Find research datasets worth reusing

opencc-by-4.0Nov 2020View details →

Case study result data set for Energy Economics article "Demystifying market clearing and price setting effects in low-carbon energy systems"

The data set contains country-specific power generation and consumption time series data for the European energy system, including both traditional and new market participants due to cross-sectoral integration. Country codes: ALPHA-3 Unit: Megawatt (electric) (interval average values, i.e. MWh/h) Generation technology types <ul> <li>batteryStorage (Li-Ion)</li> <li>conventionalHydro (aggregated for different equivalent hydropower systems)</li> <li>natural_gas_CC_COND (Combined Cycle Gas Turbine)</li> <li>natural_gas_CC_EXCOND (Combined Cycle Gas Turbine as extraction condensing CHP plant for district heating)</li> <li>natural_gas_GT_COND (Open-Cycle Gas Turbine)</li> <li>natural_gas_GT_EXCOND (Open-Cycle Gas Turbine as extraction condensing CHP plant for industry)</li> <li>offshoreWind (aggregated for different LCOE and IEC wind turbine classes)</li> <li>offshoreWindExplicit (offshore wind generation considered for offshore grid investments in the North Seas area, aggregated for different LCOE classes)</li> <li>onshoreWind (solar PV, aggregated for different LCOE classes)</li> <li>other (geothermal, waste)</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> <li>solar (solar PV, aggregated for different LCOE classes)</li> <li>uran_ST_COND (steam turbine condensing power plant)</li> </ul> Consumption technology types <ul> <li>BEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>PHEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>airConditioning</li> <li>batteryStorage (Li-Ion)</li> <li>conventionalLoad</li> <li>heatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridHeatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridTruck (Hybrid Overhead-Line truck)</li> <li>largeScaleDirectResistiveHeating (Centralised CHP systems)</li> <li>natural_gas_CC_EXCOND_electrodeHeater</li> <li>natural_gas_CC_EXCOND_heatpumpHeater</li> <li>natural_gas_GT_EXCOND_electrodeHeater</li> <li>natural_gas_GT_EXCOND_heatpumpHeater</li> <li>powerToGas</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> </ul>

opencc-by-4.0Nov 2021View details →

Dataset to Study TSO-DSO Coordination Market Models for Flexibility Procurement to Balancing and Congestion Management

The dataset is composed by an interconnected system consisting of the IEEE 14-bus (TN) transmission network connected to three distribution networks: the Matpower systems 18-bus (DN_18), 69-bus (DN_69), and 141-bus (DN_141). All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Base demand is adapted from the case, while base generation profiles are added to all nodes. All distribution systems are balanced, and the transmission system is imbalanced. Thermal limits of the lines are adapted in order to create congestion in the systems. Each distribution system is connected to the transmission system through one line, which has capacity of 1.0. The interconnected system is fully represented in "Network.xlsx", in which: <ul> <li>System: transmission (TN) or distribution (DN_18, DN_69, DN_141);</li> <li>LineID: ID of the lines;</li> <li>BusNumber: number of the nodes within the systems. This parameter is used to define the lines (from/to);</li> <li>BaseDemand and BaseSupply: base active demand and generation of each node;</li> <li>ConnectedDN: distribution system to which the transmission system node is connected to. If blank, the node is not connected to any distribution system. Only for the transmission system;</li> <li>InterfaceCapacity: thermal limit of the interface between the transmission and distribution systems;</li> <li>ThermalLimit: thermal limit of the transmission/distribution systems lines. For distribution systems, a value of 10 indicates that the line has no limit; </li> <li>SFTN: shift factor matrix of the transmission system. Capture the change in the active power flow over a line due to a change in injection or offtake at a node;</li> <li>BaseReactiveDemand and BaseReactiveSupply: base reactive demand and generation at each node. Only for distribution systems;</li> <li>VoltageLB and VoltageUB: lower and upper limits for the magnitude squared of the voltage in each distribution system node. Only for distribution systems;</li> <li>ConnectedTN: identify if the distribution node is connected or not to the transmission system. Only for distribution systems;</li> <li>ResistanceR: resistence of the distribution system lines. Only for distribution systems;</li> <li>ReactanceX: reactance of the distribution system lines. Only for distribution systems.</li> </ul> Flexibility bids are randomly generated in the different nodes. For downward flexibility bids, the prices are drawn from the uniform distribution in the range 10 to 15, and for upward flexibility bids, they are drawn from the range 45 to 50. The bids maximum quantities are generated according to the base demand or supply of the node from which they are connected. A minimum value for the quantity is imposed as 0.01. The generated orderbook is presented in "OrderbookTN" (transmission system) and "OrderbookDN" (distribution systems): <ul> <li>OrderID: the ID of the order, to make each order unique;</li> <li>System: the system (TN, DN_18, DN_69, DN_141) from which the order is offered;</li> <li>BusNumber: the node from which the order is offered;</li> <li>FlexibilitySense: UPWARD for increase in generation or decrease in demand; DOWNWARD for increase in demand or decrease in generation;</li> <li>Price: the submitted order price;</li> <li>Quantity: the maximum quantities of the order.</li> </ul> Source of the systems' topology: [1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010. Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate TSO-DSO coordination market models for the procurement of flexibility. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.

opencc-by-4.0Mar 2021View details →

Case study result data set for Energy Economics (submitted) article "On Wholesale Electricity Prices and Market Values in a Carbon-Neutral Energy System"

The data set contains wholesale power price time series data for Germany and France focussing on price setting effects in a long term low carbon European energy system context (scenario year 2050) generated with the model SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. The single time series are focussing on the price setting effects of different flexible technologies including both traditional and new market participants due to cross-sectoral integration. Unit: Euro/Megawatthour Abbreviations: <ul> <li>BEV - Battery Electric Vehicles</li> <li>GER - Germany</li> <li>FRA - France</li> <li>OCGT - Open Cycle Gas Turbine</li> <li>PHEV - Plug-In Hybrid Vehicles</li> <li>RES - Renewable energy sources (here: wind and solar power)</li> <li>th. - thermal</li> </ul>

opencc-by-4.0Sep 2023View details →

Dataset to Study Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets

We publish the dataset used to study Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets (as part of chapter 7 of deliverable D3.3 of the OneNet project). The dataset is composed by an interconnected system consisting of the IEEE 14-bus (TN) transmission network connected to two distribution networks: the Matpower systems 69-bus (DN_69), and 141-bus (DN_141). All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Injections and loads of the nodes are adapted to create an anticipated  imbalance in the interconnected system, resolved by flexibility. In addition, the lines’ upper limits are adjusted to create anticipated congestion in the networks. The interconnected system is fully represented in "Network_case_A_B_C.xlsx" (upward balancing need) and "Network_case_D.xlsx" (downward balancing need). Upward and downward flexibility bids are randomly generated and allocated to the nodes.  7 bids lists are available in this dataset.  Source of the systems' topology: [1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010. Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.

opencc-by-4.0Oct 2023View details →

W4RES Case studies of women leading RHC market uptake

23 interviews were conducted with women (co-)leading or initiating RHC concepts in 8 different countries (IT, DK, EL, SK, AT, DE, BE, BG) that served as the background material for this dataset. Before conducting the interviews, the W4RES partners were asked to identify case studies based on: • Structure of the organization• Geographical outreach• Visibility and interaction on social media• Financial power• Source of Renewable Energy• Renewable Heating and Cooling conceptsIt moreover entailed a scoring section in which the case studies were ranked according to:• Technical innovation• Social innovation• Transferability• Societal impact• Political impact• Market potential• External communication• Female leadership and influence• Internal support measures for womenW4RES partners interviewed the female leaders and stakeholders in the organizations that ranked highest in the case study identification tables. These women were asked to reflect on the impact of their RHC solutions, the role of women in the organization, the support measures targeting women, as well as potential barriers and needs. The interview material was coded manually and collected in a synthesis matrix. This overview facilitates cross-case comparison and allows to see patterns for barriers and success factors.

opencc-by-4.0Nov 2024View details →

Coding frame and dataset for the study: Land-use governance: The interplay of social, market, and policy drivers – A global systematic review

This file entails the coding frame and dataset used to conduct the systematic literature review "Land-use governance: The interplay of social, market, and policy drivers – A global systematic review". This study was first published as Chapter 2 of the PhD Dissertation "From soil to society - Rethinking governance for multifunctional land use and management" (Elsa L. Dingkuhn, 2025), and in a modified form in the journal Earth System Governance (Dingkuhn et al. 2025). The file consists of three sheets: - Coding frame: Includes coding instructions and definitions used to extract and categorize the data. - Variables: A list of dataset variables with explanations. - List of included studies: The 81 studies from which the data was sourced. - Data_List of observations: The dataset itself, consisting of 718 observations extracted from the included studies.

opencc-by-4.0Jul 2022View details →

Modelling assumptions and input dataset for the case study of the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study"

This data package includes the modelling assumptions and input data to replicate the results of the case study included in the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study". This paper is part of the 18th International Conference on the European Energy Market (EEM22). The case study models the Belgian day-ahead electricity market, in which the existing storage is considered, in addition to large-scale battery energy storage systems of different sizes for varying renewable energy shares. A detailed description of the case study is provided in the readme file.  This supplementary data package includes the following files:      --Belgium Model Input Data.xlsx:  Dataset used as input in the case study of the mentioned paper     --Modelling Assumptions.pdf: Modelling assumptions considered in the case study     --readme.txt (this file): Includes a detailed description of the data package   The data included in this dataset was collected from public open sources [1]-[2]. Please notice that this dataset does not replace the original open access information. For accessing the data, please visit the following websites: [1] “ENTSO-E Transparency Platform.” [Online]. Available: https://transparency.entsoe.eu/dashboard/show. [Accessed: 06-Jul-2022]. [2] “Grid data.” [Online]. Available: https://www.elia.be/en/grid-data. [Accessed: 06-Jul-2022].

openmit-licenseSep 2022View details →

Bigger telcos are not necessarily better for infrastructure: A case study of EU versus US markets

This is data and slides for an anticipated forthcoming publication. Telecommunications companies (telcos) provide infrastructure essential to the delivery of digital content. Further, investment in next-generation communication technologies is also seen as critical to overall competitiveness of a market.  This dataset results from an examination of the case to be made for European telco consolidation, through comparison with both telcos in the more-concentrated US market, and with other corporations involved in the information or ``eye-ball'' value chain. We find that both profits and growth for EU and US telcos are already comparable before investment in infrastructure, and that in line with standard theory, more value is returned to customers in the form of infrastructure investment in the less-concentrated, EU market. Profits are also in line with other companies in the value chain, with the notable exception of the extremely-concentrated digital ad exchanges segment.  The data for the charts was collected from Bloomberg, so we therefore have protected the primary datasheet, available on specific request. No discrepancies with information available from other public sources was identified in respect of the data on revenue. However, companies do not report operating profit (EBIT) and EBITDA systematically in the same manner. We based our calculation on the Bloomberg adjusted EBIT and EBITDA. We thank Benedikt Ströbl for comparing the Bloomberg revenue, EBIT and EBITDA figures with other available sources for all companies in the sample. In particular, the data from Bloomberg was compared to data from Alphaquery and 10-K and annual reports. The below is a non-exhaustive list of the data points for which the Bloomberg adjusted data displayed a delta compared to the data that could be collected from the public sources used for verification, where only some years displayed a delta in the data the year is specified in brackets: (i) in respect of EBIT: Publicis (2018, 2019), NYT (2017) and Axel Springer (2017 and 2019); (ii) in respect of EBITDA (additionally to EBIT list): Verizon, Bertelsmann (2018), Interpublic (2019). Finally, to avoid any confusion in respect of the segment data for Alphabet, the data is presented as retrieved from Bloomberg in full on the tab “Alphabet”, data from the SEC reports used on top of the Bloomberg data to estimate the EBITDA is also reproduced on this tab.

opencc-by-sa-4.0Apr 2024View details →

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

opencc-by-4.0Sep 2024View details →

Delphi Study Data: Rounds 1 and 2 for Enhancing Efficiency and Transparency in Ghana's Rental Housing Market

The dataset includes responses from two rounds of a Delphi study conducted among experts in the real estate sector, policymakers, and government officials in Ghana. The data captures both quantitative and qualitative insights on factors influencing rental housing decisions. <ul> <li>Round 1: Initial ratings of factors such as rental cost, proximity to work, landlord responsiveness, and availability of amenities.</li> <li>Round 2: Pairwise comparisons of the top factors identified in Round 1, used for AHP analysis to determine their relative importance.</li> </ul>

FIG. 11 in Nineteenth-century butchery and transport for a market economy: Plum Grove as a case study for commercial transactions in the Midwestern USA

FIG. 11. – Distal measurements of right cattle metatarsals (following von den Driesch 1976). Elements from immature individuals (un- fused) are here included with those from mature individuals (fused, with fusion taking place between 2-2.5 years of age [Grigson 1982: 22]). The division in size between older, smaller individuals and younger, larger ones may also represent a break between older females and younger males (figure created by Cerisa R. Reynolds).

FIG. 10 in Nineteenth-century butchery and transport for a market economy: Plum Grove as a case study for commercial transactions in the Midwestern USA

FIG. 10. – Each cattle skull from within Plum Grove's Bone Feature exhibits impact fractures indicative of blunt force trauma, indicating that each individual was killed in the same manner. These impact fractures are clearly visible in this photograph of five of the Fea- ture's cattle skulls (photograph courtesy of William E. Whittaker).

FIG. 3 in Nineteenth-century butchery and transport for a market economy: Plum Grove as a case study for commercial transactions in the Midwestern USA

FIG. 3. – Project location in relation the to surrounding topography, highlighting Plum Grove's location within Iowa City as well as Iowa City's location within the state of Iowa. Base map is aerial photo- graph from ISUGISSRF (2011); figure created by Bryan Kendall.

FIG. 1 in Nineteenth-century butchery and transport for a market economy: Plum Grove as a case study for commercial transactions in the Midwestern USA

FIG. 1. – Map of the contiguous U.S., with the main course of the Mississippi River highlighted in a thicker grey line. As the site this paper focuses on is located in Iowa City, Iowa, this city is also identified here as a grey circle (map from United-States-Map.com; modified by Cerisa R. Reynolds).

FIG. 2. – A in Nineteenth-century butchery and transport for a market economy: Plum Grove as a case study for commercial transactions in the Midwestern USA

FIG. 2. – A map of railways across the contiguous United States in 1870, with Iowa City, Iowa, and Chicago, Illinois, highlighted (map from http://www.latinamericanstudies.org/19-century/railroads-1870.jpg; adapted by Cerisa R. Reynolds).

FIG. 6. – A in Nineteenth-century butchery and transport for a market economy: Plum Grove as a case study for commercial transactions in the Midwestern USA

FIG. 6. – A map of the Plum Grove Historic Farm house and the Bone Feature,identifying where unit excavations have been carried out and where testing indicates the extent of the Bone Feature. As is visible here, the Bone Feature is parallel to and runs to the east of the Plum Grove house (figure created by Bryan Kendall).

FIG. 5 in Nineteenth-century butchery and transport for a market economy: Plum Grove as a case study for commercial transactions in the Midwestern USA

FIG. 5. – The northern face of the Plum Grove house, taken in 2010. The Bone Feature discussed throughout this paper was identified roughly 4-5m east (here, to the left) of the house (photography courtesy of William E. Whittaker). Additional photographs of the site as well as digital reconstructions of the property as it would have looked at various times throughout history can be seen at: http://www.uiowa.edu/~plumgrov/index.html.