Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
31
datasets available to search
ShareScore release 0.9.0
Dataset results
31 results for “Design Process”
A Free Verbalization Method of Evaluating Sound Design: The Effectiveness of Artificially Intelligent Natural Language Processing Methods and Tools
<p>"Robot" voice sound files. Seventeen sound files were recorded in four formats; raw human voiceover (VO), and three types of robot voice: vocoded voice 1 (“robo”), vocoded voice 2 with music (“kbd”), and a “beep” voice. Each was recorded as 44.1kHz, 24-bit wav files in a professional recording studio. VO was recorded by professional voice actor DB Cooper, who has been the robot voice for several video games, as well as the voice of the DEE BMW internal car AI voice system. Cooper recorded three versions of the emotes on a Sennheiser MKH-416. Professional sound designer pdx Drescher, an expert in robot<br> and interface sound design, created three sets of robot voices from<br> the original voice files. With guidance from one of the authors,<br> pdx was tasked with trying different approaches to turning the VO<br> samples into three different types of robot voice while attempting<br> to maintain the meaning of the original sounds as described in the<br> list above through preserving the prosody/melodic contour of the<br> original. The first set, robo, used some clips from one of pdx’s prior<br> robot voice projects and integrated them to approximate the emo-<br> tional intention of the VO. Clips were re-pitched, manipulated, and<br> modulated using ProTools plugins. For the kbd takes, VO sounds<br> were played into a Shure SM58 microphone. Vocoder patches mod-<br> ified the signal by voice formants, and the pitch was determined<br> by MIDI notes and pitch-bend controllers. Output of the synthe-<br> sizer was then edited with additional synth patches and effects (EQ,<br> modulation, etc.). We made particular use of a plugin called Envy<br> by Cargo Cult, which takes the volume, pitch, and EQ envelopes of<br> one sound (the original VO) and apply them to another sound. This<br> helped make the synth resemble the prosody of the original sound<br> to some degree. The beep sounds underwent a similar development<br> process as the robo takes, but with interface “bleeps and bloops”<br> derived from various sound effects libraries, including the Star Trek<br> LCARS soundset.</p> <p>The following sounds were recorded: 1. Warning calm (“Uh-oh”) 2. Warning alarm (“ah!”) 3. Wrong/<br> error (“rrrrr”) 4. Correct/good (“yay”) 5. Surprise (neutral) (“Oh!”) 6.<br> Surprise (good) (“Oh!”) 7. Surprise (bad) “(ohhh”) 8. Love/adoration<br> (“awww”) 9. Disgust (“ew”) 10. Contempt (“ech”) 11. Guilt (“hmmm”)<br> 12. Confused (“huh?”) 13. Laugh (“ha ha”) 14. Calculating (“hmmm”)<br> 15. Sigh 16. Giggle 17. Pain (“ow ")</p>
Clinical Evaluation of Manufacturing Processes for a Reusable Multifocal Optical Design in a Presbyopic Population
ClinicalTrials.gov study NCT03787472. IPD Sharing: YES. Countries: 1. Publications: 0.
This Study is Designed to Evaluate PD/PK and Safety of Replagal Manufactured by Two Different Processes.
ClinicalTrials.gov study NCT01304277. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Designing Computer Science Competency Statements: A Process and Curriculum Model for the 21st Century
<p>Companion materials - Based on CS2013 Knowedge Areas - Competency Statements for CS Curricula:</p> <p>Produced by members of the ITiCSE 2020 Working Group Report:</p> <p>Designing Computer Science Competency Statements: A Process and Curriculum Model for the 21st Century</p> <p>Clear, A., Clear, T., Vichare, A., Charles, T., Frezza, S., Gutica, M., . . . Pitt, F. (2020). Designing Computer Science Competency Statements: A Process and Curriculum Model for the 21st Century [In Press]. In Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education (pp. TBA): ACM.</p>
MGEP_HienNguyen_skill-rating questionnaires for advanced service design through Analytical Hierarchy Process_2022
<p>This presents a dataset of expert evaluation to determine what design skills for advanced services are important for the design team. The expert selection criteria were defined by the purposive sampling to recruit the appropriate experts through the chain sampling technique. Their evaluations were collected through the skill-rating questionnaires in accordance with Analytical Hierarchy Process (AHP). The resulting dataset was processed through the AHP algorithms programmed by R language. Transparency data and code availability may be (re)used by the design practitioners and/or researchers for replication and further analysis, offering the reproduceable research method by which multiple-criteria decision analysis is conducted.</p>
The Role of Artificial Intelligence in The Architectural Design Process
<p>This research explores the integration of artificial intelligence (AI) in architectural design education, focusing on its impact on student creativity and design processes. Architects have increasingly adopted AI, with many acknowledging its potential to enhance efficiency. However, challenges remain, particularly in balancing AI with human creativity and intuition. The study, conducted in a third-year architecture studio course, employs Quantitative Content Analysis (QCA) to examine how AI influences design outcomes. By analyzing student reports, the research investigates patterns in AI usage and its role in achieving educational goals, aiming to inform AI's integration into architecture pedagogy.</p>
Multi-disciplinary Participatory Design of a Process to Deliver a CKD Diagnosis in Primary Care
ClinicalTrials.gov study NCT03084159. IPD Sharing: NO. Countries: 1. Publications: 0.
Perceptions of and Reactions to Ultra-Processed Menu Label Designs
ClinicalTrials.gov study NCT07214805. IPD Sharing: YES. Countries: 1. Publications: 0.
Evolution of Family Alliance in Families With a Designated Adolescent Patient (12-18 Years) During a Family Therapy Process
ClinicalTrials.gov study NCT04370964. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Sperm Micro-swim up Technique; Process and Select in the Same Dish: a Sibling-oocyte-split Design
ClinicalTrials.gov study NCT05113225. IPD Sharing: NO. Countries: 1. Publications: 0.
A Layer Model for the Railway Infrastructure Design Process - Supplement Data
<p>This repository is the supplement data for the dissertation <em>"</em><em>A Layer Model for the Railway Infrastructure Design Process"</em>. The main result is the `schema/layer_model.json` file with the result of the dissertation in the <a href="https://json-schema.org">JSON Schema format</a>. The result was achieved by following the <a href="https://doi.org/10.2307/25148625">Design Science Research method</a>, which consists of cycles. The cycles are documented in the `DSR_cycles` folder and the git commits of this repository. The final result of the DSR cycles is an artefact in the `/` respectively the folders `data/layers`, `schema`, `src`, and `test`. <a href="https://julialang.org">Julia</a> and <a href="https://yaml.org">YAML</a> were used for data transformation and data input.</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.