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99 results for “Flow system”
Metadata with submitted Emission Control Science and Technology Journal manuscript Traceable uncertainty of exhaust flow meters embedded in portable emission measurement systems
<p>Metadata with submitted Emission Control Science and Technology Journal <em>Traceable uncertainty of exhaust flow meters embedded in portable emission measurement systems</em></p> <p>Link to article: https://link.springer.com/article/10.1007/s40825-025-00260-z</p>
Data from: Phylogenomics of a genus of ‘Great Speciators’ reveals rampant incomplete lineage sorting, gene flow, and mitochondrial capture in island systems
Open the record for dataset details and reuse information.
Data for: Water system simulation modeling with hydropower optimization and environmental flows: An example with Pywr
Open the record for dataset details and reuse information.
Characterizing the groundwater flow pathways and recharge sources of a desert inter-dune aquifer system by geophysical approaches and multiple isotopes (B, H and O)
<p>Major cations and anions, and H, O, and B isotope data of South Sumujaran Lake of Badain Jaran Desert, China</p>
Dataset of paper "Wavelength synergistic effects in continuous flow-through water disinfection systems"
<p>Dataset of paper "Wavelength synergistic effects in continuous flow-through water disinfection systems"</p>
Dataset for An Automatized Rebalancing System to Address Faradaic Imbalance and Prolong Cycle Life in Alkaline Ferrocyanide – Anthraquinone Redox Flow Batteries
<p>Dataset for the results shown in the publications "An Automatized Rebalancing System to Address Faradaic Imbalance and Prolong Cycle Life in Alkaline Ferrocyanide – Anthraquinone Redox Flow Batteries"</p>
Continuous-Flow Synthesis of BiVO4 Nanoparticles: From laboratory scale to practical systems
<p>#Data set of "Continuous-Flow Synthesis of BiVO4 Nanoparticles: From laboratory scale to practical systems"</p> <p>---</p> <p>## GENERAL INFORMATION</p> <p>1. Data set title: "Continuous-Flow Synthesis of BiVO4 Nanoparticles: From laboratory scale to practical systems"</p> <p>2.Authorship: <br> Name: Christian Robles<br> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0003-1166-2950</p> <p> Name: Laura Montañés<br> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0002-1076-013X <br> <br> Name: Camilo A. Mesa<br> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0002-8450-2563</p> <p> Name:Diego Iglesias <br> Institution: institute of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0002-4030-5816</p> <p> Name: Helena Rabelo<br> Institution: Catalan Institute of Nanoscience and Nanotechnology (ICN2) and BIST, Campus UAB, Ballaterra, 08193, Barcelona, Spain<br> ORCID: 0000-0002-7773-3929</p> <p> Name: Maria Chiara Spadaro<br> Institution: Catalan Institute of Nanoscience and Nanotechnology (ICN2) and BIST, Campus UAB, Ballaterra, 08193, Barcelona, Spain. Department SIMAU, Marche Polytechnic University, 60131, Ancona, Italy.<br> ORCID: 0000-0002-6540-0377<br> <br> Name: Jordi Arbiol<br> Institution: Catalan Institute of Nanoscience and Nanotechnology (ICN2) and BIST, Campus UAB, Ballaterra, 08193, Barcelona, Spain ICREA, 08010, Barcelona, Spain<br> ORCID: 0000-0002-0695-1726</p> <p> Name: Jesús Redondo<br> Institution: Department of Polymers and Advanced Materials, Centro de Física de Materiales CFM (UPV/EHU-CSIC), University of the Basque Country UPV/EHU, 20018, San Sebastián Spain. Department of Surface and Plasma Science, Faculty of Mathematics and Physics, Charles University, 180 00 Prague 8, Czech Republic<br> ORCID: 0000-0002-8147-689X</p> <p> Name: F.Schiller<br> Institution: Department of Polymers and Advanced Materials, Centro de Física de Materiales CFM (UPV/EHU-CSIC), University of the Basque Country UPV/EHU, 20018, San Sebastián Spain. Donostia International Physics Center DIPC, 20018 San Sebastián, Spain<br> ORCID: 0000-0003-1727-3542</p> <p> Name:Sara Barja<br> Institution: Department of Polymers and Advanced Materials, Centro de Física de Materiales CFM (UPV/EHU-CSIC), University of the Basque Country UPV/EHU, 20018, San Sebastián Spain. Donostia International Physics Center DIPC, 20018 San Sebastián, Spain. IKERBASQUE, Basque Foundation for Science, 48009 Bilbao, Spain</p> <p> Name: Beatriz Julián-López<br> Institution: Institude of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0003-1019-776X</p> <p> Name: Ana Gutiérrez-Blanco<br> Institution: Institude of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0001-9412-2321<br> Email: <angutier@uji.es></p> <p> Name: Victor Sans<br> Institution: Institude of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0001-7045-5244<br> Email: <sans@uji.es></p> <p> Name: Sixto Giménez<br> Institution: Institude of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0002-4522-3174<br> Email: <sjulia@uji.es></p> <p>## FILE DESCRIPTION<br>----------------<br>### Figure 1<br>-Fig1b.txt : Distribution hystogram of the BiVO4 NPs after the synthetic procedure.</p> <p>### Figure 3 (All measurements were carried out in 0.1 M potassium phosphate buffer (pH 7), containing 0.1 M Na2SO3 as hole scavenger.)<br>-Fig3a.txt : LSVs under chopped illumination conditions, scan rate, 10 mV·s-1, simulated solar light (AM 1.5 G, 100 mW·cm-2), back illumination.<br>-Fig3b.txt : Chronoamperometries at 1.23 V vs. RHE.<br>-Fig3c.txt : IPCE measurements and integrated photocurrent at 1.23 V vs. RHE</p> <p>### Figure 4<br>-Fig4c.txt : LSV of the measured photoanode (the electrolyte consisted on 0.5 M KPi at a constant pH of 7 and 0.5 M Na2SO3 as hole scavenger) with back illumination configuration.</p> <p>### Figure S4<br>-FigS4a.txt : Absorbance spectra of the different BiVO4 colloidal suspension alliquots taken for reproducibility studies.Seven consecutive synthetic runs were carried out and an aliquot was taken from each run, always under identical conditions regarding the collection time of the sample (30 minutes after starting the reaction).<br>-FigS4b.txt : Transmittance spectra of the different BiVO4 colloidal suspension alliquots taken for reproducibility studies.Seven consecutive synthetic runs were carried out and an aliquot was taken from each run, always under identical conditions regarding the collection time of the sample (30 minutes after starting the reaction).<br>-FigS4c.txt : Absorbance mean + error or the different BiVO4 colloidal suspension alliquots taken for reproducibility studies.<br>-FigS4d.txt : Transmittance at the selcted wavelength (400 nm) for the different BiVO4 colloidal suspension alliquots taken for reproducibility studies.</p> <p>## Figure S6<br>-FigS6.txt : Thermogravimetric analysis (TGA) curve of the BiVO4 NPs from room temperature to 500 ºC.</p> <p>### Figure S7<br>-FigS7.txt : XRD patterns of the FTO/BiVO4 photoanodes before and after thermal annealing. </p> <p>### Figure S8<br>-FigS8a.txt : Raman spectra of the FTO/BiVO4/Al2O3 based films before and after annealing. <br>-FigS8b.txt : FT-IR spectra of the FTO/BiVO4/Al2O3 based films before and after annealing. </p> <p>### Figure S9<br>-FigS9a.txt : Absorptance spectra of the FTO/BiVO4/Al2O3 samples before and after the annealing process.<br>-FigS9b.txt : Tauc plot of the FTO/BiVO4/Al2O3 samples before and after the annealing process. </p> <p>### Figure S11<br>-Fig11a.txt : LSV of the FTO/BiVO4/Al2O3 films in 0.1 M potassium phosphate buffer (pH 7) containing 0.1 M Na2SO3 as hole scavenger under AM 1.5 G, 100 mW·cm-2 back side illumination (scan rate, 10 mV·s-1) for the deposition reproducibility studies.<br>-Fig11b takes the same data contained in Fig11a.txt</p> <p>### Figure S12 (Measurements in 0.1 M phosphate buffer (pH 7) containing 0.1 M Na2SO3 as hole scavenger under AM 1.5 G, 100 mW·cm-2 back side illumination.)<br>-FigS12a.txt : Consecutive LSVs (LSV1, LSV2 LSV3) and LSV after 2 hours testing for the FTO/BiVO4 (area 5 cm2).<br>-FigS12b.txt : Consecutive LSVs (LSV1, LSV2 LSV3) and LSV after 2 hours testing for the FTO/BiVO4/Al2O3 films (area 5 cm2) showing the enhanced stability after deposition of the Al2O3 layer.</p> <p> </p>
The application of a mode of double preconcentration and double stripping steps in combination with a flow system to decrease the detection limit and increase the selectivity of determinations in stripping analysis of As(III)
<p><span>Due to the fact that arsenic </span><span>has very harmful and toxic effects on living organisms </span><span>monitoring of its content in various environmental samples </span><span>at trace level concentrations</span><span> </span><span>is desirable and highly recommended. Such advantages as low cost and portable instrumentation, achieving low detection limits and most of all the possibility of the direct determination of As(III) with no separation step make stripping voltammetry suitable for the determination of arsenic.</span><span> </span><span> <br></span></p> <p><span>These data concer the application of double deposition and double stripping steps mode in combination with a flow system for anodic stripping voltammetric determination of As(III) ions. An advantage of the proposed way of the voltammetric measurements is a decrease of the detection limit as compared to the values obtained using a traditional three-electrode system thanks to an initial preconcentration of the analyte on the first working electrode as well as the significant increase in a selectivity of As(III) determination thanks to the possibility of solution exchange after the first deposition step.</span></p> <p><span><span>The proposed way of the voltammetric measurements leads to the following analytical advantages: (i) a decrease of the detection limit as compared to the values obtained using traditional three-electrode system by an initial preconcentration of the analyte using the large-surface area electrode; (ii) an increase of the selectivity of voltammetric determinations thanks to the application of a flow system and exchange the solution after the first deposition step; (iii) simplification of performing the measurements in comparison to the original version of double accumulation and stripping steps mode due to the lack of a need to change the position of the first working electrode; (iv) the possibility of analysis of samples with a complex matrix thanks to the use of a flow system leading to minimization of matrix effects in the second deposition step. The developed procedure of As(III) determination offers a wide linear dynamic range in the range from 1 × 10<sup>-9</sup> to 5 × 10<sup>-8</sup> mol L<sup>-1</sup> at a deposition time of 120 s at both working electrodes. The detection limit for As(III) determination was 4.8 × 10<sup>-10</sup> mol L<sup>-1</sup>. The undoubted advantage of the described procedure is its high selectivity. The tolerable limit of excess of Cu(II), which is the main interferent in the course of anodic stripping voltammetric As(III) determination, was significantly increased thanks to the solution exchange after the first deposition step. The correctness of the proposed procedure was confirmed by analysis of a real water sample and lake water certified reference material by obtaining satisfactory recovery values. The additional advantage of the proposed procedure is a miniaturization of the measurement system and the use of environmentally friendly electrodes.</span></span></p>
Passive Operating System Fingerprinting Revisited - Network Flows Dataset
<p>For the evaluation of OS fingerprinting methods, we need a dataset with the following requirements:</p> <ul> <li>First, the dataset needs to be big enough to capture the variability of the data. In this case, we need many connections from different operating systems.</li> <li>Second, the dataset needs to be annotated, which means that the corresponding operating system needs to be known for each network connection captured in the dataset. Therefore, we cannot just capture any network traffic for our dataset; we need to be able to determine the OS reliably.</li> </ul> <p>To overcome these issues, we have decided to create the dataset from the traffic of several web servers at our university. This allows us to address the first issue by collecting traces from thousands of devices ranging from user computers and mobile phones to web crawlers and other servers. The ground truth values are obtained from the HTTP User-Agent, which resolves the second of the presented issues. Even though most traffic is encrypted, the User-Agent can be recovered from the web server logs that record every connection’s details. By correlating the IP address and timestamp of each log record to the captured traffic, we can add the ground truth to the dataset.</p> <p>For this dataset, we have selected a cluster of five web servers that host 475 unique university domains for public websites. The monitoring point recording the traffic was placed at the backbone network connecting the university to the Internet.</p> <p>The dataset used in this paper was collected from approximately 8 hours of university web traffic throughout a single workday. The logs were collected from Microsoft IIS web servers and converted from W3C extended logging format to JSON. The logs are referred to as <em>web logs </em>and are used to annotate the records generated from packet capture obtained by using a network probe tapped into the link to the Internet.</p> <p>The entire dataset creation process consists of seven steps:</p> <ol> <li>The packet capture was processed by the Flowmon flow exporter (<a href="https://www.flowmon.com">https://www.flowmon.com</a>) to obtain primary flow data containing information from TLS and HTTP protocols.</li> <li>Additional statistical features were extracted using GoFlows flow exporter (<a href="https://github.com/CN-TU/go-flows">https://github.com/CN-TU/go-flows</a>).</li> <li>The primary flows were filtered to remove incomplete records and network scans.</li> <li>The flows from both exporters were merged together into records containing fields from both sources.</li> <li><em>Web logs </em>were filtered to cover the same time frame as the flow records.</li> <li><em>Web logs</em> were paired with the flow records based on shared properties (IP address, port, time).</li> <li>The last step was to convert the User-Agent values into the operating system using a Python version of the open-source tool <em>ua-parser</em> (<a href="https://github.com/ua-parser/uap-python">https://github.com/ua-parser/uap-python</a>). We replaced the unstructured User-Agent string in the records with the resulting OS.</li> </ol> <p>The collected and enriched flows contain 111 data fields that can be used as features for OS fingerprinting or any other data analyses. The fields grouped by their area are listed below:</p> <ul> <li>basic flow properties - flow_ID;start;end;L3 PROTO;L4 PROTO;BYTES A;PACKETS A;SRC IP;DST IP;TCP flags A;SRC port;DST port;packetTotalCountforward;packetTotalCountbackward;flowDirection;flowEndReason;</li> <li>IP parameters - IP ToS;maximumTTLforward;maximumTTLbackward;IPv4DontFragmentforward;IPv4DontFragmentbackward;</li> <li>TCP parameters - TCP SYN Size;TCP Win Size;TCP SYN TTL;tcpTimestampFirstPacketbackward;tcpOptionWindowScaleforward;tcpOptionWindowScalebackward;tcpOptionSelectiveAckPermittedforward;tcpOptionSelectiveAckPermittedbackward;tcpOptionMaximumSegmentSizeforward;tcpOptionMaximumSegmentSizebackward;tcpOptionNoOperationforward;tcpOptionNoOperationbackward;synAckFlag;tcpTimestampFirstPacketforward;</li> <li>HTTP - HTTP Request Host;URL;</li> <li>User-agent - UA OS family;UA OS major;UA OS minor;UA OS patch;UA OS patch minor;</li> <li>TLS - TLS_CONTENT_TYPE;TLS_HANDSHAKE_TYPE;TLS_SETUP_TIME;TLS_SERVER_VERSION;TLS_SERVER_RANDOM;TLS_SERVER_SESSION_ID;TLS_CIPHER_SUITE;TLS_ALPN;TLS_SNI;TLS_SNI_LENGTH;TLS_CLIENT_VERSION;TLS_CIPHER_SUITES;TLS_CLIENT_RANDOM;TLS_CLIENT_SESSION_ID;TLS_EXTENSION_TYPES;TLS_EXTENSION_LENGTHS;TLS_ELLIPTIC_CURVES;TLS_EC_POINT_FORMATS;TLS_CLIENT_KEY_LENGTH;TLS_ISSUER_CN;TLS_SUBJECT_CN;TLS_SUBJECT_ON;TLS_VALIDITY_NOT_BEFORE;TLS_VALIDITY_NOT_AFTER;TLS_SIGNATURE_ALG;TLS_PUBLIC_KEY_ALG;TLS_PUBLIC_KEY_LENGTH;TLS_JA3_FINGERPRINT;</li> <li>Packet timings - NPM_CLIENT_NETWORK_TIME;NPM_SERVER_NETWORK_TIME;NPM_SERVER_RESPONSE_TIME;NPM_ROUND_TRIP_TIME;NPM_RESPONSE_TIMEOUTS_A;NPM_RESPONSE_TIMEOUTS_B;NPM_TCP_RETRANSMISSION_A;NPM_TCP_RETRANSMISSION_B;NPM_TCP_OUT_OF_ORDER_A;NPM_TCP_OUT_OF_ORDER_B;NPM_JITTER_DEV_A;NPM_JITTER_AVG_A;NPM_JITTER_MIN_A;NPM_JITTER_MAX_A;NPM_DELAY_DEV_A;NPM_DELAY_AVG_A;NPM_DELAY_MIN_A;NPM_DELAY_MAX_A;NPM_DELAY_HISTOGRAM_1_A;NPM_DELAY_HISTOGRAM_2_A;NPM_DELAY_HISTOGRAM_3_A;NPM_DELAY_HISTOGRAM_4_A;NPM_DELAY_HISTOGRAM_5_A;NPM_DELAY_HISTOGRAM_6_A;NPM_DELAY_HISTOGRAM_7_A;NPM_JITTER_DEV_B;NPM_JITTER_AVG_B;NPM_JITTER_MIN_B;NPM_JITTER_MAX_B;NPM_DELAY_DEV_B;NPM_DELAY_AVG_B;NPM_DELAY_MIN_B;NPM_DELAY_MAX_B;NPM_DELAY_HISTOGRAM_1_B;NPM_DELAY_HISTOGRAM_2_B;NPM_DELAY_HISTOGRAM_3_B;NPM_DELAY_HISTOGRAM_4_B;NPM_DELAY_HISTOGRAM_5_B;NPM_DELAY_HISTOGRAM_6_B;NPM_DELAY_HISTOGRAM_7_B;</li> <li>ICMP - ICMP TYPE;</li> </ul> <p>The details of OS distribution grouped by the OS family are summarized in the table below. The <em>Other </em>OS family contains records generated by web crawling bots that do not include OS information in the User-Agent.</p> <table> <thead> <tr> <th scope="col">OS Family</th> <th scope="col">Number of flows</th> </tr> </thead> <tbody> <tr> <td>Other</td> <td>42474</td> </tr> <tr> <td>Windows</td> <td>40349</td> </tr> <tr> <td>Android</td> <td>10290</td> </tr> <tr> <td>iOS</td> <td>8840</td> </tr> <tr> <td>Mac OS X</td> <td>5324</td> </tr> <tr> <td>Linux</td> <td>1589</td> </tr> <tr> <td>Ubuntu</td> <td>653</td> </tr> <tr> <td>Fedora</td> <td>88</td> </tr> <tr> <td>Chrome OS</td> <td>53</td> </tr> <tr> <td>Symbian OS</td> <td>1</td> </tr> <tr> <td>Slackware</td> <td>1</td> </tr> <tr> <td>Linux Mint</td> <td>1</td> </tr> </tbody> </table> <p> </p>
Randomly Distributed Crab Burrows Enhance Groundwater Flow and Salt Transport in Creek-Marsh Systems
<p>This dataset contains SUTRA input file and training image that used in our research paper "Randomly Distributed Crab Burrows Enhance Groundwater Flow and Salt Transport in Creek-Marsh Systems".</p>
Effect of Sildenafil on the Microcirculatory Blood Flow and Endothelial Progenitor Cells in Systemic Sclerosis
ClinicalTrials.gov study NCT01347008. IPD Sharing: NO. Countries: 1. Publications: 1.
Development of a flow chamber system for the reproducible in vitro analysis of biofilm formation on implant materials
<p>The data provided are the original data from the microscopic investigation of oral bacterial biofilms. Bacteria were stained with a life/dead staining. Viable cells are represented in red, non-viable cells are shown in green. The biofilms were gained by 50 stacks each with a CLSM. The biofilms were formed in a flow chamber system with a flow velocity of 100µL/min over 24-72 hours. The biofilms were grown on tintanium discs.</p>
Dataset of flow rate, water temperature, radon concentration, and seismic waveforms from a hot spring system
<p>Flow rate, water temperature, radon concentration, meteorological data, and seismic waveforms in a hot spring system that used for investigating the groundwater radon changes and hydrological responses induced by large earthquakes. </p>
Discrete Global Grid System-based Flow Routing Datasets in the Amazon and Yukon Basins
<p>ISEA3H DGGS-based flow routing datasets in the Amazon and Yukon River Basins.</p>
Modeling fluid flow in ship systems for controller tuning using an artificial neural network
<p>Dataset used to develop ANN NARX models</p>
A passenger flow data set collected in the metro system of Hangzhou, China
<p>This repository is a passenger flow (mobility) data set collected in the Hangzhou metro system with 81 stations.</p> <p><strong>Note</strong>: The source data of this repository is from <strong><a href="https://tianchi.aliyun.com/competition/entrance/231708/information">Urban computing data set - Tianchi competition</a></strong>.</p>
A Stop-Flow Sample Delivery System for Transient Spectroscopy
<p>data supporting: "A Stop-Flow Sample Delivery System for Transient Spectroscopy"; Reviews of Scientific Instruments, 2021</p>
A New Dataset of People Flow in an Industrial Site with UWB and Motion Capture Systems
<p>Improving performance and safety conditions at industrial sites remain key elements of the Group's strategy. Major issues require the ability to dynamically locate people and assets on the site. Currently, the security and regulation of access to areas with different characteristics (types of tasks, level of risk, or confidentiality...) are often carried out with badge doors or barriers. These means present several weaknesses in the face of inappropriate movements of people, but also of objects or tools. Also, there is increasing use of technological devices requiring precise localization in the industrial environment such as AGVs (mobile robots or drones) or augmented reality devices. It is therefore becoming essential to have tools to dynamically manage these flows of people or goods associated with precise location technologies. An Ultra-Wide-Band solution will be employed to quickly and efficiently identify persons who may find themselves in unauthorized areas or perform tasks for which they are uninstructed. Besides dynamic people tracking, this solution can overcome problems of moving objects and tools in production workshops. We propose a new dataset to have information about workers' displacement and evaluate workers' performance and safety.</p> <p>The dataset is available at this link: IndoorIndustrialLocalisationDataset: <a href="https://github.com/vauchey/IndoorInsdustrialLocalisationDataset/">https://github.com/vauchey/IndoorInsdustrialLocalisationDataset/</a></p> <p> </p> <p>Paper :</p> <p>Mickael Delamare<sup>1</sup>, Fabrice Duval<sup><span>2</span></sup>, Remi Boutteau<sup><span>3</span></sup>.</p> <p><sup>1</sup><a href="http://www.cesi.fr./">cesi</a>, CESI,Rouen campus, 76000 ROUEN, France,<a href="mailto:mdelamare@cesi.fr">mdelamare@cesi.fr</a><br> <span><sup>2</sup> </span><a href="https://lineact.cesi.fr/">LINEACT CESI</a>, LINEACT CESI, Rouen campus,76000 ROUEN, France, <a href="mailto:fduval@cesi.fr">fduval@cesi.fr</a><br> <span><sup>3</sup> </span><a href="http://www.esigelec.fr/">ESIGELEC</a> , IRSEEM, Rouen, France, Normandie Univ, UNIROUEN, <a href="mailto:remi.boutteau@univ-rouen.fr">remi.boutteau@univ-rouen.fr</a>,</p> <p>Link : <a href="https://www.mdpi.com/1424-8220/20/16/4511">https://www.mdpi.com/1424-8220/20/16/4511</a></p> <p>DOI : <a href="https://doi.org/10.3390/s20164511">https://doi.org/10.3390/s20164511</a></p> <p>Accepted to the journal Sensors (MDPI)</p> <p> </p>
Nitrification in biofloc system with different aeration flows rates
<p>Determine the response of nitrifying bacteria present in the biofilm submitted to different aeration intensities during the production of <em>Litopenaeus</em> <em>vannamei</em> (Boone, 1931) in a clear water system and also with bioflocs.</p>
Nutrient Flows in the French Sanitation System
<p>Code and generated results for nitrogen and phosphorus flows analysis of the French sanitation system. Produced from multiple data sources: wastewater treamtent plats operational data, industry discharges to sewers, food intake surveys...</p>
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.