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6,025 results for “Science of science”
Cedar Creek Ecosystem Science Reserve site, station Unknown, study of aboveground net primary productivity in units of gramsPerMeterSquaredPerYear on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Cedar Creek Ecosystem Science Reserve (CDR) contains aboveground net primary productivity measurements in gramsPerMeterSquaredPerYear units and were aggregated to a yearly timescale.
Ruffed grouse (Bonasa umbellus) drumming surveys, 1987-2017, Adirondack Long-Term Ecological Monitoring Program Project No. 9 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative
The objective is to document long-term population trends of ruffed grouse in a northern hardwood ecosystem. The survey area is the Huntington Wildlife Forest, a 6,000 ha field station which receives no hunting pressure. Routes are surveyed starting an hour prior to sunrise on 2-5 mornings each year between April 14 and May 9 (occasionally later), on days when wind and rain are minimal to absent. Counts are standardized relative to weather conditions and timing. Observers count the number of individual ruffed grouse heard drumming (""drummers"") at 32-50 route stations during a 4-minute period. Trends at stations over time as well as overall drummer index are calculated and compared to independent datasets.
Seed Production Survey, 1988-2009, Adirondack Long-Term Ecological Monitoring Program Project No. 26 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York, USA
The purpose of this project is to 1) estimate number of seeds per unit area in a mature northern hardwood/mixed conifer forest stand and 2) document changes over time in selected tree and shrub seed production. Permanent seed traps were established in 1988 in two forest types. Seed traps (13.9 L [5 gal] capacity buckets) are installed 0.5 m off the ground on two metal stakes in the center of each forested plot. Buckets are open to the tree canopy and have small (< 2cm) holes near the bottom edges for drainage. Fifty collection buckets are placed approximately 30 m (100 feet) apart and distributed along painted grid lines in the Huntington Wildlife Forest Natural Area. Twenty-five plots are northern hardwood upland forest (dominated by sugar maple, American beech and yellow birch with some conifers) and 25 plots are in the mixed hardwood/conifer lakeshore forest type (dominated by red maple, yellow birch, red spruce and eastern hemlock). Tree and shrub seeds are collected annually during two periods: July to November (Fall) and November to July (Spring). The spring and autumn collections are based on tree species’ seed phenology. If a bucket was tipped over due to disturbance by a bear or some other factor, it was censored from the survey for that year. Mice or other seed predators that were physically found/present in buckets also resulted in sample censoring. Animal scat or partly-consumed seeds are not censored, as these may have fallen from the tree canopy during seed predators’ normal activities.
White-tailed Deer Population Study, 1962-2008, Adirondack Long-Term Ecological Monitoring Program by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York, USA
From 1962-2008, White-tailed deer (Odocoileus virginianus) were studied at the SUNY ESF Huntington Wildlife Forest (HWF) and adjacent private and public lands in Essex and Hamilton Counties, New York, USA. Social group membership, migration and dispersal, reproductive biology, and many other objectives were studied over the course of the study period. Deer were captured, individually marked with ear tags or streamers, fitted with radio collars (later, GPS collars), and released to be tracked for a variety of research objectives. Deer were located by visual observation, recapture, and/or their location was estimated with ground, air or tower-based radio telemetry. Physical condition of deer was recorded at capture and at subsequent recapture or visual observation select variables were documented (e.g., deer group size; presence of fawns with does). Physiological, demographic, social organization, home range and behavior data were collected. HWF is a no-hunting area but deer could be harvested if they moved to huntable parts of the study area; there was a managed hunt on HWF in 1966-1970 and in 1984 to meet deer density and forest management objectives at that time. Unmarked deer were incorporated into the dataset if they were roadkilled, harvested or otherwise encountered during field activity; these deer did not receive individual identifications but may have been incorporated into select projects.
Continuous Forest Inventory (CFI), 1970-2017, Long-term Forest Property Monitoring by State University of New York College of Environmental Science and Forestry, New York, USA
SUNY College of Environmental Science and Forestry (ESF) based in Syracuse, New York, maintains a series of Continuous Forest Inventory (CFI) permanent plots on their Forest Properties. ESF has over 700 CFI plots located on 5 different properties, four properties in the Adirondack Mountains of northern New York and one property south of Syracuse. Plots cover northern hardwood species including sugar maple, red maple, yellow birch, beech, white ash, red oak, white pine, hemlock, red spruce, and pine/softwood plantations of various species. Data is collected at ten year intervals on each property starting from initial plot establishment. Plot information collected includes: location information, slope, aspect, forest type, cutting history, and photo of plot. Tree information/measurements include (in general, trees greater than 3.6 inches diameter at breast height): tree tag number, species, tree history, diameter at breast height, sawlog height, bole height, total height, crown vigor, crown class, tree location, and tree notes. Data is collected/field checked/edited according to detailed written procedures by ESF professional staff with assistance of students. Data is collected to monitor general forest health, growth rates, mortality, and overall forest metrics. Data is used to calculate standing volumes, stocking of forest trees, carbon stocking in addition to other information. ESF Forest Properties with CFI plots:
Assessing the use of bison for savanna restoration at Cedar Creek Ecosystem Science Reserve: Soil Carbon and Nitrogen
Oak savanna is the most threatened ecosystem in Minnesota and fire, alone, is not restoring and preserving it. Our savanna restoration research started more than a half century ago in what had once been native savanna at Cedar Creek. It has shown that burning about 4 to 7 times per decade eliminates shrubs and non-savanna tree species and restores prairie grassland species. However, our 50 years of research is also showing that these frequent and intense fires are preventing oaks from regenerating. Bison are now known to be a keystone species for restoring and preserving grasslands, but their roles in savanna ecosystems remain unknown. In grasslands, bison preferentially graze the dominant warm season grasses that would otherwise outcompete wildflowers, thereby promoting plant coexistence and enhancing plant diversity. Here we propose to test whether bison grazing might promote the growth and survivorship of oak seedlings in burned savannas by reducing grass fuel for fires and by knocking back dominant grass competitors. We will maintain the existing fire frequencies and the design of the long-term burning experiment, while adding bison grazing as an additional factor in part of several burn units on the southeast side of the property. Bison will graze during the summer and early fall seasons. Grazing exclosures will be established, and oak seedlings will be planted, to test effects of bison grazing on early oak growth and survivorship. The outcomes we plan to achieve are to: (1) discover better restoration and preservation practices for savanna ecosystems; (2) determine how these practices impact savanna biodiversity; and (3) educate Minnesotans about the ecological heritage of their state, including the roles that bison, fire and biodiversity play in the functioning of savannas and other Minnesota ecosystems. We will achieve these goals and outcomes by: (1) restoring bison grazing to 200 acres of oak savanna; (2) experimentally testing whether bison grazing promot
iSCAPE Citizen Science Workshops Data
<p><strong>Dataset Description</strong></p> <p>This dataset contains all the sensor data recorded during the Citizen Science Workshops during the iSCAPE project . Several workshops were held as part of the Citizen Science activities during the project in the cities of Vantaa, Dublin, Bologna, Bottrop, Hasselt and Guildford. Each csv file contains time series data of each experiment, and the yaml files contain the lists of devices used in each site.</p> <p><strong>Sensors</strong></p> <p>The sensors used are herein referred as Citizen Kits or Smart Citizen Kits, and are a set of modular hardware components that feature a selection of low cost sensors for environmental monitoring listed below. The hardware is licensed under <a href="https://www.ohwr.org/licenses/cern-ohl/license_versions/v1.2">CERN Open Hardware License V1.2</a> and is fully described in the HardwareX Open Access publication: <a href="https://doi.org/10.1016/j.ohx.2019.e00070">https://doi.org/10.1016/j.ohx.2019.e00070</a>. The sensor documentation can be found at <a href="https://docs.smartcitizen.me">https://docs.smartcitizen.me</a> and with this DOI at Zenodo: <a href="https://doi.org/10.5281/zenodo.2555029">https://doi.org/10.5281/zenodo.2555029</a>.</p> <p>In the list below, the different sensors for the Citizen Kits are detailed, and their [CHANNELS] in the csv files above linked.</p> <p> </p> <ul> <li>Air temperature (ºC): Sensirion SHT-31 [TEMP]</li> <li>Relative Humidity (%rh): Sensirion SHT-31 [HUM]</li> <li>Noise level (dBA): Invensense ICS-434342 [NOISE_A]</li> <li>Ambient light (lux): Rohm BH1721FVC [LIGHT]</li> <li>Barometric pressure (kPa): NXP MPL3115A26 [PRESS]</li> <li>Particulate Matter PM 1 / 2.5 / 10 (µg/m3) Planttower PMS 5003 [EXT_PM_1,EXT_PM_25,EXT_PM_10]</li> </ul> <p><strong>How to find the data</strong></p> <p>Each yaml file contains the description of a test. Each test is comprised of recordings of several devices in the same location and during the same period. Each yaml file is comprised of the following fields:</p> <ul> <li>author: who has been in charge of performing the test (internal reference - not relevant)</li> <li>comment: describing in general terms what was done in the test, and with what purpose</li> <li>commit: the firmware commit (in the case of Smart Citizen devices) with which the test was performed, for development purposes only</li> <li>devices: a descriptor containing different fields for traceability (below)</li> <li>id: the test name</li> <li>project: within the test was performed, in this case it is always iscape</li> <li>report: if there is any report analysing the test</li> <li>type_test: indoor, oudoor test or other.</li> </ul> <p><strong>Description of devices entry</strong></p> <p>For each device that was used in the test, two generic types are used:</p> <ul> <li>low cost sensors (type: STATION or KIT)</li> <li>high end sensors (type: REFERENCE)</li> </ul> <p>For <strong>low cost Smart Citizen sensors</strong>, the fields are:</p> <ul> <li>alphasense: electrochemical sensors device ids, by pollutant (for manufacturer calibration) and slots in which they were placed</li> <li>device_id: device id in Smartcitizen API</li> <li>fileNameInfo: not used</li> <li>fileNameProc: (only if source = csv is specified) 2019-03_EXT_UCD_URBAN_BACKGROUND_API_CITY_COUNCIL_REF.csv</li> <li>fileNameRaw: (only if source = csv is used) raw file name</li> <li>frequency: original recording frequency</li> <li>location: for timezone correction only, not accurate</li> <li>max_date: last recording date</li> <li>min_date: first recording date</li> <li>name: self-explanatory</li> <li>pm_sensor: if there was a pm sensor connected (all of them are PMS5003 if no sensor is specified)</li> <li>source: api or csv</li> <li>type: STATION (KIT + Alphasense + PM board with two PMS5003) or KIT</li> <li>version: smartcitizen hardware version</li> </ul> <p>For <strong>high end</strong> sensors, the fields are:</p> <ul> <li>channels: which channels the device was recording for internal convertion <ul> <li>names: which are the columns in the csv file</li> <li>pollutants: which pollutants do they respectively refer to</li> <li>units: the units of these pollutants</li> </ul> </li> <li>equipment: the brand of the analyser</li> <li>fileNameProc: same as above</li> <li>fileNameRaw: same as above</li> <li>index: format in which the timeindex is done, for parsing purposes <ul> <li>format: (example '%Y-%m-%d %H:%M:%S')</li> <li>frequency: frequency at which the device was recorded</li> <li>name: column name</li> </ul> </li> <li>location: same as above</li> <li>name: name of the device</li> <li>type: REFERENCE (always for these devices)</li> <li>source: csv</li> </ul> <p><strong>iSCAPE Dataset Reference Numbers</strong></p> <p>The datasets here presented are related to the following iSCAPE dataset reference numbers:</p> <ul> <li>DS_TS_093</li> <li>DS_TS_094</li> <li>DS_TS_095</li> <li>DS_TS_096</li> <li>DS_TS_097</li> <li>DS_TS_098</li> </ul>
A glimpse into papers about research relevance in different fields of science (1974-2018)
<p><strong>Note: </strong>This is a supplementary material of the following paper:</p> <p>Vahid Garousi, Markus Borg, Markku Oivo, "Practical relevance of software engineering research: Synthesizing the community's voice", Springer Empirical Software Engineering Journal, 2020, DOI: 10.1007/s10664-020-09803-0</p>
Figure 7 in The introduced terrestrial slugs Ambigolimax nyctelius (Bourguignatı 1861) and Ambigolimax valentianus (Férussacı 1821) (Gastropoda: Limacidae) in Californiaı with a discussion of taxonomyı systematicsı and discovery by citizen science
Figure 7. Explanation of the supplementary plates, or 'Exp. Planch. supp. Plate 4A' (Férussac et al. 1820–1851).
Figure 2 in The introduced terrestrial slugs Ambigolimax nyctelius (Bourguignatı 1861) and Ambigolimax valentianus (Férussacı 1821) (Gastropoda: Limacidae) in Californiaı with a discussion of taxonomyı systematicsı and discovery by citizen science
Figure 2. Jaws of A. nyctelius (a–c, h) and A. valentianus (d–g) from sites within Los Angeles County, California imaged using SEM (a–f) and light microscopy (g, h). (a) iNat 4936336, LACM 180543; (b, h) iNat 4936344, LACM 180541; (c) iNat 4936343, LACM 180540; (d, g) iNat 4936341, LACM 180536; (e) iNat 4936340, LACM 180537; (f) iNat 4936346, LACM 180538. Images by Emily Burnett.
Figure 5 in The introduced terrestrial slugs Ambigolimax nyctelius (Bourguignatı 1861) and Ambigolimax valentianus (Férussacı 1821) (Gastropoda: Limacidae) in Californiaı with a discussion of taxonomyı systematicsı and discovery by citizen science
Figure 5. Male genitalia of A. nyctelius (a), iNat 4936338, LACM 180542, and A. valentianus (b), iNat 4936340, LACM 180537 from Los Angeles County, California. bc, bursa copulatrix; ga, genital atrium; p, phallas; pa, phallas appendix; pr, phallas retractor muscle; so, spermoviduct; vd, vas deferens. Photos by Cedric Lee.
Rewriting Diversity: Editing Wikipedia and Opening Science
<p><strong>Episode Summary:</strong></p> <p>The podcast this episode will report directly from an ‘Edit-a-thon’ that aims to celebrate International Women’s Day 2019 by improving diversity on Wikipedia pages. The podcast will investigate what the challenges to diversity in science are and how the Open Science movement can help.</p> <p><strong>Organising Institutions:</strong></p> <p><a href="https://www.mdc-berlin.de/">Max Delbrück Center for Molecular Medicine (MDC)</a>, <a href="https://www.bihealth.org/en/">Berlin Institute of Health</a><a href="https://www.bihealth.org/en/"> (BIH)</a>, <a href="https://www.charite.de/en/">Charité Universitätsmedizin Berlin,</a> and <a href="https://www.leibniz-fmp.de/home.html">Leibniz Forschungsinstitut für Molekulare Pharmakologie (FMP)</a></p> <p><strong>Interviewees:</strong></p> <p><a href="https://twitter.com/karinhoehne">Dr Karin Höhne</a></p> <p><a href="https://en.wikipedia.org/wiki/Jess_Wade">Dr Jess Wade</a></p> <p><a href="https://www.linkedin.com/in/alice-white-61897a2a/?originalSubdomain=uk">Dr Alice White</a></p> <p><a href="http://fmp-berlin.academia.edu/WingYingChow">Dr Wing Ying Chow (Ying)</a></p> <p>Steph (Attendee and Science Communicator)</p> <p><strong>Links: </strong></p> <p><a href="https://www.bbc.com/news/technology-32412121">How to Edit a Wikipedia Page</a> - BBC Article</p> <p><a href="https://wellcomelibrary.org/">Wellcome Library</a></p> <p><a href="https://en.wikipedia.org/wiki/Inferior_(book)">Inferior, Angela Saini</a> (The book that inspired Jess Wade)</p> <p><a href="https://medium.com/@denalbz/reimagining-open-science-through-a-feminist-lens-546f3d10fa65">Reimagining Open Science Through a Feminist Lens</a></p> <p><strong>Quotes: </strong></p> <p>'We have so many women doing incredible work so we just wanted to make them visible'</p> <p>'Men's work is more likely to accepted and more likely to be cited'</p> <p>'One of the things that excites me about Wikipedia is that it is a great gateway for people who might never have clicked on a journal article'</p> <p>'Anything you can do in your pyjamas is a good thing'</p>
Open Science and Career Pathways
<p><strong>Episode Summary:</strong></p> <p>In this episode we are discussing researcher career options and how open science can have career benefits. Our interview guest will be <a href="https://www.vitae.ac.uk/events/event-presenters/dr-janet-metcalfe">Dr Janet Metcalfe</a> who is the Head of Vitae, an organisation supporting researcher professional development. We will cover the challenges researchers face in modern academia, the surprising truth about changing career paths, and how Open Science can benefit research career progression. </p> <p><strong>Resources and Links:</strong></p> <ul> <li><a href="https://www.vitae.ac.uk/%20">Vitae</a></li> <li><a href="https://qz.com/547641/theres-an-awful-cost-to-getting-a-phd-that-no-one-talks-about/">Cost of Getting a PhD (Article)</a></li> </ul> <p><strong>Episode Quotes:</strong></p> <p>“People, when they are doing their PhD, they tend to measure their PhD in terms of the research results at the end of it, but actually the most important product of a PhD is the person.”</p>
Provisioning forest and conservation science with European tree species distribution models under climate change
<p>Estimating shifts in the current range of forest tree species is crucial for formulating adaptive management strategies such as assisted migration. Ecological niche models have been the most widely used tools to estimate the potential climatic suitability of species worldwide. The reliability of such estimations depends on the model algorithm and the input data such as climate and species occurrence. We developed a dataset of the potential distribution of seven ecologically and economically important tree species of Europe in terms of their climatic suitability with an ensemble approach while accounting for uncertainty due to model algorithms. The distribution models shall be the basis for follow-up studies in forest and conservation science.</p>
Grammar2PDDL: PDDL benchmark generated from data science grammar
<p>A PDDL benchmark set, generated from Data Science grammar by requiring particular rules and terminals as soft goals.</p> <p>The dataset can also be found at <a href="https://github.com/IBM/PDDL-benchmark-ds-grammar">https://github.com/IBM/PDDL-benchmark-ds-grammar</a></p>
Hindsight is 2020: Reviewing How the ORION Project Impacted Open Science
<p><strong>Episode Summary: </strong></p> <p>Season 2 is here! We start by looking back at the last year, and talking for the first time about the Open Science project that we are part of ORION. In particular, we consider the aim and impact of Open Science training and interview two participants: Malte Schäfer from TU Braunschweig and Dr Deirdre Winrow from University College Dublin, from our workshop and our MOOC. </p> <p><strong>Links:</strong></p> <ul> <li><a href="http://tiny.cc/ORIONMOOC2">Enroll in the MOOC 2.0</a></li> <li><a href="https://www.orion-openscience.eu/publications/training-materials">ORION Training Materials</a></li> <li><a href="https://www.orion-openscience.eu/activities/training">ORION Training Workshops</a></li> </ul> <p><strong>Quotes: </strong></p> <p>"He radicalised me"</p> <p>"It gives them space to reflect on Open Science"</p> <p>"The system of science has changed"</p>
A New Normal: How the Center for Open Science is Changing How Science is Done
<p><strong>Episode Summary: </strong></p> <p>In this episode we talk to Dr Brian Nosek about the work of the Center for Open Science. We discuss how to shift expectations of what is the norm is science, preregistration, networks, and the impact of a digital society on scientific practice. </p> <p><strong>Episode Links:</strong></p> <p><a href="https://cos.io/">Center for Open Science</a></p> <p><a href="https://twitter.com/OSFramework">@OSFramework</a></p> <p><a href="http://projectimplicit.net/nosek/">Brian Nosek</a></p> <p><a href="https://twitter.com/BrianNosek">@BrianNosek </a></p>
Are We Doing Good? Discussing Open Science and Scientific Practice at the Doing Good Conference
<p><strong>Episode Summary: </strong></p> <p>In this week's episode we report on a recent symposium: Doing Good: Scientific Practice Under Review. There are interviews and impressions from the organisers and attendees who discuss why Open Science is good science, what the current state of play is, and what researchers need in order to make positive changes for the better. </p> <p><strong>Episode Links:</strong></p> <p><a href="https://www.cbs.mpg.de/doing-good">Symposium Program and Slides</a></p> <p><a href="https://twitter.com/doinggood_symp">Symposium Twitter</a></p> <p><strong>Episode Quotes: </strong></p> <p>"Are we doing good? That's the question we are asking in the symposium and asking from different perspectives". </p>
Life Sciences in the Fast Lane: Dan Qunitana on Rapid Feedback, Tweeting, and Time Management
<p><strong>Episode Summary:</strong></p> <p>In the last episode of 2019 we talk to Professor Dan Quintana from the University of Oslo about the advantages of sharing preprints and ideas online, fears about getting scooped, and lessons he has learnt about Twitter and time management. </p> <p><strong>Episode Links: </strong></p> <p><a href="https://www.dsquintana.com/publication/">Dan Quintana</a></p> <ul> <li><a href="https://twitter.com/dsquintana">Dan Quintana Twitter</a></li> </ul> <p><a href="https://twitter.com/hertzpodcast">Everything Hertz Podcast</a></p> <p><a href="https://twitter.com/pb_cast">Physiology & Behavior Podcast</a></p> <p><strong>Episode Quotes: </strong></p> <p>"The great thing about social media is it is a great way to organise a lot of people who are like-minded"</p>
The FAIR is in Town: figshare, The Turing Way, and Open Science Quest at the OSFAIR2019
<p><strong>Episode Summary:</strong> </p> <p>In this episode we are highlighting some of the tools on show at the Open Science FAIR 2019 in Porto, Portugal. </p> <p><strong>Links: </strong></p> <ul> <li><a href="https://figshare.com/">figshare</a> <ul> <li><a href="https://figshare.com/authors/Alan_Hyndman/580840">Alan Hyndman</a></li> </ul> </li> <li><a href="https://the-turing-way.netlify.com/introduction/introduction">The Turing Way Book</a></li> <li><a href="https://github.com/alan-turing-institute/the-turing-way">The Turing Way GitHub </a> <ul> <li><a href="https://www.research.manchester.ac.uk/portal/rachael.ainsworth.html">Rachael Ainsworth</a></li> </ul> </li> <li><a href="https://zenodo.org/record/2646121#.XYJVASgzZPY">Open Science Quest</a> <ul> <li><a href="https://twitter.com/jonatortue">Jonathan England</a></li> </ul> </li> </ul> <p><strong>Quotes:</strong></p> <p>'So two reasons: because you have to and just because it is for the good of society'</p> <p>'Make reproducible research too easy not to do'</p> <p>'I wanted to change the way people became aware of Open Science best practices'</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.