Find research datasets worth reusing

opencc-by-4.0Dec 2022View details →

Golden Agents - Processes of Creativity (case study)

We can observe a 'rise' of or 'renewed interest' in the illustrated book in the second half of 17th century and the beginning of the 18th century. New genres and techniques and an increasing competition in the book market lead to new collaborations between authors, printers, and illustrators. It would be interesting to track these producers who collaborated on illustrated book projects and trace whether these commercial collaborations were grounded or resulted in social and/or religious interactions. For this, we are bringing together several datasets in the Golden Agents infrastructure that each contain information on the production of books, or on social relations between actors involved in the production process. Aim of case study: <ul> <li>Linking & querying different datasets to find overarching patterns on creative industries in the Dutch Republic</li> </ul> Overarching Research Question: <ul> <li>How can we map innovation in cultural production?</li> <li>What is the relation between different sorts (bonding & bridging) of creative and social interactions?</li> </ul> This release (v0.9) marks the state of the data and case study at the end of the Golden Agents project (2016-2022).

opencc-by-4.0Jan 2023View details →

Processed cadastre map for Aarhus municipality 2018

Cadastre map for Aarhus municipality The whole of Denmark is divided into cadastres, which represent legally binding property boundaries. The 2018 cadastre map comprises a total of 81,437 cadastre units within Aarhus municipality. The cadastre map contains some general land use information, such as whether a cadastre unit is part of the road or rail network and if it is primarily used for agricultural production. Furthermore, each cadastre unit contains a unique identification string, which can be linked to building specific information from the Danish Building and Dwelling (BBR) register and to ownership information from the Central Business (CVR) register. Based on this information, for the 2018 cadastre, we compiled information on ownership categories, building specific information (e.g. use, such as residential, business etc.) and whether a cadastre unit is publicly accessible. For some of the information, the cadastre map was overlaid with other spatially explicit datasets. The different columns in the compiled cadastre map are described in the table below Information contained in the 2018 cadastre map for Aarhus municipality <table> <tbody> <tr> <td> Column Name </td> <td> Description </td> <td> Unit </td> <td> Comment </td> </tr> <tr> <td> parcel_id </td> <td> Unique ID for cadastre parcel </td> <td> Number </td> <td> </td> </tr> <tr> <td> cvr_name </td> <td> Owner name from CVR (central business) register </td> <td> Text </td> <td> </td> </tr> <tr> <td> road_rail_code </td> <td> Code depicting if a cadastre is designated as road or railway line: 0 = Other; 1 = road; 2 = rail </td> <td> Number </td> <td> Based on information contained in the cadastre dataset </td> </tr> <tr> <td> road_rail_name </td> <td> Name for cadastres designated as roads or railways </td> <td> Text </td> <td> </td> </tr> <tr> <td> code_BBR </td> <td> Code depicting use of buildings from BBR (building and dwelling register) </td> <td> Number </td> <td> In case of more than one building use, the cadastre is assigned the building use with the largest area within the cadastre </td> </tr> <tr> <td> name_BBR </td> <td> Name for use of buildings from BBR (building and dwelling register) </td> <td> Text </td> <td> </td> </tr> <tr> <td> built_year </td> <td> Building year of oldest existing building on the cadastre </td> <td> Year </td> <td> </td> </tr> <tr> <td> ownership_code </td> <td> Code for type of ownership </td> <td> Number </td> <td> Based on re-classification of owner name from CVR-register </td> </tr> <tr> <td> ownership_name </td> <td> Name for type of ownership </td> <td> Text </td> <td> </td> </tr> <tr> <td> ownership_agg_code </td> <td> Code for aggregated type of ownership </td> <td> Number </td> <td> Based on re-classification of owner name from CVR-register </td> </tr> <tr> <td> ownership_agg_name </td> <td> Name for aggregated type of ownership </td> <td> Text </td> <td> </td> </tr> </tbody> </table>       <table> <tbody> <tr> <td> public_access_code </td> <td> Code depicting if a cadastre is publicly accessible: 0 = No public access; 1 = public access; 2 = road </td> <td> Number </td> <td> cadastres depicted as publicly accessible include: all publicly owned areas, unless they are farmed; common areas owned by private owner/housing associations; common areas owned by cooperative housing associations; private forests; privately owned nature areas, if these are not fenced </td> </tr> <tr> <td> public_access_name </td> <td> Name for public accessibility </td> <td> Text </td> <td> </td> </tr> <tr> <td> build_code_01 </td> <td> Code for building use for 1st aggregation level </td> <td> Number </td> <td> Based on aggregation of use categories from BBR (building and dwelling register) </td> </tr> <tr> <td> build_name_01 </td> <td> Name for building use for 1st aggregation level </td> <td> Text </td> <td> </td> </tr> <tr> <td> build_code_02 </td> <td> Code for building use for 2nd aggregation level </td> <td> Number </td> <td> </td> </tr> <tr> <td> build_name_02 </td> <td> Name for building use for 2nd aggregation level </td> <td> Text </td> <td> </td> </tr> <tr> <td> build_code_03 </td> <td> Code for building use for 3rd aggregation level </td> <td> Number </td> <td> </td> </tr> <tr> <td> build_name_03 </td> <td> Name for building use for 3rd aggregation level </td> <td> Text </td> <td> </td> </tr> <tr> <td> public_access_greenspace_type_code </td> <td> Code for type of publicly accessible green space: 0 = No publicly accessible green-blue space; 1 = Institution; 2 = Common area; 3 = Cemetery; 4 = Sport/leisure; 5 = Other green-blue space </td> <td> Number </td> <td> Based on combination of ownership, building type and public accessibility </td> </tr> <tr> <td> public_access_greenspace_type_name </td> <td> Name for type of publicly accessible green space </td> <td> Text </td> <td> </td> </tr> <tr> <td> golf_course_code </td> <td> Code depicting if a cadastre is used as golf course: 0 = ND; 1 = golf course </td> <td> Number </td> <td> Based on owner name form CVR register </td> </tr> <tr> <td> golf_course_name </td> <td> Name for golf course </td> <td> Text </td> <td> </td> </tr> <tr> <td> riding_school_horse_racing_track_code </td> <td> Code depicting if a cadastre is used as riding school or horse racing track: 0 = ND; 1 = riding school/horse racing trach </td> <td> Number </td> <td> Based on owner name form CVR register </td> </tr> <tr> <td> riding_school_horse_racing_track_name </td> <td> Name for riding school/horse racing track </td> <td> Text </td> <td> </td> </tr> </tbody> </table>       <table> <tbody> <tr> <td> primary_school_code </td> <td> Code depicting if the cadastre belongs to a primary school: 0 = no primary school; 1 = primary school </td> <td> Number </td> <td> Based on overlay with feature point dataset from Aarhus municipality </td> </tr> <tr> <td> primary_school_type_code </td> <td> Code depicting school type: 0 = No primary school; 1 = public school; 2 = public special school; 3 = private school; 4 = private continuation school </td> <td> Number </td> <td> Based on overlay with feature point dataset from Aarhus municipality </td> </tr> <tr> <td> primary_school_type_name </td> <td> Name of school type </td> <td> Text </td> <td> </td> </tr> <tr> <td> primary_school_name </td> <td> Name of school </td> <td> Text </td> <td> Based on overlay with feature point dataset from Aarhus municipality </td> </tr> <tr> <td> primary_school_count_pupils </td> <td> Number of pupils attending school (2022) </td> <td> Number </td> <td> Based on school webpages </td> </tr> </tbody> </table>

opencc-by-4.0Jan 2023View details →

Dataset used in the publication entitled "Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids" presented at 2022 20th International Conference on Harmonics and Quality of Power (ICHQP)

Dataset obtained from experimental research carried out in a real power grid. Based on the dataset, the problem of decomposition in identification of sources of voltage fluctuations has been presented in the publication: Kuwałek P., Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids, Proceedings of the 20th International Conference on Harmonics and Quality of Power, IEEE , art. no. 43, 2022, Italy, Naples. The description of the power grid model is presented in this publication. The research results are part of the work under the project entitled "Voltage fluctuation diagnostic focused on identification and localization disturbing loads in power grids" funded by the National Science Centre, Poland - 2021/41/N/ST7/00397.

opencc-by-4.0Feb 2023View details →

Post-processed data and graphical tools for a CONUS-wide eddy flux evapotranspiration dataset

Post-processed data and graphical tools for a CONUS-wide eddy flux evapotranspiration dataset   We curated a dataset of post-processed in situ evapotranspiration (ET) measurements, primarily from eddy covariance flux towers, from stations located within the contiguous United States. The dataset includes daily and monthly aggregated ET, energy balance metrics, and micrometeorological data that were post-processed from 148 flux towers, 4 weighing lysimters, and 8 Bowen Ration stations. Original data was retrieved from the <a href="https://ameriflux.lbl.gov/">AmeriFlux</a> network and other networks and partners. The dataset is oriented towards ET and includes both ET that has been corrected for energy balance closure error as well as the uncorrected values. Energy balance components (latent and sensble heat flux, soil heat flux, and net radiation) were subject to limited gap-filling and latent energy (ET) was subject to additional visual quality control. Other meteorological measurements such as air temperature, precipitation, humidity, etc. are included for most stations depending on availability, and some additional variables were calculated. Interactive graphics of most post-processed data are also included. The dataset has many potential uses including evaluation of regional hydrologic and atmospheric models, energy balance analysis, and more. Description of the Data and file structure The dataset is in a compressed (zipped) archive titled "flux_ET_dataset", so first it needs to be downloaded and extracted. Once extracted there are four major components within:  1. A collection of time series files with daily aggregated data (one for each station),  these are in the directory named "daily_data_files" and are in CSV format. 2. A similar collection of time series files for monthly aggregated data in "monthly_data_files".  3. Interactive graphic files (HTML format) for each station which are in the "graphical_files" directory.  4. Two additional tables in the root directory, including a metadata file named "station_metadata.xlsx" with site information such as site ID, coordinates, land cover type, principal investigator information, etc. The other table named "variable_explanation.xlsx" lists all variables that were post-processed in the flux dataset and gives a short description of each as well as their units.  Each data and plot file starts with the station's ID or site ID which are listed in the station_metadata.xlsx file.  Here is a visual of the file structure: <blockquote> flux_ET_dataset │   README.md │   variable_explanation.xlsx │   station_metadata.xlsx │ └───daily_data_files │   │   [site ID]_daily_data.csv │   │   ... └───monthly_data_files │   │   [site ID]_monthly_data.csv │   │   ... └───graphical_files │   │   [site ID]_plots.html │   │   ... ``` </blockquote> The variable names in the daily and monthly data files as well as the graphics all follow the same naming scheme which are defined in the variable_explanation.xlsx file. For example, LE stands for latent energy flux and is in units of W/m2.  Sharing/access Information Currently, this repository is the only location where the data are hosted. Original data, prior to post-processing, were retrieved from multiple providers listed below: * AmeriFlux network (https://ameriflux.lbl.gov/)  * California State University, Monterey Bay, Seaside, CA, USA  * Desert Research Institute, Reno, NV, USA  * gridMET, Northwest Knowledge Network at the University of Idaho (https://thredds.northwestknowledge.net/)  * United States Geological Survey Nevada Water Science Center, Carson City, NV, USA  * Delta-Flux network, Arkansas, Louisiana, MS, USA  * United States Department of Agriculture Agricultural Research Service (USDS-ARS):      * Sustainable Water Management Research Unit, Stoneville, MS, USA      * US Salinity Laboratory, Agricultural Water Efficiency and Salinity Research Unit, Riverside, CA, USA      * Conservation & Production Research Laboratory, Bushland, TX, USA      * US Arid-Land Agricultural Research Center, Maricopa, AZ, USA      * Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA  Further contact information for each station as well as DOI's for original AmeriFlux data are included in the "station_metadata.xlsx" file.  Code/Software All files that comprise this dataset were generated using the "flux-data-qaqc" open-source Python package version 0.1.6. The package is hosted on <a href="https://github.com/Open-ET/flux-data-qaqc">GitHub</a> and <a href="https://pypi.org/project/fluxdataqaqc/">PyPI</a>, it also has <a href="https://flux-data-qaqc.readthedocs.io/en/latest/">online documentation</a> including an in depth user tutorial.

opencc-by-4.0Oct 2022View details →

Polar / Plasma Waves Investigation processed dataset and ephemeris used to produced the Smith et al. (2022) catalogue (doi:10.5281/zenodo.7260994 )

This data set contains Polar / Plasma Waves Investigation processed using the SPACE Labelling Tool (Louis et al., 2022, doi:10.5281/zenodo.6886528). It also contains the Polar ephemeris in the geocentric solar ecliptic (GEO) coordinate system (from https://sscweb.gsfc.nasa.gov/cgi-bin/Locator.cgi) This processed dataset contains Auroral Kilometric Radiation (AKR) observations and was used to produced the Smith et al. (2022) catalogue of AKR (doi:10.5281/zenodo.7260994) This work has been funded by Science Foundation Ireland Grant 18/FRL/6199, and by a 2022 SCOSTEP/PRESTO Grant.

opencc-by-4.0Feb 2023View details →

Literature Datasets for the publication "Systematic Review: Prevalence and Practices of Immunofluorescent Cell Image Processing"

This dataset contains the CSV files returned from PubMed searches used to complete a Systematic Review of Image Processing Publication Practices for methods applied to immunofluorescent images of all CNS cells.  The file names are organized "date_supplementarytablenumber" followed by the appropriate search terms.

Dataset - Analytic Network Process in economics, finance and management

This data set presents the recent use of the Analytic Network Process (ANP) in the decision process in the areas of economics, finance and management. It has 434 ANP studies for a 10-year period (2012-2021) within the Scopus database. They were identified using the keyword “Analytic Network Process” in articles indexed in the following two database categories: "Business, Management and Accounting"; and "Economics, Econometrics and Finance".

Integration of the Ash-Based Treatment of the Anerobic Digestate in a Wider Valorization Process by Aspen Plus® Simulation

Spreadsheets for the 7 scenarios investigated in the article: "Aspen Plus® process simulation model of the biomass ash-based treatment of anaerobic digestate for production of fertilizer and upgradation of biogas". A comparison of the following 7 process strategies is mentioned in the above article: Case 1 is the foundation case (labelled as untreated MD) that served as benchmark and it implied the production of MD using the original PSM of Rajendran et al. [11]. In Case 2 a stream of pure hydrochloric acid was incorporated at a 0.1000 times the flowrate of the MD towards the ionization reactor (1HCl:10MD). In Case 3 a stream of hydrochloric acid was incorpo-rated at 0.1176 times the flowrate of the MD towards the stoichiometric-equilibria reactor (1HCl:8.5MD). In Case 4 a stream of hydrochloric acid was incorporated at 0.1212 times the flowrate of the MD towards the ionization reactor (1HCl:8.25MD). The remaining 3 cases are built on Case 3 (i.e. considering the previous acidification of the MD with the dose of 3.18 mEq HCl/g). In Case 5 the stream of SSA (Table 1) was incorpo-rated at 0.0040 times the flowrate of the MD towards the stoichiometric-equilibria reactor (1SSA:1.76HCl:15MD). In Case 6 the stream of ash (Table 1) was incorporated at 0.0060 times the flowrate of the MD towards the ionization reactor (1SSA:1.18HCl:10MD). In Case 7 the stream of ash was incorporated at 0.0080 times the flowrate of the DM to the stoi-chiometric-equilibria reactor (1SSA:0.88HCl:7.5MD).  Additionally, MS Word file summarizes the most relevant data: Table S1, Mass balance of the nutrients monitored in the Aspen Plus® simulations. (1/3); Table S2, Mass balance of the nutrients monitored in the Aspen Plus® simulations. (2/3); Table S3, Mass balance of the nutrients monitored in the Aspen Plus® simulations. (3/3); Table S4, List of the components in the PSM of Rajendran et al. [11]. (1/3); Table S5, List of the components in the PSM of Rajendran et al. [11]. (2/3); Table S6, List of the components in the PSM of Rajendran et al. [11]. (3/3).

Processed proteomic and phosphoproteomic timeseries from Ostreococcus tauri, with Gene Ontology enrichment, from "A phospho-dawn of protein modification anticipates light onset in the picoeukaryote O. tauri"

Diel regulation of protein levels and protein modification had been less studied than transcript rhythms. These data tables in .XLSX format report partial proteome (Table_S1) and phosphoproteome data (Table_S2), assayed using shotgun mass-spectrometry, from cultures of the alga Ostreococcus tauri under light-dark cycles, sampled at Zeitgeber times (ZT, hours) 0, 4, 8, 12, 16 and 20. 10% of quantified proteins but two-thirds of phosphoproteins were rhythmic. Gene Ontology enrichment analysis was applied to infer the functional enrichment of the proteins or phosphoproteins, grouped by their loadings in PCA analysis (Table_S3), by hierarchical clustering (Table_S4) or by the peak time of their rhythmic profile (Table_S5).Prompted by night-peaking and apparently dark-stable proteins, we also tested the proteome of cultures transferred to prolonged darkness for 24, 48, 72 or 96h (Table_S6), where the proteome changed less than under the diel cycle. The raw data are available from ProteomeXchange, with identifiers PXD001734, PXD001735 and PXD002909.

opencc-by-4.0Aug 2022View details →

Processed data for "Model identification of neural encoding (MINE)" publication

This dataset contains mouse and zebrafish data processed by MINE. These datafiles were used to generate the publication figures for the mouse cortical dataset [musall.hdf5] (Figure 5) and the zebrafish whole-brain [main_analysis.hdf5] (Figures 6 and 7) and reticulospinal datasets [rs_analysis.hdf5] (Figure 6).   Musall.hdf5 contains reordered data from "Musall, S., Kaufman, M.T., Juavinett, A.L. et al. Single-trial neural dynamics are dominated by richly varied movements. Nat Neurosci 22, 1677–1686 (2019)." The contents of each dataset are described in DataContent_xxx.pdf

Vibration Sensor and Process Data from Ferrosilicon Production

Elkem Facility The facility specializes in producing ferrosilicon (FeSi) and ferrosilicon magnesium (FSM) master alloys. Elkem Bjølvefossen is among the world’s largest producers of FSM. Three reduction furnaces deliver the base metal which is then alloyed and refined to the right quality of FeSi or FSM. These alloys are important additives in the manufacturing of steel products. Silicon in the form of FeSi is used to remove oxygen from the steel and as an alloying element to improve the final quality of the steel. Silicon increases strength and wear resistance, elasticity, i.e., spring steels, scale resistance, and heat resistant steels and lowers electrical conductivity and magnetostriction. After tapping and refining, the ferro-alloys are crushed to grains ranging from 1 mm to 25 mm in size. Consumers of FeSi and FSM have strict requirements for particle size, related mainly to the chemical kinetics of their refining and alloying processes. For this reason, the crushed material is separated in sieves and packaged by particle size before shipment. Two lattice gratings inside the Mogensen shaker separate the material according to required particle size.  The subject of the present study is a mechanical shaker platform containing one or more such sieves. The shaker is a <a href="https://www.mogensen.se/">Mogensen S0556</a>  that was installed in 1996 in Bjølvefossen and is no longer produced in this type. This device is powered by two counter-rotating 1.2-horsepower AC motors operating at 960 RPM. Together with the spring suspension, these cause an elliptical motion that both transports and scatters the incoming material across the sieve. The shaker is engineered so that the motion transitions from a slanted ellipse at the in-feed to nearly linear at the output. Vibration Data Vibration data from two sensors. Each sensor measures acceleration in three axes with three different ADCs.  ERP and MES data Manufacturing Execution System (MES) data as well as process data from the Enterprise Resource Planning (ERP) is given. It is providing information about the material that is currently being produced as well as the data from the scales from the material packing station where the bags with completed production were packed. MES data has a resolution of 5 seconds and the process data from ERP has a resolution of roughly 10 minutes. This operational data is meant to provide insight into the current state and throughput of the facility and will serve as labels for the correlation analysis with the vibration data.

opencc-by-4.0Nov 2021View details →

dMRI data from the PRIME-DE processed by mrHARDIflow

This dataset contains processed diffusion MRI data of primate subjects, DTI[1], DIAMOND[2] and fODF[3] reconstruction and metrics. Subjects included in the dataset come from 3 sites of the PRIME-DE[4] database, specifically Aix-Marseille, UC-Davis and Mount Sinai - Philips. The data was processed using mrHARDIflow[5], a pipeline tailored to process high resolution diffusion data. Please, cite the paper, as well as the PRIME-DE paper, if you use any data available here in your research. <ol> <li>LeBihan, D., Mangin, J.-F., Poupon, C., Clark, C. A., Pappata, S., Molko, N., et al. (2001). Diffusion tensor imaging: Concepts and applications. Journal of Magnetic Resonance Imaging 13, 534–546. doi:<a href="http://doi.org/10.1002/jmri.1076">10.1002/jmri.1076</a></li> <li>Scherrer, B., Schwartzman, A., Taquet, M., Sahin, M., Prabhu, S. P., and Warfield, S. K. (2016). Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (diamond). Magnetic Resonance in Medicine 76, 963–977. doi:<a href="http://doi.org/10.1002/mrm.25912">10.1002/mrm.25912</a></li> <li>Tournier, J.-D., Calamante, F., and Connelly, A. (2007). Robust determination of the fibre orientation distribution in diffusion mri: Non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35, 1459–1472. doi:<a href="http://doi.org/10.1016/j.neuroimage.2007.02.016">10.1016/j.neuroimage.2007.02.016</a></li> <li>Milham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., et al. (2018). An open resource for non-human primate imaging. Neuron 100, 61–74. doi:<a href="http://doi.org/10.1016/j.neuron.2018.08.039">10.1016/j.neuron.2018.08.039</a></li> <li> Valcourt Caron, A., Shmuel, A., Hao, Z., and Descoteaux, M. (2021). mrHARDIflow : A pipeline tailored for the preprocessing and analysis of Multi-Resolution High Angular diffusion MRI and its application to a variability study of the PRIME-DE database. bioRxiv. doi:<a href="http://doi.org/10.1101/2021.11.22.469616">10.1101/2021.11.22.469616 </a> </li> </ol>

Processed data to regenerate figures in Noecker et al, "Systems biology elucidates the distinctive metabolic niche filled by the human gut microbe Eggerthella lenta"

This archive contains the processed source data for the publication by Noecker et al, "Systems biology elucidates the distinctive metabolic niche filled by the human gut microbe Eggerthella lenta" (2023, in review). Data tables underlying each figure panel are included, except for the following panels: <ul> <li>Figure S1A: Source data is in Table S1 of the publication</li> <li>Figure 6D: Source data can be found at NCBI GEO accession GSE212420 (supplementary counts data matrix)</li> </ul> Raw metabolomics data can also be found at Metabolomics Workbench accession PR001620. Methods used to summarize these data and generate the figures are described in the manuscript Materials and Methods and figure captions. Code to generate all figures is also available at www.github.com/turnbaughlab/2022_Noecker_ElentaMetabolism and 10.5281/zenodo.7779454.

Dataset for the article "Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes"

This dataset include the data needed to create the socio-hydrological model to simulate human evacuation processes via a transportation network before a flood hits the residential area. Source code, in JAVA, for generating households in the agent-based model are also provided.

Process Simulation of Friction Extrusion of Aluminum alloys

The friction extrusion process (FEP) is a solid-state material processing technique in which a translating extrusion die is pressed against a billet/feedstock material in a rotating extrusion container to produce an extruded rod or wire. A key aspect of FEP is the non-equilibrium thermo-mechanical condition created at the contact region between the die and billet, leading to a possibly improved microstructure. Numerical simulations of FEP are highly complex due to contact between the tool and the workpiece, and interplay between thermo-mechanical conditions and the present severe plastic deformation. In the present work, a three-dimensional finite element model is developed to study the material flow behavior for different extrusion ratios for a 60° die angle during friction extrusion. The developed model is numerically validated against experimental data. The spatial temperature, strain and velocity distributions illustrate the effect of extrusion ratio on the deformation characteristics of the extruded aluminum alloys, thereby assisting in experimental microstructure interpretation.

Openness process in Valladolid city

Nature-Based Solutions require planning approaches and governance architectures that support accessibility to green spaces, while maintaining their quality for the provision of ecosystem services. Urban environmental problems are often difficult to handle and successful solutions require combined efforts of different scientific disciplines but also an active dialogue between stakeholders from policy and society (Lemos and Morehouse, 2005). In this context, transdisciplinary approaches for knowledge co-production provide insights about the ways and the rationale for engaging with multiple knowledge holders: experts and scientists as well as citizens and practitioners (Bergmann et al., 2012, Jahn et al., 2012). The scientific frameworks of urban ecosystem services were brought into the interface between policy and science to inform urban planning and governance (Frantzeskaki and Tilie, 2014). The quality of the URBAN GreenUP project implementation depends on social learning and adequate technical solutions. This is possible through the support and cooperation between the involved parties and the resulting input of knowledge (Luyet, 2012). Participation is often reduced to the dissemination of information and the holding of workshops. These approaches generally do not take into account either the heterogeneity of stakeholders, or the complexity of the decision making process (Luyet, 2012). The KPI “Openness of participatory processes” is based on the participation actions delivered in the city of Valladolid. There are defined two steps, data collection and data evaluation.

Raw and post-processed data for the study of prosodic cues to word boundaries in a segmentation task using reverse correlation

The current dataset provides all the stimuli (folder ../01-Stimuli/), raw data (folder ../02-Raw-data/) and post-processed data (../03-Post-proc-data/) used in a prosody reverse correlation study with the title "prosodic cues to word boundaries in a segmentation task using reverse correlation" by the same authors. The listening experiment was implemented using one-interval trials with target words of the structure l'aX (option 1) and la'X (option 2). The experiment was designed and implemented using the <a href="https://github.com/aosses-tue/fastACI">fastACI toolbox</a> under the name 'segmentation'. A between-subject design was used with a total of 47 participants, who evaluated one of five conditions, LAMI (N=16), LAPEL (N=18), LACROCH (N=5), LALARM (N=5), and LAMI_SHIFTED (N=3). More details are given in the related publication (to be submitted to JASA-EL in May 2023).

ALO Meteor Radar Processed Data in Jan 2022

Meteor radar data include: Horizontal wind (vel) and detected meteors (met).  Both original format and MatLab format (mat) files are included.  Wind data are in three temporal resolutions, 1-hr, 30-hr, and 15-min.

Raw and post-processing data for using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox

Description: The current dataset provides all the stimuli (folder ../01-Stimuli/), raw data (folder ../02-Raw-data/) and post-processed data (../03-Post-proc-data/) used in the Forum Acusticum 2013 paper titled "Using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox" by the same authors. In this paper, we replicated the tone-in-noise experiment by Ahumada et al. (1975) but using an artificial listener instead of collecting data from real participants. The behavioural data were mimicked using an artificial listener based on 'king2019' (King et al., 2019) as a front-end model using a template-matching decision to indicate whether a 500-Hz tone was (or not) present in each of the noisy trials. This study offers a step-by-step guide of how can be an artificial listener integrated into fastACI. Use these data: Download all these data, locate them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="https://github.com/aosses-tue/fastACI">https://github.com/aosses-tue/fastACI</a>) you can recreate the figures of our paper. After downloading and initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB), run the script g20230501_FA_Artificial_listener_paper_figs.m (provided in this dataset) and follow the instructions on the screen to generate one of the four study figures. This script calls the function publ_osses2023b_FA_figs.m from the toolbox.

Galaxy Training Material for Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)

This dataset contains the training data for the Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms) GTN tutorial. It includes 3 GC-[EI+]-HRMS files from seminal plasma samples, the RECETOX Metabolome HR-[EI+]-MS library collected from mostly endogoenous compounds from MetaSci Human Metabolite Library, reference alkanes, sample metadata table, and preprocessed XCMS object.