Availability over the whole experiment ID (%)</li> <li>availability200: Availability over the whole experiment ID (%) if timeout=200ms</li> <li>availability1000: Availability over the whole experiment ID (%) if timeout=1000ms</li> <li>DayOfWeek: Day of the Week [Monday, ..., Sunday]</li> </ul>
Animal bones from Iron Age settlements in Scania, Southern Sweden
<p>This data is a compilation of the zooarchaeological record from Iron Age settlements in Scania, southern Sweden. It consists of data from various technical reports produced between 1961 to 2019, by different analysts. Published reports and unpublished but archived communications are included. This data may be of interest to anyone interested in archaeological themes involving animals in any kind, such as economy, animal husbandry, animal production, hunting, fishing, and so on. It may also be of paleozoological interest, as it contains valuable fauna historical information such as presence of wild species of different kinds. </p> <p>The database is the basis for the published catalogue included in the book "Animal husbandry in Iron Age Scania, with a catalogue" published 2022. The book is open acess and you can download it via this link: https://www.ht.lu.se/en/series/9128370/</p> <p>The data can bee accessed through a one .csv-file, which is an export of the data set which was originally recorded in a MS Access-database. Both files are published in this version. The dataset consists of data on 130 animal bone assemblages from 101 Scanian settlement sites.</p> <p>The original Access-database, with two levels, one (Site) with descriptive information on the archaeological site (totally 12 variables), and one (zooarch-overview) with quantitative data on number of specimens, in general and per recorded taxa (totally 35 variables). Presence of bird, fish, amphibian and wild mammalian taxa is also included. </p> <p>Included is a READ ME (.csv) describing the data set in more detail.</p> <p>ERRATA (READ ME-file): No of observations is 130, not 131.</p>
Metagenome-assembled genomes from Stordalen Mire, Sweden (MAGs v2)
<p><strong>This release (MAGs v2) is a major new version of this metagenome-assembled genome (MAG) set.</strong> All previous releases on this page (which only differ in the metadata) are designated "MAGs v1." The current release (MAGs v2) uses<strong> </strong>CheckM2 v1.0.2 filtering (≥70% completeness, ≤10% contamination) to expand this dataset to include <strong>36,419 MAGs</strong>, with the following subcategories:</p> <ul> <li>Cronin_v1: Manually-curated subset of the "Field" category from MAGs v1.</li> <li>Cronin_v2: MAGs from raw bin filtering on the same assemblies used to generate Cronin_v1.</li> <li>Woodcroft_v2: MAGs from raw bin filtering on the same assemblies used to generate the MAGs reported in <a href="https://doi.org/10.1038/s41586-018-0338-1">Woodcroft & Singleton et al. (2018)</a>.</li> <li>SIPS: Updated genomes from samples originating from a stable isotope probing (SIP) incubation experiment by Moira Hough et al. ("SIP" in MAGs v1), re-analyzed due to read truncation and sample linkage issues in MAGs v1.</li> <li>JGI: Expanded set of genomes from the Joint Genome Institute's metagenome annotation pipeline.</li> </ul> <p> </p> <p>FILES:</p> <ul> <li><strong>Emerge_MAGs_v2.tar.gz</strong> - Archive containing the MAG files (.fna).</li> <li><strong>metadata_MAGs_v2_EMERGE.tsv</strong> - Table containing source sample names and accessions, GTDB taxonomy information, CheckM2 quality reports, NCBI GenomeBatch- and MIMAG(6.0)-formatted sample attributes and other metadata for the MAGs. </li> </ul> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute (<a href="https://emerge-bii.github.io">https://emerge-bii.github.io/</a>), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.</p> <p>This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440.</p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.</p> <p>Data collected at the Joint Genome Institute was generated under the following awards:</p> <ul> <li>The majority of sequencing at JGI was supported by BER Support Science Proposal 503530 (DOI: <a href="https://doi.org/10.46936/10.25585/60001148">10.46936/10.25585/60001148</a>), conducted by the U.S. Department of Energy Joint Genome Institute (<a href="https://ror.org/04xm1d337">https://ror.org/04xm1d337</a>), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.</li> <li>Sequencing of SIP samples was performed under the Facilities Integrating Collaborations for User Science (FICUS) initiative (proposal 503547; award DOI: <a href="https://doi.org/10.46936/fics.proj.2017.49950/60006215">10.46936/fics.proj.2017.49950/60006215</a>) and used resources at the DOE Joint Genome Institute (<a href="https://ror.org/04xm1d337">https://ror.org/04xm1d337</a>) and the Environmental Molecular Sciences Laboratory (<a href="https://ror.org/04rc0xn13">https://ror.org/04rc0xn13</a>), which are DOE Office of Science User Facilities. Both facilities are sponsored by the Office of Biological and Environmental Research and operated under Contract Nos. DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL).</li> </ul>
EVIDENT H2020– Environmental data for Sweden cities Dataset
<p>EVIDENT H2020- Environmental data for Swedish Cities Dataset</p> <p>Environmental data from 615 cities in Sweden</p> <p>Weather, in combination with residential characteristics and electricity consumption, might be useful to consider in association with other datasets. In the instance of EVIDENT, they will be examined in combination with electricity consumption to establish the correlation with weather and to examine if weather conditions contribute and should be considered for policy development.</p> <p>The data have been collected from 18.10.2021 to 4.05.2023 and refer to 615 Swedish cities. The collection has been carried out with agents created and by calling in API. In the file "Sweden_Cities_Avg_DaySect.xlsx", all cities have averaged from all measures. Also, the day has been divided into 3 sections and the averages apply to each section of the day.</p> <p>In each city, on average, there are 6 measurements per day. The source dataset is "swedish_cities_environmental.csv.". example</p> <table> <tbody> <tr> <td>country</td> <td>city</td> <td>temperature</td> <td>feels_like</td> <td>temp_min</td> <td>temp_max</td> <td>pressure</td> <td>humidity</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>wind_speed</td> <td>wind_deg</td> <td>sunrise</td> <td>sunset</td> <td>weather_description</td> </tr> </tbody> </table> <p>There are 2 more datasets, "swedish cities environmental_tranformDay.csv" and "swedish cities environmental_week.csv", and refer to transformations made in the original dataset.<br> The first file is about the day analysis, where the day has been divided into 3 sections and depending on the time of the measurement, a new column has been created in the Day section and can take values 0,1,2. In addition, there is the column day_hours which is the duration of the day in seconds from sunrise to sunset. Finally, there is pressure, humidity and wind speed. In the second file, the column weekday has been added and relates to the day of the week (e.g. Monday), and the daily analysis has been removed.</p> <p>More information can be found on the public deliverables of the EVIDENT project <a href="https://evident-h2020.eu/deliverables/">https://evident-h2020.eu/deliverables/</a>. More specifically, the experiment's theoretical framework and motivation are described in are described in deliverable D1.2 <a href="https://evident-h2020.eu/wp-content/uploads/2021/12/EVIDENT_D1.2_Assessing_behavioural_biases_and_financial_literacy.pdf">Assessing behavioural biases and financial literacy</a> and deliverable <strong>D1.3</strong> <a href="https://evident-h2020.eu/wp-content/uploads/2022/03/EVIDENT_D1.3_Specification_of_Big_Data_Analytics.pdf">Specifications of Big Data Analytics</a>, in section 4 while the final design is reported in <strong>D3.2</strong> <a href="http://evident-h2020.eu/wp-content/uploads/2023/01/EVIDENT_D3.2_Implementation-of-preparatory-actions-for-RCT-surveys-and-serious-game.pdf">Implementation of preparatory actions for RCT, surveys and serious game</a>.</p>
Effect of Restoration on Physical and Chemical Peat Properties in Previously Drained Boreal Peatlands, latitude 57-63, Sweden, 2021
The major objective behind peatland restoration is to improve ecosystem services, such as increased biodiversity, increased carbon sequestration, increased groundwater storage, and improved surface water quality. However, a century or more of drained conditions has drastically changed the soil properties in relation to natural wetlands and this is likely to profoundly influence the potential for various biogeochemical peat processes. Thus, peatland restoration may result in undesired impacts and potential environmental threats. Two such undesired effects are increased methane production and increased mercury methylation. In this study, we investigated how nine boreal peatlands across a latitudinal gradient in Sweden have been affected by rewetting after up to a century of drained conditions. Each peatland was sampled for three 50 cm deep peat cores that were analyzed for carbon, nitrogen, δ13C, δ15N, bulk density, and organic matter proportion. Adjacent to each restored peatland, we sampled a corresponding pristine (natural) peatland to facilitate a comparison of how the peat properties have been affected by drainage and subsequent rewetting of the peatlands. Groundwater depth was monitored at all peatland locations to confirm restored conditions at the rewetted peatlands. The results indicate that a long period of drained conditions and subsequent rewetting have changed the peat properties, with differences shown in C/N ratio, dry bulk density, and organic matter content. Rewetting will thus not regenerate a pristine environment. Instead, it creates new conditions to which various biogeochemical processes will respond and these do not necessarily represent conditions prior to disturbance. Our study will provide background information to understand the biogeochemical dynamics in peatlands after restoration, especially since the study covers a large span of nutrient conditions and catchment settings. This understanding will be fundamental for the development of strat
Västerbotten & Norrbotten Counties (Sweden) - NEVERMORE Climate Dataset
<p>The dataset consist of the historical and climate projection (CMIP6) for gridded atmospheric variables and the climate hazards/extreme events alongside the return values (likelihood) of hazards/extreme events. The dataset was developed during NEVERMORE project as part of WP3 from CMCC and NCSRD.</p>
Metagenome-assembled genomes from Stordalen Mire, Sweden (2019) (MAGs from long-read, short-read, & hybrid assemblies)
<p>METHODS:</p> <p>Soil samples (6 total) were collected at the Stordalen Mire site in 2019 from two depths (1-5 & 20-24 cm below ground) across three habitats (Palsa, Bog, and Fen). DNA was extracted based on the protocol described by <a href="http://dx.doi.org/10.17504/protocols.io.yxmvm244bg3p/v1">Li et al. (2024)</a>. For short reads, libraries were prepared at the Joint Genome Institute (JGI) with the KAPA Hyperprep kit, and sequenced with Illumina NovaSeq 6000. For long reads, libraries were prepared with the SMRTbell Express Template Prep Kit 2.0 (PacBio), then sequenced using PacBio Sequel IIe at JGI. PacBio data was processed at JGI to form filtered CCS (Circular Consensus Sequencing) reads. </p> <p>Assemblies were generated with short-only, long-only, and hybrid read sources: <strong>Short-only</strong> was assembled with <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411777/">metaSPAdes</a> (v3.15.4) using <a href="https://zenodo.org/records/10806928">Aviary</a> (v0.5.3) with default parameters. <strong>Long-only</strong> was assembled with <a href="https://www.nature.com/articles/s41592-020-00971-x">metaFlye</a> (v2.9-b1768) using <a href="https://zenodo.org/records/10806928">Aviary</a> (v0.5.3) with default parameters. <strong>Hybrid</strong> assembly was performed using <a href="https://zenodo.org/records/10806928">Aviary</a> v0.5.3 with default parameters. This involved a step-down procedure with long-read assembly through <a href="https://www.nature.com/articles/s41592-020-00971-x">metaFlye</a> (v2.9-b1768), followed by short-read polishing by <a href="https://genome.cshlp.org/content/27/5/737">Racon</a> (v1.4.3), <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112963">Pilon</a> (v1.24) and then Racon again. Next, reads that didn't map to high-quality metaFlye contigs were hybrid assembled with <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411777/">SPAdes (--meta option)</a> and binned out with <a href="https://peerj.com/articles/7359/">MetaBAT2</a> (v2.1.5). For each bin, the reads within the bin were hybrid assembled using <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005595">Unicycler</a> (v0.4.8). The high-coverage metaFlye contigs and Unicycler contigs were then combined to form the assembly fasta file. Genome recovery was performed using <a href="https://zenodo.org/records/10806928">Aviary</a> v0.5.3 with samples chosen for differential abundance binning by <a href="https://zenodo.org/records/10939393">Bin Chicken</a> (v0.4.2) using <a href="https://zenodo.org/records/7130825">SingleM metapackage S3.0.5</a>. This involved initial read mapping through <a href="https://zenodo.org/records/10531254">CoverM</a> (v0.6.1) using <a href="https://academic.oup.com/bioinformatics/article/34/18/3094/4994778">minimap2</a> (v2.18) and binning by <a href="https://peerj.com/articles/1165/">MetaBAT</a>, <a href="https://peerj.com/articles/7359/">MetaBAT2</a> (v2.1.5), <a href="https://www.nature.com/articles/s41587-020-00777-4">VAMB</a> (v3.0.2), <a href="http://doi.org/10.1038/s41467-022-29843-y">SemiBin</a> (v1.3.1), <a href="https://zenodo.org/records/10460259">Rosella</a> (v0.4.2), <a href="https://www.nature.com/articles/nmeth.3103">CONCOCT</a> (v1.1.0) and <a href="https://academic.oup.com/bioinformatics/article/32/4/605/1744462">MaxBin2</a> (v2.2.7). Genomes were analyzed using <a href="https://www.nature.com/articles/s41592-023-01940-w">CheckM2</a> (v1.0.2) and clustered at 95% ANI using <a href="https://zenodo.org/records/10526086">Galah</a> (v0.4.0).</p> <p> </p> <p>FILES:</p> <ul> <li><strong>EMERGE_MAGs_2019_long-short-hybrid.tar.gz</strong> - Archive containing the MAG files (.fna).</li> <li><strong>metadata_MAGs_2019_EMERGE.tsv</strong> - Table containing source sample names and accessions, GTDB classifications, CheckM2 quality information, NCBI GenomeBatch- and MIMAG(6.0)-formatted attributes, and other metadata for the MAGs.</li> </ul> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute (<a href="https://emerge-bii.github.io/">https://emerge-bii.github.io/</a>), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.</p> <p>This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440.</p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.</p> <p>Data from the Joint Genome Institute (JGI) was collected under BER Support Science Proposal 503530 (DOI: <a href="https://doi.org/10.46936/10.25585/60001148">10.46936/10.25585/60001148</a>), conducted by the U.S. Department of Energy Joint Genome Institute (<a href="https://ror.org/04xm1d337">https://ror.org/04xm1d337</a>), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.</p>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Sweden
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_SE: Swedish Board of Agriculture</li> <li>TSE_2022_SE: Swedish Board of Agriculture</li> <li>TSE_2021_SE: Swedish Board of Agriculture</li> <li>TSE_2020_SE: Swedish Board of Agriculture</li> <li>TSE_2019_SE: Swedish Board of Agriculture</li> </ul>
Trap catches of carrot fly (Psila rosae) and cutworm (Agrotis ipsilon) from 142 fields in Denmark and Southern Sweden 1997-2019
<p>The pests were caught in various vegetable crops in Denmark and Southern Sweden: Yellow sticky traps for carrot flies and pheromone traps for cutworm adults.</p> <p>The data is provided as both a tab-separated text file and a binary R data file. The R files provides code to read and plot the data. The two plots produced are also provided as PNG files.</p> <p>The data were collected by SEGES Innovation, Denmark.</p>
Municipal-level vaccination data (Sweden)
<p>This is a dataset created for a project investigating variation in COVID-19 vaccination rates in Swedish municipalities (aggregated). The dataset compiles data from several publicly available data sources, namely: the Swedish Public Health Agency, the Swedish Election Authority, Statistics Sweden, Frikyrkoundersökning, and the Swedish Association of Local Authorities and Region’s database (KOLADA).</p>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Sweden
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
Radiocarbon Dates Iron Production Middle-Northern Sweden
<p>Radiocarbon dates analysed in the MA-thesis of Jonatan Rigvald, Uppsala university. This only represents a selection of the full database of radiocarbon dated iron production sites, to be publised (Hennius et al. in press).</p>
Incidences of community onset severe sepsis, Sepsis-3 sepsis, and bacteremia in Sweden – a prospective population-based study.
<p>Sepsis epidemiology study 2011-2012 Sweden</p> <p>Ljungström, Lars; Andersson, Rune; Jacobsson, Gunnar</p> <p> </p> <p>Data collected during the prospective "Sepsis Skaraborg study" performed 2011-2012 in the western region of Sweden. Adult patients admitted to the emergency department for suspicion of a community-onset sepsis were evaluated. The study was approved by the Regional Ethical Review Board of Gothenburg (376-11). The file includes data for patient characteristics, vital signs, biomarker measurements, cases of bacteremia, and patient classifications using Sepsis-2 and Sepsis-3 criteria.</p>
National Checklists 2017: Sweden Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Sweden collected using effechecka and geonames polygons
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.