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54 results for “Design tools”
Systematic Evaluation and Usability Analysis of Formal Tools for Railway System Design - Technical Annexes
<p>This package includes additional data for the paper ``Systematic Evaluation and Usability Analysis of Formal Methods Tools for Railway Signalig System Design'', by Alessio Ferrari, Franco Mazzanti, Davide Basile, and Maurice ter Beek, CNR-ISTI, Italy, accepted for publication in the IEEE Transactions on Software Engineering, DOI: 10.1109/TSE.2021.3124677</p> <p>The paper concerns the systematic evaluation and usability analysis of 14 formal tools for system design, namely CADP (2020-g), FDR4(4.2.7), NuSMV(1.1.1), ProB(1.9.3), Atelier B (4.5.1), Simulink (R2020a), SPIN (6.4.9), UMC (4.8), UPPAAL (4.1.4), mCLR2 (202006.0), SAL (3.3), TLA+ (2) and CPN Tools (4.0). The current package includes the following content:</p> <ol> <li>Tool Evaluation Template and .pdf: a document including the reference evaluation template, and the evaluation sheet of each tool. </li> <li>Tool Evaluation Table.xlsx: a table summarizing the results of the evaluation.</li> <li>System Usability Test - SUS Results.xlsx: an excel file with multiple sheets with all the raw results of the usability test for the tools.</li> </ol>
A tool to support FastFlow program design
<p>We describe the implementation of ff-RPL, a shell to support structured parallel programming development in FastFlow. The shell provides ways to explore the space of functionally equivalent, alternative parallel implementations of the same application with different non functional properties. The tool is entirely written in C++ and has been designed in such a way it can be easily extended to take into account new non functional features, refactoring and optimization rules, as well as different parallel patterns. Preliminary experimental results are shown relatively to the code generation part.</p>
PDEStrIAn: A phosphodiesterase structure and ligand interaction annotated database as a tool for structure-based drug design
<p>A systematic analysis is presented of the 220 phosphodiesterase (PDE) catalytic domain crystal structures present in the Protein Data Bank (PDB) with a focus on PDE-ligand interactions. The consistent structural alignment of 57 PDE ligand binding site residues enables the systematic analysis of PDE-ligand Interaction FingerPrints (IFPs), the identification of subtype-specific PDE-ligand interaction features, and the classification of ligands according to their binding modes. We illustrate how systematic mining of this phosphodiesterase structure and ligand interaction annotated (PDEStrIAn) database provides new insights into how conserved and selective PDE interaction hot spots can accommodate the large diversity of chemical scaffolds in PDE ligands. A substructure analysis of the co-crystalized PDE ligands in combination with those in the ChEMBL database provides a toolbox for scaffold hopping and ligand design. These analyses lead to an improved understanding of the structural requirements of PDE binding that will be useful in future drug discovery studies.</p>
Designing With: AI, ML and DV - Interactive Framework Tools Dataset
<p>This dataset presents a compilation of 182 AI tools, specifically designed for incorporation into design education. The collection was meticulously assembled through a process of rigorous mining of existing internet repositories and academic literature, resulting in a diverse array of AI tools that are applicable to a multitude of design methodologies.</p>
Research compendium for 'The contribution of integrated 3D model analysis to Protoaurignacian stone tool design'
<p><strong>Abstract:</strong> Protoaurignacian foragers relied heavily on the production and use of bladelets. Techno-typological studies of these implements have provided insights into important aspects of cultural variability. However, new technologies have seldom been used to quantify patterns of stone tool design. Taking advantage of a new scanning protocol and open-source software, we conduct the first 3D analysis of a Protoaurignacian assemblage, focusing on the selection and modification of blades and bladelets. We study a large sample of complete blanks and retouched tools from the early Protoaurignacian assemblage at Fumane Cave in northeastern Italy. Our main goal is to validate and refine previous techno-typological considerations employing a 3D geometric morphometrics approach complemented by 2D analysis of cross-section outlines and computations of retouch angle. The encouraging results show the merits of the proposed integrated approach and confirm that bladelets were the main focus of stone knapping at the site. Among modified bladelets, various retouching techniques were applied to achieve specific shape objectives. We suggest that the variability observed among retouched bladelets relates to the design of multi-part artifacts that need to be further explored via renewed experimental and functional studies.</p> <p><strong>Overview of contents:</strong></p> <p>01. AGMT3-D project of the first dataset used in the study;</p> <p>02. AGMT3-D project of the second dataset used in the study;</p> <p>03. Raw outline data of the middle cross-section. Each specimens has a dedicate .txt file;</p> <p>04. Raw outline data of the upper cross-section. Each specimens has a dedicate .txt file;</p> <p>05. Angles3-D project with all files generated by the software (.mat and .xlsx formats) to quantify the mean retouch angle of retouched bladelets;</p> <p>06. R project and scripts for the 1) 2D shape analysis of the middle and upper cross-sections of retouched bladelets and the 2) design of all bivariate plots and boxplots with jittered points used in the paper. All related datasets, principal components, and generated figures are included in the folder;</p> <p>07. Folder with all figures published in the paper and in its supplementary materials;</p> <p>08. Dataset of the first study in .csv with all attributes used to run the statistical analysis presented in the paper;</p> <p>09. Dataset of the second study in .csv with all attributes used to run the statistical analysis presented in the paper;</p> <p>10. Dataset for the study of the upper cross-sections in .csv with all attributes used to run the statistical analysis presented in the paper;</p> <p>11. Dataset for the study of the middle cross-sections in .csv with all attributes used to run the statistical analysis presented in the paper;</p> <p>12. Dataset in .csv used to study the mean retouch angles;</p> <p>13. Supplementary information file in .pdf with all supplementary figures and tables.</p> <p><strong>Extra</strong>: All 3D meshes of blades and bladelets are available on Zenodo following this link: https://doi.org/10.5281/zenodo.6362150.</p>
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).
Figure 2: The design tool architecture
<p>Taking in consideration the two important aspects considered above, we de¯ne<br> the architecture of the design tool (Figure. 2). We observe that there is a<br> database where we store the instances of each diagram and the details of the<br> di®erent users that, according to the metaphor, can participate in the de¯nition<br> of a speci¯c model (Storyboard Artist, Designer, Pedagogic people, Technical<br> users). The engine of the design tool accesses to the database using a database<br> access object layer (DAO). The DAO has the task to abstract and encapsulates<br> all accesses to the data source; it manages the connection with the data source<br> to obtain and store data. The output of the design will be an XML ¯le: the<br> xml ¯le will be the input for a CVE engine that will automatically generate<br> the collaboration virtual environment. It is important to note that the CVE<br> engine may use a set of template useful for the designer. The editor must<br> present to the user these templates in order to help him in its design task.</p>
Supplementary Material: Comparing Formal Tools for System Design: a Case Study from the Railway Domain
<p>The package includes a set of models for a railway moving-block system:</p> <p>(a) a PDF document named Moving-block Model and Requirements.pdf, which includes a UML model of a moving-block system together with a set of requirements for the system;</p> <p>(b) a set of 10 folders, each one associated to a formal or semi-formal development tool. Each folder contains one or more model of the moving-block system from (a), developed by means of the tool.</p>
Doctoral Thesis Artifact "User-Centered Tool Design for Data-Flow Analysis"
<p>This artifact contains the evaluation data and source code accompanying the doctoral thesis "User-Centered Tool Design for Data-Flow Analysis" by Lisa Nguyen Quang Do. The artifact contains (1) the survey questions and anonymized answers of the surveys conducted during the thesis, (2) the user study questionnaires, results, and test applications of the user studies conducted for the thesis, (3) the source code of the research prototypes and video demonstrations of their interfaces, and (4) the benchmark suites used for the empirical evaluation of those prototypes.</p>
Dataset supporting publication: "Geofit: Experimental Investigations and Numerical Validation of Shallow Spiral Collectors as a Basis for Development of a Design Tool for Geothermal Retrofitting of Existing Buildings"
<p>Dataset supporting publication: “Geofit: Experimental Investigations and Numerical Validation of Shallow Spiral Collectors as a Basis for Development of a Design Tool for Geothermal Retrofitting of Existing Buildings” (publication available in <a href="https://zenodo.org/record/7273966#.Y2JgBnbMJPY">GEOFIT Zenodo</a>)</p> <p>The H2020 GEOFIT (grant no. 792210) project will implement and demonstrate easy-to-install and economical geothermal systems in combination with heat pumps for energy-efficient building retrofits at five pilot sites across Europe - a historic building (ITA), a school (ESP), an indoor swimming pool (IRL), an office building (FRA) and a single-family house (IRL) (GEOFIT,2018). Heat pump tests and experimental laboratory tests with shallow geothermal heat collector types are carried out in climate chambers at the AIT. Material data of different soil types are determined in the thermophysics laboratory. Furthermore, CFD simulations of the conducted experiments are calculated with ANSYS Fluent. All this provides data and know-how for the development of a design tool for ground collector configurations such as helices and slinky loops, which are particularly relevant for building retrofits in GEOFIT. Experimental work focused on near-surface spiral geothermal heat exchanger configurations that can be installed at a maximum depth of five metres. Real-scale experiments were carried out for vertically oriented spiral collectors (helix) in real soil. One objective was to develop a measurement concept in the laboratory environment to create the framework for a reliable database. This database is used as a basis for the further development or new development of engineering design tools. Distributed resistance temperature sensors and a fibre-optic temperature measurement system (DTS) were used. The moisture content of the soil was recorded using soil moisture sensors. A heat flow was conditioned by means of a helix shaped electric heating cable in a 1m³ cuboid soil container. The measurements were carried out in a climate chamber at a defined constant temperature of 10 °C. The evaluation of the transient response behaviour is spatially resolved. This results in coordinate-related temperature points, which describe temperature gradients in all axes of the container over time. Three different types of soil were investigated. The temperature behaviour of humus soil, sand and a mixture of these was investigated experimentally in smaller experiments and the material data such as heat capacity, thermal conductivity and density were determined thermophysically in the laboratory. Based on this data, a CFD model was developed which can be used to modify the geometry parameters of the helix.</p>
OPLA-Tool v2.0: a Tool for Product Line Architecture Design Optimization
<p>The Multi-objective Optimization Approach for Product Line Architecture Design (MOA4PLA) is the seminal approach that successfully optimizes Product Line Architecture (PLA) design using search algorithms. The tool named OPLA-Tool was developed in order to automate the use of MOA4PLA. Over time, the customization of the tool to suit the needs of new research and application scenarios led to several problems. The main problems identified in the original version of OPLA-Tool are environment configuration, maintainability and usability problems, and PLA design modeling and visualization. Such problems motivated the development of a new version of this tool: OPLA-Tool v2.0, presented in this work. In this version, those problems were solved by the source code refactoring, migration to a web-based graphical user interface (GUI) and inclusion of a new support tool for PLA modeling and visualization. Furthermore, OPLA-Tool v2.0 has new functionalities, such as new objective functions, new search operators, intelligent interaction with users during the optimization process, multi-user authentication and simultaneous execution of several experiments to PLA optimization. Such a new version of OPLA-Tool is an important achievement to PLA design optimization as it provides an easier and more complete way to automate this task. </p>
PERCEIVE Design Cultural Probe Kit Tools
<p>Cultural Probe Kit design tools, developed to analyse and study visitors behaviours in the field of Cultural Heritage, in relation to Curiosity, Sense of Care, Social Cohesion and Authenticity</p>
Evaluation Artifacts of RESTRuler, a Static Analysis Tool to Detect Design Rule Violations in OpenAPI Definitions
<p>All artifacts related to the empirical evaluation of the RESTRuler CLI, a Java-based tool to identify design rule violations in OpenAPI descriptions</p> <p>You can find the latest version here: <a href="https://github.com/restful-ma/rest-ruler-evaluation">https://github.com/restful-ma/rest-ruler-evaluation</a></p>
Figure 4 in The Design of Solar-Powered Lighting Aid Tool for Swimming Crab (Portunus pelagicus) Fishing - 'SuryaNet' - as an Effort to Preserve Environmental Sustainability
Figure 4. Underwater Light
Figure 1 in The Design of Solar-Powered Lighting Aid Tool for Swimming Crab (Portunus pelagicus) Fishing - 'SuryaNet' - as an Effort to Preserve Environmental Sustainability
Figure 1. Data Collection from Swimming Crab Fishermen in the Eastern Waters of Surabaya
Figure 5 in The Design of Solar-Powered Lighting Aid Tool for Swimming Crab (Portunus pelagicus) Fishing - 'SuryaNet' - as an Effort to Preserve Environmental Sustainability
Figure 5. Solar Panel
Figure 3 in The Design of Solar-Powered Lighting Aid Tool for Swimming Crab (Portunus pelagicus) Fishing - 'SuryaNet' - as an Effort to Preserve Environmental Sustainability
Figure 3. Charging Dock Design
Figure 2 in The Design of Solar-Powered Lighting Aid Tool for Swimming Crab (Portunus pelagicus) Fishing - 'SuryaNet' - as an Effort to Preserve Environmental Sustainability
Figure 2. Surya Net System Placed on the Gillnet Fishing Vessel
Review-based Comparison of Design Pattern Detection Tools
<p>Replication package containing the data obtained from the Systematic Literature Review of design pattern detection tools and the instances obtained from the execution of the tools available.</p> <p>Contains a README file introducing each of the files in this replication package.</p>
Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings (Dataset v1.2.1)
<p><strong>THE PUBLICATION</strong></p> <p>The data set provided is complementary to the thermal comfort review by Mamulova et al., 2023, titled "<strong>Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings</strong>": <a href="https://doi.org/10.1016/j.buildenv.2023.110502">Link to full publication</a>. The scoping review examines 77 multi-domain thermal comfort studies and initiates a discussion on model scalability; a model parameter which facilitates the understanding and prediction of thermal comfort conditions in real-world practice.</p> <p><strong>THE DATA</strong></p> <p>This database contains 27 scalability parameters per study which are used to analyse current research practices. For the results, please consult the review publication, as this database only contains raw data. For clarity, a legend of the scalability parameters is provided below.</p> <p><strong>*** PLEASE NOTE ***</strong></p> <p><strong>This data set may be utilised, altered and/or expanded. However, you are kindly asked to cite this data set, the review publication (if applicable) and contact the corresponding author at eugenemamulova@gmail.com. </strong></p> <table> <tbody> <tr> <td><em>Citation</em></td> <td><em>Citation number used in Mamulova et al.,"Multi-Domain Thermal Comfort Models for Office Buildings: Are Current Practices Scalable?", (2023)</em></td> <td><em>E.g. 1</em></td> </tr> <tr> <td><em>First Author</em></td> <td><em>Surname of the main author, for reference purposes only.</em></td> <td><em>E.g. Al-Atrash</em></td> </tr> <tr> <td><em>Publication</em></td> <td><em>Publication year</em></td> <td><em>E.g. 2020</em></td> </tr> <tr> <td><em>Dependent A</em></td> <td><em>List of variables used to measure thermal perception</em></td> <td><em>E.g. Neutral temperature/ Thermal sensation</em></td> </tr> <tr> <td><em>Dependent B</em></td> <td><em>Scale used to measure each dependent variable</em></td> <td> </td> </tr> <tr> <td><em>Interaction A</em></td> <td><em>List of interaction effect(s) included in the explanatory/predictive model(s) </em></td> <td><em>E.g. Thermal and age/ Thermal and acoustical and personality</em></td> </tr> <tr> <td><em>Interaction B</em></td> <td><em>Is/are the effect(s) statistically significant?</em></td> <td><em>E.g. yes/ no/ (unknown)</em></td> </tr> <tr> <td><em>Crossed A</em></td> <td><em>List of crossed effect(s) included in the explanatory/predictive model(s) </em></td> <td><em>E.g. Acoustical/ Personality/ Age</em></td> </tr> <tr> <td><em>Crossed B</em></td> <td> </td> <td><em>*Note: Temperature is a main effect and is not included in the list</em></td> </tr> <tr> <td><em>Explanatory A</em></td> <td><em>Type of explanatory model</em></td> <td><em>E.g. Observation/ Statistical/ N/A</em></td> </tr> <tr> <td><em>Explanatory B</em></td> <td><em>Description of the explanatory model</em></td> <td><em>E.g. Asymptotic General Symmetry Test to check significance of difference in thermal perception between window conditions</em></td> </tr> <tr> <td><em>Predictive A</em></td> <td><em>Does the article include a predictive model?</em></td> <td><em>E.g. yes/ no</em></td> </tr> <tr> <td><em>Predictive B</em></td> <td><em>Type of predictive algorithm</em></td> <td><em>E.g. Logistic regression/ N/A</em></td> </tr> <tr> <td><em>Predictive C</em></td> <td><em>Description or formulation of the predictive model</em></td> <td><em>E.g. Probability of feeling too hot and probability of feeling too cold in relation to sound pressure level</em></td> </tr> <tr> <td><em>Performance</em></td> <td><em>Reported predictive performance</em></td> <td><em>E.g. Accuracy = 80%/ F-score = 0.8/ N/A</em></td> </tr> <tr> <td><em>Location</em></td> <td><em>City in which the measurements take place</em></td> <td><em>E.g. Paris</em></td> </tr> <tr> <td><em>Period</em></td> <td><em>Period over which the measurements take place</em></td> <td><em>E.g. Jan-Feb 2020</em></td> </tr> <tr> <td><em>Start time</em></td> <td><em>Time of day at which the measurements begin</em></td> <td><em>*Note: Time of day is not reported for most field studies. For this reason, time of day is only recorded for laboratory experiements.</em></td> </tr> <tr> <td><em>Study type</em></td> <td><em>Type of building and whether the experimental conditions are controlled by the experiment leader</em></td> <td><em>E.g. Field (controlled)/ Field (uncontrolled)/ Lab (controlled)/ Lab (uncontrolled)</em></td> </tr> <tr> <td><em>Building layout</em></td> <td><em>Building layout</em></td> <td><em>E.g. Laboratory office (LO)/ Laboratory neutral (LN)/ Field office (FO)</em></td> </tr> <tr> <td><em>Exposure</em></td> <td><em>Exposure of the participant, in minutes, to the experimental conditions, excluding preparation time</em></td> <td><em>*Note: Exposure is not reported for most field studies. For this reason, exposure is only recorded for laboratory experiements and is assumed to be longer than 60 minutes.</em></td> </tr> <tr> <td><em>Number of buildings/chambers</em></td> <td><em>Number of different locations used for conducting measurements</em></td> <td><em>E.g. 1</em></td> </tr> <tr> <td><em>Number of participants</em></td> <td><em>Number of individuals who take part in each experiment</em></td> <td><em>*Note: Outliers who are subsequently excluded from the modelling phase are not included.</em></td> </tr> <tr> <td><em>Survey type</em></td> <td><em>Description of the type of survey used for subjective measurements</em></td> <td><em>E.g. Longitudinal questionnaire/ Transverse questionnaire/ N/A</em></td> </tr> <tr> <td><em>Survey content</em></td> <td><em>Are the contents of the survey provided in the article?</em></td> <td><em>E.g. Available/ unavailable</em></td> </tr> <tr> <td><em>Survey source</em></td> <td><em>Is/are the source(s) of the survey items mentioned in the article?</em></td> <td><em>E.g. Available/ unavailable</em></td> </tr> <tr> <td><em>Survey reliability</em></td> <td><em>Is the reliability of the survey items reported in the article?</em></td> <td><em>E.g. Available/ unavailable</em></td> </tr> <tr> <td><em>Survey duration</em></td> <td><em>Is the survey duration reported in the article?</em></td> <td><em>E.g. Available/ unavailable</em></td> </tr> <tr> <td><em>Context A</em></td> <td><em>Overview of the contextual information provided by the authors</em></td> <td><em>E.g. Room layout/ Room dimennsions</em></td> </tr> <tr> <td><em>Context B</em></td> <td><em>Qualitative/quantitative contextual information</em></td> <td><em>E.g. Figure containing room layout/ 3m x 3m x 5m</em></td> </tr> <tr> <td><em>Contextual variables A</em></td> <td><em>List of contextual variable(s) measured by the researchers (see Fig. A.)</em></td> <td><em>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</em></td> </tr> <tr> <td><em>Contextual variables B</em></td> <td><em>Range of values included in the experiment and their respective units.</em></td> <td><em>E.g. figure</em></td> </tr> <tr> <td><em>Social variables A</em></td> <td><em>List of social variable(s) measured by the researchers (see Fig. A.)</em></td> <td><em>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</em></td> </tr> <tr> <td><em>Social variables B</em></td> <td><em>Range of values included in the experiment and their respective units.</em></td> <td><em>E.g. [1,2,3,4,5]</em></td> </tr> <tr> <td><em>Personal variables A</em></td> <td><em>List of contextual variable(s) measured by the researchers (see Fig. A.)</em></td> <td><em>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</em></td> </tr> <tr> <td><em>Personal variables B</em></td> <td><em>Range of values included in the experiment and their respective units.</em></td> <td><em>E.g. [red, blue]</em></td> </tr> <tr> <td><em>Physical variables A</em></td> <td><em>List of physical variable(s) measured by the researchers (see Fig. A.)</em></td> <td><em>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</em></td> </tr> <tr> <td><em>Physical variables B</em></td> <td><em>Range of values included in the experiment and their respective units</em></td> <td><em>E.g. dB(A)</em></td> </tr> <tr> <td><em>Full-factorial</em></td> <td><em>Is/are the experiment(s) full-factorial?</em></td> <td><em>*Note: Uncontrolled field experiments are automatically labelled as fractional factorial.</em></td> </tr> <tr> <td><em>(Participant) Control</em></td> <td><em>Do participants have control over one or more experimental conditions?</em></td> <td><em>E.g. Yes/ No</em></td> </tr> <tr> <td><em>With/between subjects</em></td> <td><em>Are the experimental conditions shared between or within the participants?</em></td> <td><em>E.g. w/ b</em></td> </tr> <tr> <td><em>Fixed variables A</em></td> <td><em>List of variables reported as constant during the measurements</em></td> <td><em>E.g. Relative humidity/ Metabolic rate</em></td> </tr> <tr> <td><em>Fixed variables A</em></td> <td><em>(Range of) values and their respective units.</em></td> <td><em>E.g. 30-40%/ 1.2 met</em></td> </tr> <tr> <td><em>Summary</em></td> <td><em>Description of the research outome (outcome of the explanatory and/or predictive modelling)</em></td> <td><em>E.g. Lack of perceived control has a significant negative effect on neutral temperatures.</em></td> </tr> <tr> <td><em>Evaluation</em></td> <td><em>Are the participants invited to evaluate their experience once the experiment has been completed? </em></td> <td><em>E.g. Yes/ no</em></td> </tr> </tbody> </table> <p>Note: The data in v1.1.0 has not yet been optimised for analytics.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.