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.
65
datasets available to search
ShareScore release 0.9.0
Dataset results
65 results for “Design methods”
Supplementary material 1 from: Jakobsberg GO, Mooney WD, Rangel-Sanchez J (2022) Assessment of an inexpensive trap design and survey method for vespine wasps (Hymenoptera, Vespidae, Vespinae). Journal of Hymenoptera Research 89: 171-182. https://doi.org/10.3897/jhr.89.80284
Tables S1–S3
Figure 3 from: Jakobsberg GO, Mooney WD, Rangel-Sanchez J (2022) Assessment of an inexpensive trap design and survey method for vespine wasps (Hymenoptera, Vespidae, Vespinae). Journal of Hymenoptera Research 89: 171-182. https://doi.org/10.3897/jhr.89.80284
Figure 3 Average catch at each height of the hanging line traps. Includes the average number of individuals from each of the four species detected.
Figure 1 from: Jakobsberg GO, Mooney WD, Rangel-Sanchez J (2022) Assessment of an inexpensive trap design and survey method for vespine wasps (Hymenoptera, Vespidae, Vespinae). Journal of Hymenoptera Research 89: 171-182. https://doi.org/10.3897/jhr.89.80284
Figure 1 Trap locations and route of the east-west road transect. Trap locations (white dots) and route (black line) with state boundaries and level 3 ecoregions of the Environmental Protection Agency (Comeleo 2010).
Figure 2 from: Jakobsberg GO, Mooney WD, Rangel-Sanchez J (2022) Assessment of an inexpensive trap design and survey method for vespine wasps (Hymenoptera, Vespidae, Vespinae). Journal of Hymenoptera Research 89: 171-182. https://doi.org/10.3897/jhr.89.80284
Figure 2 Average yield of hanging/ground trap experiment. Results are separated into paired and unpaired tests and average yield is categorized by species.
DigFrag as a digital fragmentation method used for artificial intelligence-based drug design
Open the record for dataset details and reuse information.
Implementation of Scrum Method on Mobile-Based Billing Information System Design
Open the record for dataset details and reuse information.
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>
Fig. 4 in To design, or not to design? Comparison of beetle ultraconserved element probe set utility based on phylogenetic distance, breadth, and method of probe
Fig. 4. The proportion of different types of loci targeted by scarab and hydrophiloid probe sets.
Comparing Through-the-Needle With Suture-Method Catheter Designs for Popliteal Nerve Blocks
ClinicalTrials.gov study NCT03442036. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Data from: The relative power of genome scans to detect local adaptation depends on sampling design and statistical method
Open the record for dataset details and reuse information.
Designing a single cell RNA sequencing benchmark dataset to compare protocols and analysis methods
GEO Series GSE118767. Homo sapiens. 13 samples. Type: Expression profiling by high throughput sequencing.
Designing a single cell RNA sequencing benchmark dataset to compare protocols and analysis methods (Drop-Seq)
GEO Series GSE118706. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Designing a single cell ATAC-Seq (scATAC-Seq) dataset to validate long read RNA-Seq isoforms and benchmark scATAC-Seq analysis methods II
GEO Series GSE224045. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Designing a single cell RNA sequencing benchmark dataset to compare protocols and analysis methods
GEO Series GSE142286. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Designing a single cell RNA sequencing benchmark dataset to compare protocols and analysis methods (Cel_Seq)
GEO Series GSE118704. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Designing a single cell ATAC-Seq (scATAC-Seq) dataset to validate long read RNA-Seq isoforms and benchmark scATAC-Seq analysis methods
GEO Series GSE142285. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Designing a single cell RNA sequencing benchmark dataset to compare protocols and analysis methods (9 cell mixture dataset).
GEO Series GSE117450. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing.
Designing a single cell RNA sequencing benchmark dataset to compare protocols and analysis methods [5 Cell Lines Cel-seq]
GEO Series GSE126908. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
Designing a single cell RNA sequencing benchmark dataset to compare protocols and analysis methods (RNAmix_Sort-seq)
GEO Series GSE117618. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Feasibility, Acceptability, and Effectiveness of an Individualized Plant-based (iPLANT) Diet Plan in Colorectal Cancer: Mixed Method Embedded Design
ClinicalTrials.gov study NCT06250166. IPD Sharing: NO. Countries: 1. Publications: 0.
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.