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.
286
datasets available to search
ShareScore release 0.9.0
Dataset results
286 results for “data mobilization.”
Reef Life Survey data (fish and mobile invertebrate counts) from Carrie Bow Cay, Belize, 2015-2019
<p><strong>Aim</strong></p> <p>Conservation efforts have traditionally addressed the threat of biodiversity loss by prioritizing regions and habitats with high endemic species richness. However, species-poor habitats often harbor distinct, valuable, and/or functionally unique species that contribute to regional diversity. In the tropical marine realm, the "mangrove-seagrass-coral reef continuum" has dominated both the scientific literature and marine spatial planning. Bare sediment habitats are also part of this continuum and often comprise the majority of bottom area, as they do in most regions worldwide, but are understudied and rarely considered in conservation. To address this information gap, we partition the relative contributions of five habitats in the coastal mosaic (forereefs, patch reefs, seagrass, mangroves, and bare sand) to fish diversity across a tropical seascape.</p> <p><strong>Location</strong></p> <p>Carrie Bow Cay, Belize Barrier Reef</p> <p><strong>Methods</strong></p> <p>Using diver visual censuses, we assessed fish community composition at three replicate sites from each of the five habitats annually for five years. We then partitioned diversity into within (α) and between (β) habitat contributions, and further partitioned β-diversity into components attributed to species replacement and richness difference. Finally, we determined the local contributions to β-diversity (LCBD) by each habitat and identified the key individual species behind these contributions.</p> <p class="MsoNormal"><strong>Main Results</strong><br>Tropical sand flats were unique habitats, contributing disproportionately to total fish species diversity across the seascape, despite having the fewest species overall. Sand flats also had higher-than-expected local contributions to β-diversity (LCBD) and had the highest percentage of species not found in other habitats.</p> <p><strong>Main Conclusions</strong></p> <p>Our results demonstrate that previously understudied sand habitats harbor a relatively unique assemblage of species compared with other nearby habitats. Marine spatial planning aimed at maximizing preservation of total biodiversity (including unique species) in the tropics should consider sand habitats.</p>
Madrid city mobility data
<p>This file includes mobility data in the form of the total number of travels between districts for the city of Madrid. The quantities are daily and span from February 2020 until May 2021. The data have been curated, being the original raw data extracted from the web of the Spanish Ministry of Transport https://www.mitma.gob.es/ministerio/covid-19/evolucion-movilidad-big-data (In Spanish).</p>
Supplementary material 1 from: Vohland K, Hoffmann A, Underwood E, Weatherdon L, Bonet F, Häuser C, Wetzel F (2016) 3rd EU BON Stakeholder Roundtable (Granada, Spain): Biodiversity data workflow from data mobilization to practice. Research Ideas and Outcomes 2: e8622. https://doi.org/10.3897/rio.2.e8622
3rd EU BON Stakeholder Roundtable – Acronyms
Mobile Ocean Bottom Seismometer Instrument (MOBSI) data from Arctic rivers, lakes, and seas
<p>We collected the data using the Mobile Ocean Bottom Seismic Instrument (MOBSI). The MOBSI is portable (9 kg) and consists of a watertight, heavy-duty pressure housing (diameter 30 cm, height 35 cm) containing a three-component, broadband seismic sensor. There are three channels (two horizontal and one vertical) recorded by the built-in data logger to monitor ambient seismic noise. Deployed by a steel cable from a small boat or zodiac, the device was lowered to the bottom of the water bodies or the top of the beach area (if present). The device recorded ambient seismic noise for a few minutes before being retrieved and moved to a new location. The steel cable was outfitted with a communications cable, which allowed for live data analysis and quality control via a shipboard monitor. This ensured that the tilt of the instrument was correct (less than 5 degrees) and that the deployment time was sufficient. We stopped data collection when the "real-time" H/V ratio was stable, i.e., not changing with time. For all soundings, the sampling frequency was set to 100 samples per second. The MOBSI data was recorded in an internal RAW format that was converted to MINISeed format. In this paper, we present MOBSI data for seven survey lines from the Russian and the Canadian Arctic. The Banja Lake and Lena River profiles are situated in the Lena Delta, northeastern Siberia, Russia. Ivashkina Lagoon is located along the southern coastline of the Bykovsky Peninsula, northeastern Siberia, Russia. The Tuktoyaktuk profiles are located north of Tuktoyaktuk Island (Northwest Territories, Canada) in the Canadian Beaufort Sea. The survey lines presented in this manuscript are the following:</p> <p>Profile A-A' (Banja Lake): 8 soundings</p> <p>Profile B-B' (Banja Lake): 9 soundings</p> <p>Profile C-C' (Banja Lake): 9 soundings</p> <p>Profile D-D' (Lena River-Samoylov Island): 9 soundings</p> <p>Profile E-E' (Lena River-Chay-Tumus): 16 soundings</p> <p>Profile F-F' (Tuktoyaktuk Island): 10 soundings</p> <p>Profile G-G' (Tuktoyaktuk Island): 9 soundings</p> <p>Profile H-H' (Ivashkina Lagoon): 20 soundings</p> <p>The Excel file "MOBSI_soundings.xlsx" provides an overview of all the soundings, whereby each tab is split by the survey line. The latitude, longitude, water depth, and recording time for each sounding is provided.</p>
Raw data from Mobile scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the raw data from the Mobile scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p> <p>This dataset also has a second part that contains the raw audio data recorded in this scenario. That part of the dataset is access controlled, see <a href="http://dx.doi.org/10.5281/zenodo.2537984">this deposit</a> for more details.</p>
UTAUT Mobile Health Apps Measurement Data
Open the record for dataset details and reuse information.
Data from: Feeding ecology underlies the evolution of cichlid jaw mobility
The fish feeding apparatus is among the most diverse functional systems in vertebrates. While morphological and mechanical variation of feeding systems are well studied, we know far less about the diversity of the motions that they produce. We explored patterns of feeding movements in African cichlids from Lakes Malawi and Tanganyika, asking whether the degree of kinesis is associated with dietary habits of species. We used geometric morphometrics to measure feeding kinesis as trajectories of shape change, based on 326 high-speed videos in 56 species. Cranial morphology was significantly related to feeding movements, both of which were distributed along a dietary axis associated with prey evasiveness. Small-mouthed cichlids that feed by scraping algae and detritus from rocks had low kinesis strikes, while large-mouthed species that eat large, evasive prey (fishes and shrimps) generated the greatest kinesis. Despite having higher overall kinesis, comparisons of trajectory shape (linearity) revealed that cichlids that eat mobile prey also displayed more kinematically conserved, or efficient, feeding motions. Our work indicates that prey evasiveness is strongly related to the evolution of cichlid jaw mobility, suggesting that this same relationship may explain the origins and diversity of highly kinetic jaws that characterize the super-radiation of spiny-rayed fishes.
Burkina Faso mobility data with some noise
<p>Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.</p>
Figure 9 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 9 - Timeline. We will prepare and update the Web app (Aim 2) as we develop it for use in annotating gold standard audio data (Aim 1) and as we get feedback on its use in connection with Amazon's Mechanical Turk (Aim 2). Year 2 will consist primarily of testing the aggregation of annotated audio data for further analysis (Aim 2), to train an automated approach (Exploratory Aim), and to publish and present our findings.
Figure 2 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 2 - Mockup of audio recording annotation tool – Step 1: Selection. This figure shows a mockup of what an audio annotation Web application tool could look like. In this first step, (A) the Worker presses the Play icon to listen to the voice recording, (B) selects a problematic segment by clicking and dragging the mouse over the waveform, and (C) replays the recording if necessary and selects other problematic segments.
Figure 5 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 5 - DARPA-funded seedling project. This schematic represents our DARPA-funded seedling project to assess the feasibility of collecting phone voice recordings from PD patients for use in a competition.
Figure 4 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 4 - Audio recording annotation tool – Step 3: Rating. Following Figures 2 and 3, here the Worker rates how serious the problem is that is affecting the highlighted segment of the recording. In this example, the Worker indicates that the background noise (wind) is not good, but that it doesn't interfere with his/her ability to hear the voice in the recording.
Figure 7 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 7 - Example mPower patient voice data. In the mPower app, PD patients are prompted to perform the voice activity three times per day: once before taking their medication, a second time when they feel they are at their best after taking their medication, and a third "random" time. This figure shows example voice data for a single patient on medication (top) and at a "random" time, very likely off medication (bottom). On the left are waveforms, showing the acoustic voice signal over time (0-10 seconds), from which one can clearly see that the patient's voice trailed off to a minimum (bottom left) compared to after medication (top left). On the right are spectrograms, representing signal amplitude at different frequencies (0-5 kHz) over time (0-10 seconds). The spectrogram after medication (top right) has more uniform frequency bands across the recording compared to the rather "muddled" spectrogram recorded at the random time (bottom right).
Figure 3 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 3 - Audio recording annotation tool – Step 2: Annotation. Following Figure 2, here the Worker selects one or more categories describing why the highlighted segment in the audio waveform is problematic. In this example, there was a lot of background noise (wind).
Figure 6 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 6 - Android and iOS Parkinson app screenshots. Top: Android PD app screenshots showing instructions for the phonation (voice) task. Bottom: mPower PD app screenshots. Each participant in the mPower study is prompted to perform a voice activity three times a day. The rightmost screenshot demonstrates the visual feedback that is provided during audio recording, to try to keep the voice at the best amplitude for recording.
Figure 6 from: Vohland K, Hoffmann A, Underwood E, Weatherdon L, Bonet F, Häuser C, Wetzel F (2016) 3rd EU BON Stakeholder Roundtable (Granada, Spain): Biodiversity data workflow from data mobilization to practice. Research Ideas and Outcomes 2: e8622. https://doi.org/10.3897/rio.2.e8622
Figure 6 - Participants of the 3rd EU BON Stakeholder Roundtable discussing details of the workflow (credits: Katrin Vohland).
Figure 2 from: Vohland K, Hoffmann A, Underwood E, Weatherdon L, Bonet F, Häuser C, Wetzel F (2016) 3rd EU BON Stakeholder Roundtable (Granada, Spain): Biodiversity data workflow from data mobilization to practice. Research Ideas and Outcomes 2: e8622. https://doi.org/10.3897/rio.2.e8622
Figure 2 - Simplified workflow from data mobilization via processing to stakeholders from the practice.
Figure 1 from: Vohland K, Hoffmann A, Underwood E, Weatherdon L, Bonet F, Häuser C, Wetzel F (2016) 3rd EU BON Stakeholder Roundtable (Granada, Spain): Biodiversity data workflow from data mobilization to practice. Research Ideas and Outcomes 2: e8622. https://doi.org/10.3897/rio.2.e8622
Figure 1 - EU BON Work Packages (WP) with the three sections (a) Data Sources and Infrastructure, (b) Science and Application and (c) Policy and Dialogue. The Stakeholder Roundtables are a specific task in the WP 6 that targets the stakeholder engagement and science-policy dialogue (credits: Pensoft).
Figure 5 from: Vohland K, Hoffmann A, Underwood E, Weatherdon L, Bonet F, Häuser C, Wetzel F (2016) 3rd EU BON Stakeholder Roundtable (Granada, Spain): Biodiversity data workflow from data mobilization to practice. Research Ideas and Outcomes 2: e8622. https://doi.org/10.3897/rio.2.e8622
Figure 5 - Some exemplified results from the questionnaire send around in advance. Left: provision of data. right: Data requirements. N=20 (Florian Wetzel, MfN, 2015).
Estimation of methane emissions from the U.S. ammonia fertilizer industry using a mobile sensing approach (supplemental data)
Open the record for dataset details and reuse information.
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.