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32 results for “survey adaptation”
Underwater surveys of mullet schools (Mugil liza) with Adaptive Resolution Imaging Sonar
<p>This dataset is part of a research project that employs deep learning, with a density-based regression approach, to count fish in low-resolution sonar images (Tarling et al. preprint arXiv DOI: http://arxiv.org/abs/2104.14964).</p> <p>In this repository, we provide data from sonar-based underwater videos of schools of migratory mullets (<em>Mugil liza</em>) recorded at the Tesoura beach (28.495775 S, 48.759996 W), a 100-meter long beach at the inlet canal connecting the Laguna lagoon system to the Atlantic Ocean, in southern Brazil. Since the water transparency at the lagoon canal is very low (from 0.3 to 1.5m visibility; collected <em>in situ</em> with a Secchi disk), mullet schools were recorded by deploying an Adaptive Resolution Imaging Sonar, ARIS 3000 (Sound Metrics Corp, WA, USA), which uses 128 beams to project a wedge-shaped volume of acoustic energy and convert their returning echoes into a digital overhead view of the mullet schools.</p> <p>This dataset contains 500 fully annotated images that were manually marked for the location and abundance of mullet fish, and 126 raw sonar video files, representing over 100k images. The files are organized as follows:</p> <p>1) "2018-MM-DD_HHMMSS" files are mp4 videos (you may need to add the file extension ".mp4"): There are 126 ARIS files converted into MP4 videos totalling over 789MB of underwater footage captured at 3 frames/seconds. Note that file names indicate the date and time the video was recorded.</p> <p>2) ".npy" files (in Labelled_data.zip): From the video files, 500 images were selected for labelling. Images (x) were cropped to represent a 4x8.5m<sup>2</sup> area and resized to 320 x 576 pixels. Mullet fish were marked with a point annotation. Corresponding ground truth density maps (y) were generated by convolving a Gaussian kernel over the image mask, size =4 and standard deviation = 1. The labelled dataset was randomly split into a holdout partition of 350 training images, 70 validation, and 80 test. </p> <p>3) ".csv" files: log of frames selected for the labelled subset of data</p> <p>4) ".h5" file: pre-trained weights for our multi-task with uncertainty regularisation network</p> <p>To advance the development of these machine learning tools, we also make our code openly available (https://github.com/ptarling/DeepLearningFishCounting).</p>
Contrasting plant adaptation strategies to latitude in the native and invasive range of Spartina alterniflora: geographic survey (2014) and Common garden (2015-2017)
We examined trait differences and evolution across geographic clines among continents of the intertidal grass Spartina alterniflora within its invasive and native ranges. Between September and November 2014, we sampled vegetative and reproductive traits in the field at 20 sites over 20° latitude in China (invasive range) and 28 sites over 17° latitude in the US (native range). We grew both Chinese and US plants in a greenhouse common garden for three years (2015 - 2017) to determine if differences in performance of S. alterniflora between the introduced and native ranges were due to genetic differences or differences in abiotic conditions.
Survey answers to identify barriers and enablers to climate change adaptation solutions (as part of the Adaptation AGORA project)
<p><span>This dataset s the result of collaborative work for Deliverable 4.1 (WP4; T4.1) of the Adaptation AGORA project. This survey aimed to capture the key factors supporting or hindering adaptation practitioners experienced with engaging citizens and stakeholders in climate change adaptation initiatives. </span></p> <p><span>The survey targeted <span>European adaptation practitioners, i.e., all professionals in charge of implementing climate change adaptation initiatives, and more particularly, those involved in collaborative processes engaging stakeholders and citizens </span><span>at the local and/or regional scale.</span></span></p> <p><span><span>The survev protocol can be found here: Euro-Mediterranean Center for Climate Change, University of Geneva, Stockholm Environment Institute, Barcelona Supercomputing Center, & Agenzia per la Promozione della Ricerca Europea. (2024). Protocol to carry out surveys to identify barriers and enablers to climate change adaptation solutions. Zenodo. <a href="https://doi.org/10.5281/zenodo.13385305" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13385305</a></span></span></p>
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
<p>We present the data used in "DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection". It was also used in the conference paper presented in Machine Learning and the Physical Sciences workshop at NeurIPS 2022: "Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection".</p> <p>A plethora of AI methods, has already shown huge promise in increasing quality and speed of work with astronomical datasets, but high complexity of AI methods leads to extraction of dataset-specific non-robust features, which leads to models that cannot work on multiple datasets at the same time. We develop a Universal Domain Adaptation method <em><strong>DeepAstroUDA</strong></em>, capable of performing <strong>semi-supervised domain adaptation, that can be applied to datasets with different data distributions and class overlap</strong>. Extra classes can be present in any of the two datasets, and the method can even be used in the presence of unknown classes. We apply our model to three examples of galaxy morphology classification tasks of different complexities (3-class and 10-class problems), with anomaly detection i.e. in all our experiments we have one extra class in the unlabeled target dataset, which represents our anomaly class.</p> <p> </p> <p><strong>DATA:</strong></p> <p><strong>1) DA across two different data releases of the same survey (LSST 1 and 10 years of observation):</strong> We use data from Ciprijanovic et al. 2022. which can also be found on Zenodoo: <a href="https://zenodo.org/record/5514180#.Y6SM7y-B2_w">https://zenodo.org/record/5514180</a> . Data contains three classes: spiral (0), elliptical (1) and merging galaxies (3, anomaly class).</p> <p><strong>2) DA across two surveys (SDSS and DeCALS): </strong>We create datasets using data and labels from the Galaxy Zoo project. Datasets contain 10 classes (9 known classes present in both SDSS and DeCALS data, and one unknown anomaly class present only in DeCALS data): disturbed (0), merging (1), round smooth (2), cigar shaped smooth (3), barred spiral (4), unbarred tight spiral (5), unbarred loose spiral (6), edge-on without bulge (7), edge-on with bulge (8), lenses (9, unknown anomaly class).</p> <p>SDSS (wide filed): datasets is split into two files - sdss_1.h5, sdss_2.h5</p> <p>DeCALS: decals.zip</p> <p><strong>3) DA between wide and deep observing fields of the same survey (SDSS):</strong> We create datasets using data and labels from the Galaxy Zoo project. Datasets contain same 10 classes as in 2), with the final lens anomaly class being only present in the SDSS deep field.</p> <p>SDSS (wide filed): the same data as in 2)</p> <p>SDSS (Strip 82 deep field): sdss_stripe82.zip</p> <p>All SDSS and DECaLS files contain full datasets (train, validation and test). Exact split that we performed (0.6 : 0.2 : 0.2) can be done using the code that accompanies this publication: <a href="https://github.com/deepskies/DeepAstroUDA">https://github.com/deepskies/DeepAstroUDA</a> .</p>
Text-fig. 1 Simplified geological map of NW Portugal (adapted from Portuguese Geological Survey 1:500.000 lithological map). in Overview Of The Stratigraphy And Initial Quantitative Biogeographical Results From The Devonian Of The Albergaria-A-Velha Unit (Ossa-Morena Zone, W Portugal)
Text-fig. 1 Simplified geological map of NW Portugal (adapted from Portuguese Geological Survey 1:500.000 lithological map).
The role of evolving niche choice in herbivore adaptation to host plants: Literature survey data and R scripts for simulations
Open the record for dataset details and reuse information.
Feature Model for Scrum Adaptation - Evaluation Survey
<p>Feature Model for Scrum Adaptation - Evaluation Survey</p>
Scrum roles adaptations survey and feature model evaluation
<p>Scrum roles adaptations survey and feature model evaluation</p>
Adaptations of Scrum roles in software projects: Survey and Representation Tentative with Feature Models
<p>Vídeo short paper sobre adapatações dos papéis do Scrum</p>
Contextualized Adaptive Research Description INterfaces Applying LinkedData Evaluation Survey Responses
<p>Building adaptive web forms for publishing research datasets based on contextual information and established ontologies allows the acquisition of fine-grained, structured, semantic metadata which facilitation interdisciplinary findability and reuse.</p> <p>The provided dataset contains the responses of 74 participants in a Turtle (ttl) format from an online survey experiment where they had to use a prototypical web application (CARDINAL) as a proof-of-concept in practice based on a fictious scenario about a political election poll. The evaluation was conducted in July 2020.</p>
Survey results of the Benefits and Limitations of a Virtual Training on Climate Risks and Adaptation
<p>Survey results of the Benefits and Limitations of a Virtual Training on Climate Risks and Adaptation</p>
Survey instrument, data, and code for paper "Freihardt, Buntaine, Bernauer (2024): Choosing to protect: Public support for flood defense over relocation in climate change adaptation. Environmental Research Letters. DOI 10.1088/1748-9326/ad6781"
<p>This is the survey instrument, data, and code underlying the manuscript:</p> <p>"Freihardt, Buntaine, Bernauer (2024): Choosing to protect: Public support for flood defense over relocation in climate change adaptation. Environmental Research Letters. DOI 10.1088/1748-9326/ad6781"</p>
PrisonLIFE project - Adaptation, translation equivalence and content validity of the MQPL survey in Serbian
<p>This dataset was created as part of the PrisonLIFE project, funded by the Science Fund of the Republic of Serbia under Grant No. 7750249.</p> <p>This dataset includes information about the adaptation process, translations, and content validity assessment of the Serbian version of the Measuring the Quality of Prison Life (MQPL) survey, along with details about participants in focus groups and their feedback. The study references the works by Liebling et al. (2012) and Milićević et al. (2023).</p>
Data from: Coral cover surveys corroborate predictions on reef adaptive potential to thermal stress
<p>As anomalous heat waves are causing the widespread decline of coral reefs worldwide, there is an urgent need to identify coral populations tolerant to thermal stress. Heat stress adaptive potential is the degree of tolerance expected from evolutionary processes and, for a given reef, depends on the arrival of propagules from reefs exposed to recurrent thermal stress. For this reason, assessing spatial patterns of thermal adaptation and reef connectivity is of paramount importance to inform conservation strategies. In this work, we applied a seascape genomics framework to characterize the spatial patterns of thermal adaptation and connectivity for coral reefs of New Caledonia (Southern Pacific). In this approach, remote sensing of seascape conditions was combined with genomic data from three coral species. For every reef of the region, we computed a probability of heat stress adaptation, and two indices forecasting inbound and outbound connectivity. We then compared our indicators to field survey data, and observed that decrease of coral cover after heat stress was lower at reefs predicted with high probability of adaptation and inbound connectivity. Last, we discussed how these indicators can be used to inform local conservation strategies and preserve the adaptive potential of New Caledonian reefs.</p>
Fig. 3. Flood debris from which the sample for Appendix 1 in Coleoptera in Floods: Biotic Surveys, Fish Food, Adaptation, Reconstruction of Paleoenvironments, and Preconstruction of Neoenvironments
Fig. 3. Flood debris from which the sample for Appendix 1 was taken: Rock Creek Trail, Kensington, Maryland, USA, 21 April 2017.
Fig. 2 in Coleoptera in Floods: Biotic Surveys, Fish Food, Adaptation, Reconstruction of Paleoenvironments, and Preconstruction of Neoenvironments
Fig. 2. Jong-Seok Park and Sarah Samson collecting beetles in a temporary pond created by a flash flood in Hidalgo Co., New Mexico, USA during 2 August 2014. Insert: Beetles waiting out the flood.
Mountain- and brown hare genetic polymorphisms to survey local adaptations and conservation status of the heath hare (Lepus timidus sylvaticus, Nilsson 1831)
<p class="MsoNormal">We provide the first whole genome sequences from three specimens of the mountain hare subspecies the heath hare (<em>Lepus timidus sylvaticus</em>), along with a sample subset of two mountain hares (<em>Lepus timidus timidus</em>) and two brown hares (<em>Lepus europaeus</em>) from Sweden. The heath hare has a unique grey winter pelage as compared to other mountain hares (white) and brown hares (mostly brown), and face regional extinction, likely due to competitive exclusion from the non-native brown hare. Whole genome resequencing from the eight hare specimens was mapped to the <em>Lepus timidus</em> pseudoreference genome and used for detection of 11,363,883 polymorphic nucleotide positions. We show that genomic data can be useful for addressing local adaptations and conservation status of mountain hares and brown hares in Sweden, including unique subspecies.</p>
Adaptation and evolution of teaching method for university programming subject to the online learning environment - Student surveys dataset
<p>This dataset contains anonymized survey data of students studying Operating Systems class at the Technical University of Košice in the span of school years 2017/2018 to 2020/2021.</p>
Translation and Adaptation of Learning Organisation Survey for French Speaking Countries
ClinicalTrials.gov study NCT04130568. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Survey Assessing How Consumers Adapt Their Diet in Response to Health Claim Messaging
ClinicalTrials.gov study NCT05088863. IPD Sharing: YES. Countries: 1. Publications: 1.
ScienceDex guides
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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.