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8,460 results for “MONITORING”
Antimicrobial resistance monitoring results complementing the EU Overview Summary Report on AMR in zoonotic and indicator bacteria from humans, animals and food in 2017/2018 - Finland
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: AMR_2018_FI_20200204: >> Finnish Food Authority</p>
Antimicrobial resistance monitoring results complementing the EU Overview Summary Report on AMR in zoonotic and indicator bacteria from humans, animals and food in 2017/2018 - Norway
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: AMR_2018_NO_20200204: >> Norwegian Veterinary Institute</p>
Bioeconomy policy and monitoring inventories (DBFZ)
<p>STAR-ProBio_CertificationCriteria_v1.0 _Spreadsheet.xlsx is the tool for the analysis of the indicators and criteria used by existing international, European and national sustainability schemes for biomass and bioenergy</p> <p>STAR-ProBio_T9.1BEPoliciesMatrix_v1.0_Spreadsheet.xlsx is the inventory of ~50 BIO-BASED ECONOMY policy documents including a brief characterisation and analysis regarding their potential impact on sustainability assessment activities</p> <p>STAR-ProBio_T9.4BEMonitoringActivities_1.0_Spreadsheet.xlsx is the matrix with a description of BIO-BASED ECONOMY monitoring activities (with both, public and private activities) from EU and EU member states. The overview includes a brief characterisation of the activities, describing the type of data reported, reporting formats and periods, etc.</p>
Implementation of the wildlife monitoring standard on excel
<p>Enetwild consortium developed a data standard on wildlife monitoring data to aggregate raw data such as occurrences, drive hunt hunting bags, distance sampling data, or aggregated or estimated data such as density estimates, summarized hunting bags, probability of presence. This standard is based on the Darwin Core standard, using the Event core, the Occurrence extension, the extended measurement or fact extension. We proposed to extend the measurement or fact extension to allow them to be nested among themselves for recording confidence intervals and precision information. We offer the possibility to nest occurrences within each other as well. We propose controlled vocabularies in relation to statistical information, in both data and metadata. In addition to a single metadata sheet, this Excel file proposes two ways to enter a dataset. The first option is to record events, occurrences, and measurements in different sheets, which is similar to the option proposed by GBIF in its Integrated Publishing Toolkit. The second option is to record events, occurrences and measurements in a single sheet (data_AllInOne) with the help of conditional formatting, working on the most recent version of Excel.</p> <p>This is in line with the EFSA data model harmonisation under the SIGMA project.</p>
Supposed "snake specialist" consumes monitor lizards: diet and trophic implications of king cobra feeding ecology
<p>King cobra predation events on <em>Varanus nebulosus. </em>Included: photographic evidence of each event, king cobra biometric data from most recent measurements and location information.</p> <p>.csv file column headings:</p> <p>folderid: The Zenodo folder ID containing the photographic evidence of event.</p> <p>obvdate: Date of observation (yyyy-mm-dd)</p> <p>obvtime: Time of observation (24hr)</p> <p>snakeid: Unique ID given to individual king cobras captured</p> <p>svlmm: King cobra snout-to-vent length (mm)</p> <p>tlmm: King cobra tail length (mm)</p> <p>totalmm: King cobra total length (mm)</p> <p>massg: King cobra mass (g)</p> <p>easting: UTM easting (UTM Zone 47N; Datum WGS84)</p> <p>northing: UTM northing (UTM Zone 47N; Datum WGS84)</p> <p>gpsaccm: Accuracy of GPS location (m)</p> <p>notes: Comments on predation event</p>
Monitoring data for railway bridge KW51 in Leuven, Belgium, before, during, and after retrofitting
<p>This data set is obtained from a monitoring campaign of 15 months on a steel bowstring railway bridge in Leuven, Belgium. In this period, the connections of the diagonals to the bridge deck and the arches were strengthened after the observation of damage. The monitoring consists of measurements of acceleration on the bridge deck and the arches, strain on the bridge deck and the diagonals connecting the bridge deck with the arches, strain on the rails, displacement at the bearings, and temperature and relative humidity. Data is provided for two train passages and six periods of ambient vibration per day. In addition, the evolution of the identified modal characteristics over time is added. The data set is well-suited for the validation of vibration-based structural health monitoring methods, as it includes data for different states of the structure, i.e. before, during, and after the retrofitting. </p>
Supplementary material 1 from: Crookes S, Heer T, Castañeda RA, Mandrak NE, Heath DD, Weyl OLF, MacIsaac HJ, Foxcroft LC (2020) Monitoring the silver carp invasion in Africa: a case study using environmental DNA (eDNA) in dangerous watersheds. NeoBiota 56: 31-47. https://doi.org/10.3897/neobiota.56.47475
Table 1
Figure 4. Optimal maximum-likelihood tree resulting from the RAxML analysis. Bootstrap support values greater than 50 in Taxonomy of Micronesian monitors (Reptilia: Squamata: Varanus): endemic status of new species argues for caution in pursuing eradication plans
Figure 4. Optimal maximum-likelihood tree resulting from the RAxML analysis. Bootstrap support values greater than 50% are shown on the nodes. Scale bar corresponds to the mean number of nucleotide substitutions per site.
Codes and Dataset for "Application of Low-Cost Fine Particulate Mass Monitors to Convert Satellite Aerosol Optical Depth to Surface Concentrations in North America and Africa"
<p>This file contains the codes and dataset pertaining to the manuscript "Application of Low-Cost Fine Particulate Mass Monitors to Convert Satellite Aerosol Optical Depth to Surface Concentrations in North America and Africa"</p>
MOVECARE Monitoring Dataset
<p>MoveCare (Multiple-actOrs Virtual Empathic CARgiver for the Elder) is a research and innovation project funded by the European Commission under the Horizon 2020 Programme. MOVECARE develops and field-tests an innovative multi-actor platform that integrates an existing robotic system with domotic sensors, smart objects, a virtual community and an activity center to provide assistance, transparent monitoring and activities to the elder at home through a full-fledged, modular, personalized, and compliant approach that does not require the elder to wear any particular device or to change his/her habits.</p> <p>This dataset contains the heterogeneous monitoring data collected by the different monitoring systems throughout development and testing for the assessment of the user's condition. The dataset contains the following files: </p> <p>1.EXG - Grip force</p> <p>2.BWT - Body weight</p> <p>3.STB - Standalone use of smart objects</p> <p>4.PEN - Smart pen</p> <p>5.ALL - Neuropsycological tests</p> <p>6.VoiceAnalysis - Voice analysis</p> <p>7.PTB - Pressure sitting on couch</p> <p>8.SMP - Mobile use</p> <p>9.DMW - Movement detection</p> <p>10. SQ - Spot Questions</p> <p>More information about the dataset can be found in the attached file MOVECARE DATA MODEL. The files are anonymized versions of data values ready to use from the Movecare Data Model. </p>
BES LTER Base Cation Data for Core Monitoring Streams across Land Use Gradient
<p>The Baltimore Ecosystem Study LTER has established a network of long-term biogeochemical hydrologic study sites. Sites range from suburban to highly urban. More site information can be found on the BESLTER page at www.beslter.org </p> <p>Descriptions of land use and further site descriptions can be found in Kaushal et al 2017. Tabs represent a different sites, listing the base cation concentrations, as well as a tab summarizing site averages as used in Kaushal et al. 2017.</p>
Using Seismic Noise Levels to Monitor Social Isolation: An Example from Rio de Janeiro, Brazil
<p>Paper</p> <p>Dias, F.L., M. Assumpção, P.S. Peixoto, M.B. Bianchi, B. Collaço, J. Calhau (2020), <strong>Using Seismic Noise Levels to Monitor Social Isolation: </strong><strong>An Example from Rio de Janeiro, Brazil. </strong><em>Geophysical Research Letters.</em></p> <p>1) Daily median particle velocity amplitudes for station ON.ON02. Each amplitude is the median of all values between 09 AM - 6 PM local time.</p> <p>median_VEL_4_14_Hz.csv = median daily velocity (m/s) in the 4-14Hz frequency band.</p> <p>median_VEL_8_14_Hz.csv = median daily velocity (m/s) in the 8-14Hz frequency band. Data used in Fig. 2.</p> <p>median_VEL_4_8_Hz.csv = median daily velocity (m/s) in the 4-8Hz frequency band. Data used in Fig. 4.</p> <p>The raw data from station ON.ON02 can be retrieved from the RSBR (Brazilian Seismic Network) database at www.rsbr.gov.br</p> <p>2) <em>In Loco</em> Isolation index for Rio de Janeiro city. Data provided by <em>In Loco</em> company on 2020 April 22.</p> <p>InLoco_IsolationIndex_RioDeJaneiroCity.txt = date and <em>Ik</em> index for Rio de Janeiro city.</p> <p>Data used in Figs. 2, 4 and 5.</p>
Stable carbon, oxygen and nitrogen isotopes in tree rings from temperate forests within long-term monitoring network in the UK
<p>Supporting dataset for the paper "Climate and atmospheric deposition effects on forest water‑use efficiency and nitrogen availability across Britain" by Guerrieri et al. 2020 Scientific Reports DOI is 10.1038/s41598-020-67562-w (<a href="https://www.nature.com/articles/s41598-020-67562-w">https://www.nature.com/articles/s41598-020-67562-w</a>)</p> <p>The dataset includes carbon, oxygen and nitrogen stable isotope composition measured in tree rings for four species at twelve forests across climate and atmospheric deposition gradients in Britain. Data were used to i) investigate spatial and temporal (over 30‑years) changes in tree intrinsic water use efficiency (iWUE, i.e., the ratio between CO<sub>2</sub> assimilation - A, and stomatal conductance- g<sub>s</sub>); ii) assess the possible species-specific physiological mechanisms (changes in A and/or g<sub>s</sub>) evaluate whether sites receiving high Ndep experience increase in ecosystem N availability and N saturation (using tree-ring nitrogen isotope composition as a proxy); iv) elucidate drivers of spatial and temporal changes in the isotope-derived physiological and ecological processes (i.e., relative contribution of climate vs. changes in atmospheric CO<sub>2</sub> and nitrogen and sulfur deposition). </p> <p>The following files are available:</p> <ul> <li>CSV file including: a) Site names; b) Species; c) Latitude; d) Longitude; e) Investigated years (1980-2010); f) Measured isotope-related parameters and tree ring nitrogen %. Isotope-related parameters included in the dataset are: 1) delta13C, 2) carbon isotope discrimination (Delta13C), 3) intercellular CO2 concentration (ci) and the ratio of intercellular to atmospheric CO2 concentrations (ci/ca), delta18O, estimated delta18O in precipitation (delta18OP) according to Barbour et al. 2001 (see Method in Guerrieri et al. 2020 Scientific Reports), oxygen isotope discrimination above the source water (Delta18O), delta15N. </li> <li>PDF file reporting the equations used to calculate the isotope-derived parameters, which can also be found in the paper by Guerrieri et al. 2020 Scientific Reports (accepted)</li> </ul>
Daily cross-correlation functions for "Time-lapse monitoring of seismic velocity associated with 2011 Shinmoe-dake eruption using seismic interferometry: an extended Kalman filter approach"
<p>The daily cross-correlation functions used in Nishida et al. 2020. We used three-component seismograms recorded at eight stations (six broadband sensors and two short-period sensors with a natural frequency of 1 Hz) from May 1st, 2010 to April 30th, 2018. Five stations were deployed by the Earthquake Research Institute, the University of Tokyo, and the other three were deployed by the National Research Institute for Earth Science and Disaster Prevention (NIED). The data can be found in the HDF5 file. You can also find a python code of an implementation of an extended Kalman filter/smoother for time-lapse monitoring of seismic velocity at GitHub (https://github.com/qnishida/eKlfS). The code estimates the temporal change in seismic velocities using this data set. </p>
Point Abbaye Stopover Habitat Monitoring Audio Files
<p>Audio files from passive acoustic monitoring of bird migration stopover habitat. Files were created during a 2019 field season on the Point Abbaye peninsula in Baraga County, Michigan, USA. See 001README.txt for more details about the origin of audio files.</p>
Application of spectral library prediction for parallel reaction monitoring of viral peptides_PRM_data
<p><strong>Project description: </strong></p> <p>A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1,529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Additionally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab.</p> <p> </p> <p><strong>Sample processing protocol:</strong></p> <p>A total of 1,569 crude synthetic viral peptides were ordered in six pools from JPT (Berlin, Germany). Synthetic peptides were separated on a 200 cm μPAC™ column (PharmaFluidics) by using an EASY-nLC1200 system (Thermo Fisher Scientific) equipped with a μPAC™ trapping column (PharmaFluidics). The flow rate was set to 300 nL/min and a stepped linear 160 min gradient was applied: 3-10% B in 22 min, 10-33%B in 95 min, 33-49% B in 23 min, 49-80% B in 10 min and 80% B for 10 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive Plus (Thermo Fisher Scientific) operated in Full MS/dd-MS2 or unscheduled PRM mode. For MS/dd-MS2 the following parameters were used. MS1 resolution was 70.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z. The analysis parameters in PRM mode were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 17.500 with an AGC target of 10<sup>6</sup>, max. injection time of 55 ms and an isolation window of 1.4 m/z.</p> <p>Potential SARS-CoV-2 target peptides belonging to the N protein were identified by DDA of SARS-CoV-2 infected Calu-3 cells. Peptides were diluted in 0.1% TFA (0.2 µg/µL) and 5 µL were separated on a 50 cm μPAC™ column (PharmaFluidics) using an EASY-nLC1200 system (Thermo Fisher Scientific). The flow rate was set to 800 nL/min and a stepped 30 min gradient was applied: 6-11% B in 2:58 min, 11-30% B in 17:10 min, 30-35% B in 2:41 min, 35-47% B in 3:11 min, 47-80% B for 0:10 min, 80% B for 1:50 min, 80-0% B in 0:10 min and 100% A for 1:50 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive HF (Thermo Fisher Scientific) operated in Full MS/dd-MS2 (Top20) using the following parameters. MS1 resolution was 60.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z.</p> <p> </p> <p>To simulate a SARS-CoV-2 positive patient sample, we spiked cell-culture derived virus in a negative oropharyngeal swab and targeted the N protein by PRM. LC parameters were identical to DDA analysis of SARS-CoV-2 infected Calu-3 cells. The PRM parameters of the The Q Exactive HF (Thermo Fisher Scientific) were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 45.000 with an AGC target of 10<sup>6</sup>, max. injection time of 100 ms and an isolation window of 1.4 m/z.</p> <p> </p> <p> </p> <p><strong>Data processing protocol:</strong></p> <p>DDA Raw files were searched with MaxQuant against the respective virus database (UniProt) with a peptide FDR of 1%. Detailed MaxQuant parameters can be found in the parameters.txt files of the according results. MaxQuant .msms output files were used to generate spectral libraries with BiblioSpec implemented in the Skyline environment using a cut-off score of 0.95. Peptide identification of PRM runs was done in Skyline using the top 6 fragment ions of the DDA spectral library or according Prosit derived library (Prosit_2020_intensity_model).</p>
Data from: Pollinator Monitoring More than Pays for Itself
<p>Resilient pollination services depend on sufficient abundance of pollinating insects over time. Currently, however, most knowledge about the status and trends of pollinators is based on changes in pollinator species richness and distribution only.</p> <p>Systematic, long-term monitoring of pollinators is urgently needed to provide baseline information on their status, to identify the drivers of declines and to inform suitable response measures.</p> <p>We evaluated the full economic costs of implementing four potential national monitoring schemes in the United Kingdom, based on power analyses that estimated sample sizes required to detect a 30% national scale change in pollinator populations over 10 years: 1) professional pollinator monitoring, 2) professional pollination service monitoring, 3) volunteer collected pan-traps and 4) volunteer focal floral observations. These costs were compared to: i) the costs of implementing separate, expert-designed, research and monitoring networks and ii) the economic benefits of pollination services threatened by pollinator loss.</p> <p>Estimated scheme costs ranged from £6,159/year for a 75 site volunteer focal flower observation scheme to £2.7M/year for an 800 site professional pollination service monitoring network. The estimated research costs saved by using the site network as research infrastructure range from £1.46-17M/year. The economic value of UK crop yield lost following a 30% decline in pollinators was estimated at ~£188M/year.</p> <p><i>Synthesis and applications</i>. We evaluated the full costs of running pollinator monitoring schemes against the economic benefits to research and society they provide. The annual costs of monitoring are ≤0.02% of the estimated annual loss to UK crop production from a 30% decline in crop pollination services. Furthermore, by providing high quality scientific data, monitoring schemes would save at least £1.5 on data collection per £1 spent. Our findings demonstrate that long-term systematic monitoring can be a cost-effective tool for both addressing key research questions and setting action points for policy-makers. Careful consideration must be given to scheme design, the logistics of national-scale implementation and resulting data quality when selecting the most appropriate combination of surveyors, methods and site networks to deliver a successful scheme. </p>
Figure 2 in Non-invasive genetic study and population monitoring of the brown bear (Ursus arctos) (Mammalia: Ursidae) in Kastoria region - Greece
Figure 2. (A) Means of estimated LnP (Data) and standard deviations for K = 1 to K = 5. (B) Factorial correspondence analysis plot of multilocus genotypes for 82 brown bear individuals identified in the present study.
[Videos] Design of resilient smart highway systems with data-driven monitoring from networked cameras
<p>Traditional high-way transportation systems are monitored based on traffic counters. Such sensors provide much less information compared to traffic cameras and make the system less secure/resilient to attacks/disasters. Thanks to the success of deep learning for object detection/segmentation on images and the publicly available large-scale image datasets with object labels, fusing the information from both traffic counters and traffic cameras has the potential to improve the security and resilience of existing high- way transportation systems. The purpose of the project is to investigate such a potential by developing a deep-learning-based highway video monitoring method that can reliably estimate the fine-grained (car/truck/motorcycle) traffic flow of a high-way network. First, we need to collect a large- scale traffic video dataset with traffic flow estimations from corresponding traffic counters. Then, we need to find efficient deep learning methods for extracting fine-grained local traffic information from individual traffic videos. At last, we need to correlate this information with traffic counters for sensor fusion and detection of defective counters.</p>
Automated detection of Hainan gibbon calls for passive acoustic monitoring
<p>Data accompanying the paper: "Automated detection of Hainan gibbon calls for passive acoustic monitoring"</p> <p><strong>Please cite this dataset as:</strong></p> <blockquote> <p>Dufourq, Emmanuel and Durbach, Ian and Hansford, James and Hoepfner, Amanda and Ma, Heidi and Bryant, Jessica and Stender, Christina and Li, Wenyong and Liu, Zhiwei and Chen, Qing and Zhou, Zhaoli and Turvey, Samuel. (2020). Automated detection of Hainan gibbon calls for passive acoustic monitoring. BioRxiv doi: https://doi.org/10.1101/2020.09.07.285502</p> </blockquote> <p>The Hainan gibbon is the world's rarest primate and one of the world's rarest mammals, with only a single population of about 30 individuals surviving in Bawangling National Nature Reserve (BNNR), Hainan, China. Eight Song Meter SM3 recorders (Wildlife Acoustics, Maynard, Massachusetts) were used to collect acoustic data from 1 March to 20 August 2016 within BNNR. Recorders were attached to trees at approximately 1.5 meters from the ground in tropical evergreen forest. Recorders were set to record for eight hours each day from the time of sunrise, which varied between approximately 05:00 and 06:00 during the study period. Devices did not record continuously throughout the entire survey period due to logistical and technical issues; in total, survey days per recorder varied between 79 and 129 days, and roughly 6,000 hours of recordings were collected. The majority of recordings were made with a sampling rate of 9,600Hz and bit depth of 16, with isolated recordings at 28,800Hz.</p> <p>We provide the audio data (.wav) used to train and test our neural network classifier along with the corresponding labelled text files (.data).</p> <p><strong>Files provided</strong></p> <p>Train.zip - contains the training .wav audio files</p> <p>Train_Labels.zip - contains the labels for the training data</p> <p>Test.zip - contains the testing .wav audio files</p> <p>Test_Labels.zip - contains the labels for the test data</p> <p>Extra_Labelled_Data.zip - contains extra data that was labelled and non-gibbon calls used for training</p> <p>Extra_Labels.zip - contains the labels for the extra labelled data</p> <p>Unlabelled_Data.zip - contains additional .wav audio files which have not been labelled. These are split into various files (1-15) and can be downloaded individually.</p> <p>Code.zip - contains all the software scripts and notebooks</p> <p>Manual-zip - contains the user manual</p> <p><strong>Labels</strong></p> <p>The names of the labelled files start with either "g_" or "n_", for example "g_HGSM3D_0+1_20160429_051600.data" and "n_HGSM3D_0+1_20160429_051600.data". Files starting with "g_" contain the timestamps of the gibbon calls, and files starting with "n_" contain the timestamps of non-gibbon calls (e.g. background noise and bird calls). An audio file will have both a "g_" and "n_" file. Each file has the following format: Start,End,Duration,Type,Notes, where "start" denotes the start time in seconds, "end" denotes the end time in seconds, "duration" denotes the duration (end - start) in seconds, "type" denotes the type of call/noise and "notes" are additional notes which we labelled.</p> <p><strong>Types</strong></p> <p>The legend for the "type" column in the labelled files is defined as follows. The types for gibbon and non-gibbon files are different and we distinguish this below.</p> <p> </p> <p>Gibbon files ("g_")</p> <p>type 1 = one pulse gibbon call</p> <p>type 2 = multiple pulse gibbon call (check "notes column" below)</p> <p>type 3 = duet gibbon call</p> <p> </p> <p><em>Notes column (only available in gibbon files)</em></p> <p>One of the following: 2 pulse call, 3 pulse call, 4 pulse call, 5 pulse call or 6 pulse call.</p> <p> </p> <p>Non-gibbon files ("n_")</p> <p>type 1 = rain</p> <p>type 2 = other species (e.g. birds)</p> <p>type 3 = rain and other species</p> <p>type 4 = rain and external noise (e.g. aircraft)</p> <p>type 5 = natural sounds and external noise</p> <p>type 6 = natural sounds and other species</p> <p><strong>Training files (containing gibbon calls):</strong></p> <p>HGSM3AC_0+1_20160309_055600<br> HGSM3AC_0+1_20160312_055400<br> HGSM3A_0+1_20160304_060000<br> HGSM3BD_0+1_20160305_060000<br> HGSM3AC_0+1_20160314_055200<br> HGSM3B_0+1_20150616_050500<br> HGSM3BD_0+1_20160402_053600<br> HGSM3D_0+1_20160429_051600<br> HGSM3B_0+1_20160305_060000<br> HGSM3C_0+1_20160501_051500<br> HGSM3SOL_0+1_20160320_054700<br> HGSM3SOL_0+1_20160405_053400<br> HGSM3BD_0+1_20160401_053700</p> <p><strong>Testing files:</strong></p> <p>HGSM3B_0+1_20160323_054500<br> HGSM3B_0+1_20160321_054700<br> HGSM3B_0+1_20160306_055900<br> HGSM3B_0+1_20160308_055700<br> HGSM3B_0+1_20160309_055600<br> HGSM3B_0+1_20160316_055100<br> HGSM3B_0+1_20160311_055500<br> HGSM3B_0+1_20160304_060000<br> HGSM3B_0+1_20160322_054600</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.