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1,488 results for “helping”
MCR LTER: Coral Reef: Farmerfish gardens help buffer stony corals against marine heat waves, data for Honeycutt et al., PLOS One 2023
These data were generated in support of the manuscript: Honeycutt RC, Holbrook SJ, Brooks, AJ, and RJ Schmitt, PLOS One In Moorea, French Polynesia, we evaluated the response and fate of stony coral following a major thermal stress event in 2019 that caused a substantial amount of branching coral (dominantly Pocillopora) to bleach and die. We investigated whether Pocillopora colonies that occurred within territorial gardens protected by the farmerfish Stegastes nigricans were less susceptible to or survived bleaching better than Pocillopora on adjacent, undefended substrate. Bleaching prevalence and severity, which were quantified for >1,100 colonies shortly after they bleached, did not differ between colonies within or outside of defended gardens. By contrast, 399 focal colonies followed for one year revealed that a bleached coral within a garden was a third less likely to suffer complete colony death and, for survivors, about twice as likely to recover to its pre-bleaching cover of living tissue compared to Pocillopora outside of a farmerfish garden. Our findings indicate that while residing in a farmerfish garden may not reduce the bleaching susceptibility of a coral during thermal stress, it does help buffer a bleached coral against severe outcomes. This oasis effect of farmerfish gardens, where survival and recovery of thermally-damaged corals are enhanced, is another mechanism that helps explain why large Pocillopora colonies are far more abundant in farmerfish territories than elsewhere in the lagoons of Moorea, despite gardens being much less common. As such, farmerfish may have a growing role in maintaining the resilience of branching corals as the frequency and intensity of marine heat waves continue to increase. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued
Hydrodynamic field data near Galveston, Texas wetland edges to help assess storm impacts and erosion
<p>Water free surface elevation measurements via submerged pressure transducers along transects near Galveston Bay wetland edges</p>
Nursing Assignment Help | Best Writing Service By Experts
<p>Students enrolled in nursing degree and diploma programs at Australian universities find writing nursing assignments challenging and seek <a href="https://www.globalassignmenthelp.com.au/nursing-assignment-help">Nursing assignment writing services</a> in Australia.</p>
Can Artificial Intelligence help in the study of vegetative growth dynamics from herbarium collections? An evaluation of the tropical flora of the French Guiana forest
<p>Dataset was used for the article "Can Artificial Intelligence help in the study of vegetative growth dynamics from herbarium collections? An evaluation of the tropical flora of the French Guiana forest".</p> <p>The related work proposes to study to what extent the use of automated visual analysis techniques, based on deep learning, can help not only to detect relatively rare vegetative structures in herbarium collections but also to automatically classify them by type of growing shoot (continuous or rhythmic).</p> <p>Abstract of the paper:</p> <p>A better knowledge of tree vegetative growth patterns and their relationship to environmental variables is crucial in understanding forest growth dynamics and how climate change may affect them. Generally less studied than reproductive structures, the phenology of tree vegetative growth mainly focuses on the analysis of growing shoots, from vegetative buds development to leaf fall. This growth process usually strongly differs between temperate and tropical regions. In temperate regions, this pattern is quite well known. Low winter temperatures impose a stop of the vegetative growth shoots and lead to the typical expression of an annual growth cycle for the vast majority of tree species. In moist tropical regions, on the other hand, the seasonality is much less marked. In addition, these regions contain a much wider variety of tree species. These two aspects lead to a tremendous diversity of phenological patterns that are still poorly known and understood. In particular, not much is known on the periodicity and timing of growth at individual trees, population, or community levels.</p> <p>The work carried out in this study aims to advance knowledge in this area, focusing more particularly on herbarium scans, as herbarium collections offer the promise of monitoring plant phenology over long time periods. However, such a study requires the ability to detect a sufficiently large number of growing shoots in herbarium collections to draw statistically relevant conclusions, which can be very costly if the work is done manually. Furthermore, herbarium collections traditionally focus on reproductive organs, and herbarium specimens showing growing shoots are pretty rare.</p> <p>We propose in this paper to study to what extent the use of automated visual analysis techniques, based on deep learning, can help not only to detect these relatively rare vegetative structures in herbarium collections but also to automatically classify them by type of growing shoot (continuous or rhythmic). Our results show the relevance of using herbarium data for vegetative phenology research, as well as the potential of deep learning approaches for growth shoot detection.</p>
Supplementary data: Breeding wheat for organic farming: can the high grain protein gene Gpc-B1 help to tackle challenges in view of end-use quality?
<p>Agronomic and quality data of organic wheat (<em>Triticum aestivum</em>), mean comparisons and supplementary figures related to the publication "Breeding wheat for organic farming: can the high grain protein gene Gpc-B1 help to tackle challenges in view of end-use quality?" by Grausgruber et al. (2024) published in the Journal of Cereal Science.</p>
HELP Study Data Dictionary / Catalog of Items
<p>The <a href="https://doi.org/10.1136/bmjopen-2019-033391">HELP study</a> was a clinical trial in the form of a multicenter interventional randomized controlled trial (RCT) at five German university hospitals, conducted from 2020 to 2022. It aimed to enhance the clinical management of Staphylococcus bacteremia and used data both from Electronic Health Records (EHR), provided by German university hospital's Data Integration Centers in the HL7 FHIR format (German profiles of the Medical Informatics Initiative Core Data Set – <a href="https://www.medizininformatik-initiative.de/en/medical-informatics-initiatives-core-data-set">MII CDS</a>), as well as data from Electronic Case Report Forms (eCRF) used in the study for data which was too unstructered or not availabe in the EHR (hybrid data collection approach).<br>This dataset is a tabular listing and description of the data items used in the study and <em>serves (primarily) as a template for information and data modeling.</em></p>
Data to reproduce figures in "Tropical thermocline helps power Pacific equatorial upwelling"
<p>A set of netcdf include results of the energetics in the Pacific STC region. </p> <p>A jupyter notebook uses all those dataset to reproduce the main plots in the paper. Code used to compute the energetics can be found within the 'Tailleux' class inside this module: https://github.com/inciente/EastPac/blob/main/KE_tools.py</p> <p>Please feel free to reach out if you're trying to use the data, or apply the energetics framework to your own simulations.</p>
Dataset for "Cognitive behavioural therapy self-help intervention preferences among informal caregivers of adults with chronic kidney disease: an online cross-sectional survey"
<p>Data and R code used for the analysis of data for the publication: Coumoundouros et al., Cognitive behavioural therapy self-help intervention preferences among informal caregivers of adults with chronic kidney disease: an online cross-sectional survey. BMC Nephrology</p> <p><strong>Summary of study</strong></p> <p>An online cross-sectional survey for informal caregivers (e.g. family and friends) of people living with chronic kidney disease in the United Kingdom. Study aimed to examine informal caregivers' cognitive behavioural therapy self-help intervention preferences, and describe the caregiving situation (e.g. types of care activities) and informal caregiver's mental health (depression, anxiety and stress symptoms).</p> <p>Participants were eligible to participate if they were at least 18 years old, lived in the United Kingdom, and provided unpaid care to someone living with chronic kidney disease who was at least 18 years old.</p> <p>The online survey included questions regarding (1) informal caregiver's characteristics; (2) care recipient's characteristics; (3) intervention preferences (e.g. content, delivery format); and (4) informal caregiver's mental health. Informal caregiver's mental health was assessed using the 21 item Depression, Anxiety, and Stress Scale (DASS-21), which is composed of three subscales measuring depression, anxiety, and stress, respectively.</p> <p>Sixty-five individuals participated in the survey.</p> <p>See the published article for full study details.</p> <p><strong>Description of uploaded files</strong></p> <p>1. ENTWINE_ESR14_Kidney Carer Survey Data_FULL_2022-08-30: Excel file with the complete, raw survey data. Note: the first half of participant's postal codes was collected, however this data was removed from the uploaded dataset to ensure participant anonymity.</p> <p>2. ENTWINE_ESR14_Kidney Carer Survey Data_Clean DASS-21 Data_2022-08-30: Excel file with cleaned data for the DASS-21 scale. Data cleaning involved imputation of missing data if participants were missing data for one item within a subscale of the DASS-21. Missing values were imputed by finding the mean of all other items within the relevant subscale. </p> <p>3. ENTWINE_ESR14_Kidney Carer Survey_KEY_2022-08-30: Excel file with key linking item labels in uploaded datasets with the corresponding survey question.</p> <p>4. R Code for Kidney Carer Survey_2022-08-30: R file of R code used to analyse survey data.</p> <p>5. R code for Kidney Carer Survey_PDF_2022-08-30: PDF file of R code used to analyse survey data.</p>
Dataset for "Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects"
<p>Data and Code for 'Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects' by Graf et al. (Communications Earth and Environment)</p>
A role for the medial temporal lobe subsystem in guiding prosociality: the effect of episodic processes on willingness to help others
Open the record for dataset details and reuse information.
A role for the medial temporal lobe subsystem in guiding prosociality: the effect of episodic processes on willingness to help others (Experiment 2)
Open the record for dataset details and reuse information.
The Corona Connection: How LabHive and Open Science is Helping Scientists Solve COVID-19
<p><strong>Episode Summary: </strong></p> <p>The Coronavirus pandemic has led to many new initiatives to help scientists share resources and data. We talked to, Tobias Opialla and Lisa Rieble who have created a new platform called LabHive. We discussed what LabHive is and how it got started, as well as how Open Science principles and practices relate to the new normal and how communication is key. </p> <p><strong>Episode Links: </strong></p> <p><a href="https://labhive.de/#/">LabHive</a></p> <p><a href="https://wirvsvirus.org/">WirVsVirus</a></p> <p><a href="https://berlin.impacthub.net/">Impact Hub Berlin</a></p> <p><strong>Bonus links regarding Drosten, the Teachers and Kindergarteners and the BILD:</strong></p> <p>Original tweet from Drosten:<br> <a href="https://twitter.com/c_drosten/status/1264934434756755456">https://twitter.com/c_drosten/status/1264934434756755456</a> </p> <p>Replies from improperly quoted reviewers:<br> <a href="https://twitter.com/jdoeschner/status/1264948078790029313">https://twitter.com/jdoeschner/status/1264948078790029313</a> <br> <a href="https://twitter.com/domliebl/status/1264935266185293826">https://twitter.com/domliebl/status/1264935266185293826</a> <br> <a href="https://twitter.com/polenz_r/status/1264946109719379970">https://twitter.com/polenz_r/status/1264946109719379970</a> <br> <a href="https://twitter.com/christoph_rothe/status/1265344225979314177">https://twitter.com/christoph_rothe/status/1265344225979314177</a> <br> <a href="https://twitter.com/christoph_rothe/status/1264930677306413058">https://twitter.com/christoph_rothe/status/1264930677306413058</a> </p> <p>The xkcd regarding preprints:<br> <a href="https://xkcd.com/2304/">https://xkcd.com/2304/</a> </p> <p>Regarding renewed interest in Testing:<br> Drosten wanting to test schools and Kindergartens<br> <a href="https://www.ndr.de/nachrichten/info/39-Welche-Chancen-neue-Tests-bieten,podcastcoronavirus206.html">https://www.ndr.de/nachrichten/info/39-Welche-Chancen-neue-Tests-bieten,podcastcoronavirus206.html</a> </p> <p>Press Release from Berlin Senate regarding test strategy:<br> <a href="https://www.berlin.de/rbmskzl/aktuelles/pressemitteilungen/2020/pressemitteilung.935676.php">https://www.berlin.de/rbmskzl/aktuelles/pressemitteilungen/2020/pressemitteilung.935676.p</a>df </p>
Sprint for your eLife! How the eLife Innovation Sprint Helps Drive Forward Open Science Projects
<p><strong>Episode Summary</strong></p> <p>In this episode we cover the eLife Innovation Sprint 2020, that was held online on September 2nd and 3rd, 2020. The sprint facilitates collaboration between people who are working on tools, services, and other projects that enhance open science and research.</p> <p>We talk to the organiser Dr Emmy Tsang, who at the time was the Innovation Community Manager for eLife but is now the Community Engagement Manager for TU Delft. As well as participants from two of the projects that took part: Dr Cássio Amorim, creator of SciGen.Report, 'a platform to easily share and view any information that researchers may have on the reproducibility of papers'. And Esha Datta and Daniel Nüst, who who both joined the sprint to work on the Expanding Open Grants project, developed by Dr Hao Ye, which increases accessibility to examples of grants and proposals. </p> <p><strong>Episode Links: </strong></p> <p><a href="https://sprint.elifesciences.org/">eLife Sprint</a></p> <ul> <li>Emmy Tsang: @emmy_ft </li> <li>@eLifeInnovation</li> </ul> <p><a href="https://scigen.report/">SciGen.Report</a></p> <ul> <li>@ScigenReport </li> </ul> <p><a href="https://www.ogrants.org/">Expanding Open Grants</a></p> <ul> <li>@nordholmen</li> <li>The new GH org for the sprint with latest developments:<br> <a href="https://github.com/expanding-open-grants/">https://github.com/expanding-open-grants/</a></li> <li>The wrap up slides are at:<br> <a href="https://docs.google.com/presentation/d/1PlW7Iq5xOKu1kbo_CLvnYXBJ3ldPdd4ulggZJl4F2-Y/edit#slide=id.g96e132450a_0_112">https://docs.google.com/presentation/d/1PlW7Iq5xOKu1kbo_CLvnYXBJ3ldPdd4ulggZJl4F2-Y/edit#slide=id.g96e132450a_0_112</a> </li> </ul>
Data from: Biotic interactions help explain variation in elevational range limits of birds among Bornean mountains
Aim <p>Physiological tolerances and biotic interactions along habitat gradients are thought to influence species occurrence. Distributional differences caused by such forces are particularly noticeable on tropical mountains, where high species turnover along elevational gradients occurs over relatively short distances and elevational distributions of particular species can shift among mountains. Such shifts are interpreted as evidence of the importance of spatial variation in interspecific competition and habitat or climatic gradients. To assess the relative importance of competition and compression of habitat and climatic zones in setting range limits, we examined differences in elevational ranges of forest bird species among four Bornean mountains with distinct features.</p> Location <p>Bornean mountains Kinabalu, Mulu, Pueh and Topap Oso.</p> Taxon <p>Rain forest bird communities along elevational gradients.</p> Methods <p>We surveyed the elevational ranges of rain forest birds on four mountains in Borneo to test which environmental variables—habitat zone compression or presence of likely competitors—best predicted differences in elevational ranges of species among mountains. For this purpose, we used two complementary tests: a comparison of elevational range limits between pairs of mountains, and linear mixed models with naïve occupancy as the response variable.</p> Results <p>We found that lowland species occur higher in elevation on two small mountains compared to Mt. Mulu. This result is inconsistent with the expectation that distributions of habitats are elevationally compressed on small mountains, but is consistent with the hypothesis that a reduction in competition (likely diffuse) on short mountains, which largely lack montane specialist species, allows lowland species to occur higher in elevation. The relative influence of competition changes with elevation, and the correlation between lower range limits of montane species and the distribution of their competitors was weaker than in lowland species.</p> Main conclusions <p>These findings provide support for the importance of biotic interactions in setting elevational range limits of tropical bird species, although abiotic gradients explain the majority of distribution patterns. Thus, models predicting range shifts under climate change scenarios must include not only climatic variables, as is currently most common, but also information on potentially resulting changes in species interactions, especially for lowland species.</p>
Automatic plankton image classification - can capsules and filters help coping with data set shift?
<p>This data set is related to the article 'Automatic plankton image classification - can capsules and filters help coping with data set shift?' published in 'Limnology and Oceanography: Methods' by Plonus <em>et al.</em> (2021).</p> <p>The images belong to the trainings set used to train the models in the aforementioned paper (training_) and three different additional data sets which were used to evaluate the performance of the trained models in application mode (fs446_; fs466_; fs534_). The Python-Script 'separate_files.py' can be used to move all the images in different folders for each data set and class respectively.</p>
Examination of head versus body heading may help clarify the extent to which animal movement pathways are structured by environmental cues?
<p>Understanding the processes that determine how animals allocate time to space is a major challenge, althoughit is acknowledged that summed animal movement pathways over time must define space-time use. The criticalquestion is then, what processes structure these pathways? Following the idea that turns within pathways mightbe based on environmentally determined decisions, we equipped Arabian oryx with head- and body-mounted tagsto determine how they orientated their heads – which we posit is indicative of them assessing the environment– in relation to their movement paths, to investigate the role of environment scanning in path tortuosity. Aftersimulating predators to verify that oryx look directly at objects of interest, we recorded that, during routinemovement, > 60% of all turns in the animals' paths, before being executed, were preceded by a change in headheading that was not immediately mirrored by the body heading: The path turn angle (as indicated by the bodyheading) correlated with a prior change in head heading (with head heading being mirrored by subsequent turnsin the path) twenty-one times more than when path turns occurred due to the animals adopting a body headingthat went in the opposite direction to the change in head heading. Although we could not determine what theobjects of interest were, and therefore the proposed reasons for turning, we suggest that this reflects the use ofcephalic senses to detect advantageous environmental features (e.g. food) or to detect detrimental features (e.g.predators). The results of our pilot study suggest how turns might emerge in animal pathways and we proposethat examination of points of inflection in highly resolved animal paths could represent decisions in landscapesand their examination could enhance our understanding of how animal pathways are structured.</p>
Help Me study! Music Listening Habits While Studying (Dataset)
<p>This repository contains the raw data used for a research study that examined university students' music listening habits while studying. There are two experiments in this research study. Experiment 1 is a retrospective survey, and Experiment 2 is a mobile experience sampling research study.</p><p>This repository contains five Microsoft Excel files with data obtained from both experiments. The files are as follows:</p><ul><li><i>onlineSurvey_raw_data.xlsx</i></li><li><i>esm_raw_data.xlsx</i></li><li><i>esm_music_features_analysis.xlsx</i></li><li><i>esm_demographics.xlsx</i></li><li><i>index.xlsx</i></li></ul><p><strong>Files Description</strong></p><p><i><strong>File: onlineSurvey_raw_data.xlsx</strong></i></p><p>This file contains the raw data from Experiment 1, including the (anonymised) demographic information of the sample. The sample characteristics recorded are:</p><ul><li>studentship</li><li>area of study</li><li>country of study</li><li>type of accommodation a participant was living in</li><li>age</li><li>self-identified gender</li><li>language ability (mono- or bi-/multilingual)</li><li>(various) personality traits</li><li>(various) musicianship</li><li>(various) everyday music uses</li><li>(various) music capacity</li></ul><p>The file also contains raw data of responses to the questions about participants' music listening habits while studying in real life. These pieces of data are:</p><ul><li>likelihood of listening to specific (rated across 23) music genres while studying and during everyday listening.</li><li>likelihood of listening to music with specific acoustic features (e.g., with/without lyrics, loud/soft, fast/slow) music genres while studying and during everyday listening.</li><li>general likelihood of listening to music while studying in real life.</li><li>(verbatim) responses to participants' written responses to the open-ended questions about their real-life music listening habits while studying.</li></ul><p><i><strong>File: esm_raw_data.xlsx</strong></i></p><p>This file contains the raw data from Experiment 2, including the following variables:</p><ul><li>information of the music tracks (track name, artist name, and if available, Spotify ID of those tracks) each participant was listening to during each music episode (both while studying and during everyday-listening)</li><li>level of arousal at the onset of music playing and the end of the 30-minute study period</li><li>level of valence at the onset of music playing and the end of the 30-minute study period</li><li>specific mood at the onset of music playing and the end of the 30-minute study period</li><li>whether participants were studying</li><li>their location at that moment</li><li>(if studying) whether they were studying alone</li><li>(if studying) the types of study tasks</li><li>(if studying) the perceived level of difficulty of the study task</li><li>whether participants were planning to listen to music while studying</li><li>(various) reasons for music listening</li><li>(various) perceived positive and negative impacts of studying with music</li></ul><p>Each row represents the data for a single participant. Rows with a record of a participant ID but no associated data indicate that the participant did not respond to the questionnaire (i.e., missing data).</p><p><i><strong>File: esm_music_features_analysis.xlsx</strong></i></p><p>This file presents the music features of each recorded music track during both the study-episodes and the everyday-episodes (retrieved from Spotify's "Get Track's Audio Features" API). These features are:</p><ul><li>energy level</li><li>loudness</li><li>valence</li><li>tempo</li><li>mode</li></ul><p>The contextual details of the moments each track was being played are also presented here, which include:</p><ul><li>whether the participant was studying</li><li>their location (e.g., at home, cafe, university)</li><li>whether they were studying alone</li><li>the type of study tasks they were engaging with (e.g., reading, writing)</li><li>the perceived difficulty level of the task</li></ul><p><i><strong>File: esm_demographics.xlsx</strong></i></p><p>This file contains the demographics of the sample in Experiment 2 (N = 10), which are the same as in Experiment 1 (see above).</p><p>Each row represents the data for a single participant. Rows with a record of a participant ID but no associated demographic data indicate that the participant did not respond to the questionnaire (i.e., missing data). </p><p><i><strong>File: index.xlsx</strong></i></p><p>Finally, this file contains all the abbreviations used in each document as well as their explanations.</p>
Updated Supplementary Figures for Can leafhoppers help us trace the impact of climate change on agriculture?
<p>Supplementary Figures for: <strong>Can</strong> <strong>leafhoppers help us trace the impact of climate change on agriculture? </strong>to be posted in bioRxiv.</p><p><strong>Figure S1. </strong>Diversity indexes calculated in this study to compare leafhopper diversity each growing season investigated in this study and the geographic regions where the strawberry fields were located. Statistical analyses were performed for Shannon and Simpson finding that in both cases there is no interaction between years and regions with <i>p</i> = 0.0889 and <i>p</i> = 0.7139, respectively.</p><p><strong>Figure S2.</strong> Distinctive RFLP patterns obtained with <i>Cpn</i>ClassiPhyR from <i>in silico</i> digestion of <i>cpn60</i>UT from SbGPQ clones and AY-Col. Lanes labelled MW in <i>in silico</i> RFLP represent <i>Hae</i>III-digested phage <i>ϕ</i>X174 DNA.</p><p><strong>Figure S3.</strong> Phylogenetic tree using neighbour-joining method of the <i>16S, secY, nusA, rp, secA, cpn60 </i>and<i> tuf</i> sequences obtained in this study for the SbGP phytoplasma and sequences retrieved from Genbank. <i>Acholeplasma laidlawii</i> PG8 was used as an outgroup. The phylogenetic tree was bootstrapped 1000 times to achieve reliability. Bar, 1 substitution in 100 or 500 positions. </p><p><strong>Fig. S3 Panel 1: </strong>cpn60UT, tuf, and secY trees.</p><p><strong>Fig. S3 Panel 2:</strong> nusA, rp, and secA trees.</p><p><strong>Fig. S3 Panel 3:</strong> 16S tree with subtree showing heterogeneity of SbGPQ and 'Ca. P. tritici'.</p><p><strong>Figure S4.</strong> Leafhopper feeding-associated damages observed in strawberry plants. <strong>A</strong>, in the field. <strong>B</strong>, in the greenhouse after incubation with leafhoppers.</p><p><strong>Figure S5.</strong> Alpha diversity indexes were calculated to study <i>Macrosteles quadrilineatus</i> microbiome observed for each growing season. No statistical difference was observed among the sites for any of the indexes calculated.</p><p><strong>Figure S6.</strong> Effect of insecticides leafhopper population control. Only those with a number of applications higher or equal to five are presented. We did not find statistical differences among the treatments before and after the application of the insecticides with <i>p</i> = 0.8488.</p><p><strong>Figure S7.</strong> Effect of insecticides on <i>Macrosteles quadrilineatus</i> and <i>Empoasca fabae</i> population control. All insecticides (n = 12) are represented but the statistical analysis was only performed with those that the number of applications was higher than 5. We did not find statistical differences among the treatments before and after the application of the insecticides with <i>p</i> = 0.1781 for the aster leafhopper <i>M.</i> <i>quadrilineatus </i>and <i>p</i> = 0.6540 for the potato leafhopper <i>E. fabae</i>.</p><p><strong>Figure S8.</strong> Comparison among the Shannon index obtained for leafhopper populations in vineyards in 2007 and 2008 and for leafhopper populations in strawberry fields in 2021 and 2022 in Quebec.</p>
FIG. 10 in Resampling Bouché's historical localities reveals three new species and helps identifying a new genus of earthworms (Oligochaeta, Hormogastridae and Lumbricidae) in Southeastern France
FIG. 10. — Pre-clitellar nephridia of Vignysa callasensis Gérard, Decaëns & Marchán, n. sp. Scale bar: 1 mm. Photo: D. F. Marchán.
FIG. 4 in Resampling Bouché's historical localities reveals three new species and helps identifying a new genus of earthworms (Oligochaeta, Hormogastridae and Lumbricidae) in Southeastern France
FIG. 4. — Pre-clitellar nephridia of Flabellodrilus luberonensis Gérard, Decaëns & Marchán, n. gen., n. sp. Scale bar: 1 mm. Photo: D. F. Marchán.
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.