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.
6,335
datasets available to search
ShareScore release 0.9.0
Dataset results
6,335 results for “direct”
Data from: Gigapixel big data movies provide cost‐effective seascape scale direct measurements of open‐access coastal human use such as recreational fisheries
Collecting data on unlicensed open‐access coastal activities, such as some types of recreational fishing, has often relied on telephone interviews selected from landline directories. However, this approach is becoming obsolete due to changes in communication technology such as a switch to unlisted mobile phones. Other methods, such as boat ramp interviews, are often impractical due to high labor cost. We trialed an autonomous, ultra‐high‐resolution photosampling method as a cost effect solution for direct measurements of a recreational fishery. Our sequential photosampling was batched processed using a novel software application to produce "big data" time series movies from a spatial subset of the fishery, and we validated this with a regional bus‐route survey and interviews with participants at access points. We also compared labor costs between these two methods. Most trailer boat users were recreational fishers targeting tuna spp. Our camera system closely matched trends in temporal variation from the larger scale regional survey, but as the camera data were at much higher frequency, we could additionally describe strong, daily variability in effort. Peaks were normally associated with weekends, but consecutive weekend tuna fishing competitions led to an anomaly of high effort across the normal weekday lulls. By reducing field time and batch processing imagery, Monthly labor costs for the camera sampling were a quarter of the bus‐route survey; and individual camera samples cost 2.5% of bus route samples to obtain. Gigapixel panoramic camera observations of fishing were representative of the temporal variability of regional fishing effort and could be used to develop a cost‐efficient index. High‐frequency sampling had the added benefit of being more likely to detect abnormal patterns of use. Combinations of remote sensing and on‐site interviews may provide a solution to describing highly variable effort in recreational fisheries while also validating activity and catch.
Data from: Can maternally inherited endosymbionts adapt to a novel host? Direct costs of Spiroplasma infection, but not vertical transmission efficiency, evolve rapidly after horizontal transfer into D. melanogaster
Maternally inherited symbionts are common in arthropods and many have important roles in host adaptation. The observation that specific symbiont lineages infect distantly related host species implies new interactions are commonly established by lateral transfer events. However, studies have shown that symbionts often perform poorly in novel hosts. We hypothesized selection on the symbiont may be sufficiently rapid that poor performance in a novel host environment is rapidly ameliorated, permitting symbiont maintenance. Here, we test this prediction for a Spiroplasma strain transinfected into the novel host Drosophila melanogaster. In the generations immediately following transinfection, the symbiont had low transmission efficiency to offspring and imposed severe fitness costs on its host. We observed that effects on host fitness evolved rapidly, being undetectable after 17 generations in the novel host, whereas vertical transmission efficiency was poorly responsive over this period. Our results suggest that long-term symbiosis may more readily be established in cases where symbionts perform poorly in just one aspect of symbiosis.
Data from: Phenotypic plasticity in female mate choice behavior is mediated by an interaction of direct and indirect genetic effects in Drosophila melanogaster
Female mate choice is a complex decision-making process that involves many context-dependent factors. In Drosophila melanogaster, a model species for the study of sexual selection, indirect genetic effects (IGEs) of general social interactions can influence female mate choice behaviors, but the potential impacts of IGEs associated with mating experiences are poorly understood. Here, we examined whether the IGEs associated with a previous mating experience had an effect on subsequent female mate choice behaviors and quantified the degree of additive genetic variation associated with this effect. Females from 21 different genetic backgrounds were housed with males from one of two distinct genetic backgrounds for either a short (3 hr) or long (48 hr) exposure period and their subsequent mate choice behaviors were scored. We found that the genetic identity of a previous mate significantly influenced a female's subsequent interest in males and preference of males. Additionally, a hemiclonal analysis revealed significant additive genetic variation associated with experience-dependent mate choice behaviors, indicating a genotype-by-environment interaction for both of these parameters. We discuss the significance of these results with regard to the evolution of plasticity in female mate choice behaviors and the maintenance of variation in harmful male traits.
Cell type directed design of synthetic enhancers
<p>All the code, model files, and data associated with the manuscript : 'Cell type directed design of synthetic enhancers' can be found here.</p>
Clinical usability study of a transcranial direct current stimulation for primary dysmenorrhea: a randomized controlled trial
Open the record for dataset details and reuse information.
Data for: Al-Cu intermetallic phase growth in hybrid metal extrusion & bonding welds exposed to isothermal annealing or direct current cycling
<p>Experimental data used in the publication with title as stated above.</p> <p>The data is structured according to investigated weld condition and contains</p> <ol> <li>(S)TEM images</li> <li>electron diffraction patterns (SPED datasets can be provided upon request as uploading it was a hassle ...)</li> <li>STEM EDS datasets</li> <li>APT results</li> </ol>
IR4.0 and internet of things: future directions towards enhanced connectivity, automation, and sustainable innovation
Open the record for dataset details and reuse information.
Directional Terms in Idu, a Language of Northeast India
<p>The Idu language is spoken in the extreme northeast of Arunachal Pradesh. Its traditional terrain stretched between the Tibetan Plateau and the plains of the Brahmaputra, unusually incorporating a wide variety of ecozones. The Idu traditionally lived in highly dispersed settlements. As a consequence, orientation was determined more by up versus down as well as by the sides of the many rivers which cross their area. The paper describes the lexicon of Idu in describing directions. There is probably no distinct morphosyntactic category of directional. The paper illustrates each term with example sentence contexts and represents visually directional oppositions as they appear to speakers.</p>
Supplementary material for "Effects of hand postures on voice directivity"
<p>The repository contains measured voice directivities while performing static hand postures. The directivities were measured in the anechoic chamber of TH Köln applying a surrounding spherical microphone array, which has a diameter of<br> 2 m and a shape of a pentakis dodecahedron. The datasets were stored in the SOFA-format and include two measurements for each of the 13 subjects.</p> <p>Each subject's dataset contains directivities of the following conditions</p> <ul> <li>REF: Reference measurement - normal articulation of an [a].</li> <li>HFM: Holding a hand in front of the mouth while articulating an [a].</li> <li>CAM: Cupping the hand around the mouth while articulating an [a].</li> </ul> <p> </p> <p>The .pdf file contains</p> <ul> <li>general information on the voice directivity files in the SOFA format</li> <li>information on the indices and names of the SOFA-files</li> <li>Polar plots of the directivity patterns in third-octave bands </li> </ul> <p> </p> <p>The .zip files contain</p> <ul> <li>voice directivity patterns in the SOFA format sampled on the sparse measuring grid</li> <li>voice directivity patterns in the SOFA format upsampled to a dense grid</li> </ul> <p> </p> <p>The Matlab script provides</p> <ul> <li>an example reading a dataset, performing spatial upsampling if required, and creating some basic plots. </li> </ul>
Something in the wind: The influence of wind speed and direction on African lion movement behaviour
<p>Olfaction is a key sense, enabling animals to locate forage, select mates, navigate their environment, and avoid predation. Wind is an important abiotic factor that modulates the strength of olfactory information detected by animals. In theory, when airflow is unidirectional, an animal can maximise the size of their olfactory search area and increase odour detection probability by moving crosswind. Given energetic costs inherent to activity and locomotion, behavioural search strategies that optimize the benefit-cost ratio should be advantageous. We tested whether African lions (Panthera leo) modify their movement directionality and distance according to wind speed and direction during hours of darkness when they are most likely to hunt mobile and elusive prey. We tracked 29 lions in southern Zimbabwe using GPS collars and deployed a weather station to collect detailed abiotic data. We found that when wind speeds increased lions were more likely to move crosswind. We also found that female lions, which tend to hunt more often than males, travelled farther when wind speeds were stronger. The results of our analysis suggest that lions adapt their movement behaviour according to wind speed and direction. We inferred that this was a behavioural decision to maximise the amount of olfactory information gained per unit of energy spent. Our findings not only offer one of the first detailed insights on large carnivore anemotaxis (movement direction relative to wind) but also make an important contribution towards understanding the influence of wind on predator ecology in general which remains understudied to date. Keywords: Panthera Leo, anemotaxis, wind, movement behaviour, olfaction</p>
Figure 6 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
Figure 6 - The user interface field for uploading EML files into ARPHA.
Figure 4 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
Figure 4 - Download of an EML from the GBIF Integarted Publishuing Toolkit (IPT)
Figure 1 from: Senderov V, Georgiev T, Penev L (2016) Online direct import of specimen records into manuscripts and automatic creation of data papers from biological databases . Research Ideas and Outcomes 2: e10617. https://doi.org/10.3897/rio.2.e10617
Figure 1 - Poll results about composition of audience during live participation.
DIRECTIONS FOR INCREASING BUSINESS AND ITS CONTRIBUTION TO GREEN ECONOMY DEVELOPMENT
Open the record for dataset details and reuse information.
Skillful bias correction of offshore near-surface wind speed and wind direction forecasting based on a multi-task machine learning model
<h3>Dataset</h3> <p>1. observation data over 14 weather stations</p> <p>Variables: hourly near-surface 2-min average wind speed, wind direction </p> <p>2. ECMWF-IFS forecast data over 14 weather stations</p> <p>Variables: hourly predictors at surface level and upper level in next 48 hours (shown in Table 1. and Table 2.)</p> <p>Table 1. ECMWF-IFS forecast data at surface level</p> <div> <table> <tbody> <tr> <td> <p>Predictors</p> </td> <td> <p>Abbreviation</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>Temperature at 2 m</p> </td> <td> <p>2t</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Sea surface temperature</p> </td> <td> <p>sst</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Dewpoint temperature at 2 m</p> </td> <td> <p>2d</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Convective precipitation in the past hour</p> </td> <td> <p>cp</p> </td> <td> <p>mm</p> </td> </tr> <tr> <td> <p>Mean sea level pressure</p> </td> <td> <p>msl</p> </td> <td> <p>hPa</p> </td> </tr> <tr> <td> <p>Zonal component of wind speed at 10 m</p> </td> <td> <p>10u</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Meridional component of wind speed at 10 m</p> </td> <td> <p>10v</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind speed at 10 m</p> </td> <td> <p>10ws</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind direction at 10 m</p> </td> <td> <p>10wd</p> </td> <td> <p>°</p> </td> </tr> <tr> <td> <p>Zonal component of wind speed at 100 m</p> </td> <td> <p>100u</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Meridional component of wind speed at 100 m</p> </td> <td> <p>100v</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind speed at 100 m</p> </td> <td> <p>100ws</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind direction at 100 m</p> </td> <td> <p>100wd</p> </td> <td> <p>°</p> </td> </tr> </tbody> </table> </div> <div> </div> <p>Table 2. ECMWF-IFS forecast data at upper level</p> <table> <tbody> <tr> <td> <p>Predictors</p> </td> <td> <p>Abbreviation</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>Relative humidity at xxx hPa</p> </td> <td> <p>r_Lxxx</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>Temperature at xxx hPa</p> </td> <td> <p>t_Lxxx</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Vertical velocity of wind at xxx hPa</p> </td> <td> <p>w_Lxxx</p> </td> <td> <p>Pa s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Zonal component of wind at xxx hPa</p> </td> <td> <p>u_Lxxx</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Meridional component of wind at xxx hPa</p> </td> <td> <p>v_Lxxx</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind speed at xxx hPa</p> </td> <td> <p>ws_Lxxx</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind direction at xxx hPa</p> </td> <td> <p>wd_Lxxx</p> </td> <td> <p>°</p> </td> </tr> </tbody> </table> <div> </div> <p>3. key variables constructed by feature engineering</p> <p>(1) sort-term statistics, including <em>maximum, minimum, mean </em>and <em>variance</em> of key variables (<em>2t</em>,<em> 10u</em>, <em>10v </em>and <em>10ws</em>) from ECMWF-IFS model during the next 48 hours,</p> <p> (2) long-term statistics, including <em>mean </em>and <em>deviation</em> of key variables (<em>2t</em>,<em> 10u</em>, <em>10v </em>and <em>10ws</em>) from ECMWF-IFS model during history 3-yr period (January 2020–December 2022),</p> <p> (3) thermodynamic factors, including the low-level wind shear between <em>10ws</em> and <em>100ws</em>, vertical wind shear between 200 hPa and 850 hPa<em>, </em>the differences between <em>sst</em><em> </em>and <em>2t</em><em>.</em></p> <h3>Scripts</h3> <p>1. Random Forest model training code</p> <p>2. LightGBM model training code</p> <p>3. XGBoost model training code</p> <p>4. TabNet-MTL model training code</p> <p> </p>
Raw Data files for publication: " A matter of design and coupling: High indoor charging efficiencies with organic solar modules direct coupled to a sodium ion battery"
<p>Supporting raw data for the publication linked below: </p> <p>Inclusive of</p> <ul> <li>spectral data of light sources </li> <li>LED calibration data </li> <li>PV characterization data under LED and AM1.5 </li> <li>battery characterization data and charge-discharge data</li> </ul>
Pre-test and post-test scores on Grammar and Self Directed Learning in Vietnamese French class
<p>The results from 5-scale Likert survey for sefl-directed learning and grammar test before and after introducing ChatGPT as a learning instrument in a French class in University of Transport and Communications, Hanoi, Vietnam. </p> <p><strong>SURVEY FORM 01 - QUESTIONS</strong></p> <p>1<span> </span>I am satisfied with my university life overall</p> <p>2<span> </span>I am satisfied with my chosen major.</p> <p>3<span> </span>I am interested in the French courses.</p> <p>4<span> </span>I feel confident and in control when using digital technologies.</p> <p>5<span> </span>I frequently engage with ChatGPT for various tasks.</p> <p>6<span> </span>I know what I need to learn. </p> <p>7<span> </span>Regardless of the result or effectiveness of my learning, I still like learning. </p> <p>8<span> </span>I strongly hope to constantly improve and excel in my learning. </p> <p>9<span> </span>My successes and failures inspire me to continue learning. </p> <p>10<span> </span>I enjoy finding answers to questions.</p> <p>11<span> </span>I will not give up learning because I face some difficulties. </p> <p>12<span> </span>I can proactively establish my learning goals. </p> <p>13<span> </span>I know what learning strategies are appropriate for me in reaching my learning goals. </p> <p>14<span> </span>I set the priorities of my learning.</p> <p>15<span> </span>In the classroom or on my own, I am able to follow my own plan of learning.</p> <p>16<span> </span>I am good at arranging and controlling my learning time.</p> <p>17<span> </span>I know how to find resources for my learning.</p> <p>18<span> </span>I can connect new knowledge with my own personal experiences.</p> <p>19<span> </span>I understand the strengths and weakness of my learning.</p> <p>20<span> </span>I can monitor my learning progress.</p> <p>21<span> </span>I can evaluate on my own learning outcomes.</p> <p>22<span> </span>My interaction with others helps me plan for further learning.</p> <p>23<span> </span>I would like to learn the language and culture of those whom I frequently interact with.</p> <p>24<span> </span>I am able to express messages effectively in oral presentations.</p> <p>25<span> </span>I am able to communicate messages effectively in writing.</p> <p><strong>SURVEY FORM 02 - QUESTIONS</strong></p> <p>1<span> </span>In the past five weeks, I have frequently used ChatGPT for learning French grammar.</p> <p>2<span> </span>I find my experience with ChatGPT better compared to traditional grammar learning methods such as textbooks, lectures, or other educational materials.</p> <p>3<span> </span>I am satisfied with the effectiveness of using ChatGPT in learning French grammar.</p> <p>4<span> </span>I feel motivated when using ChatGPT for learning French grammar.</p> <p>5<span> </span>ChatGPT helps me to be more proactive in my study plan.</p> <p>6<span> </span>I am willing to continue using ChatGPT for learning French grammar.</p>
Direct para-selective C-H Amination of Benzyl Alcohols: Selectivity Independent of Side Substituents
<p>DFT optimized structures accompanying the paper "Direct para-selective C-H Amination of Benzyl Alcohols: Selectivity Independent of Side Substituents"</p>
Transcranial direct current stimulation enhances effort maintenance in ADHD
Open the record for dataset details and reuse information.
Exploring the role of positive direct expe-rience in adoption of energy-efficient technologies
<p>The visitors of a recently refurnished public swimming pool in a Swiss city were surveyed about their experience with new low-flow showerheads, intention to take advantage of a local utility's campaign offering low-flow showerheads for home use at a highly subsidised price, socio-psychological determinants of a decision and intention to purchase a subsidised low-flow showerhead in this context and their socio-demographic background.</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.