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3 large scale vasularized tumors
<p>To estimate the feasibility of large scale vascular tumor simulation on the current hardware at the physics department of Saarland University, I create an artificial vasculature of lateral size 2.5 cm and applied the fakeTum simulation of Tumorcode (see references). Each of the 3 datasets with time snapshots take every 50h was about 1.5 TB. Therefore I decided to deposit only the initial and final simulation state here.</p>
Pelvic arcade and vertebral structure of the second chief specimen of Tyrannosaurus rex, Amer. Mus. 5027, discovered in 1908. An orthogonal projection executed on a very large scale and reproduced one-twelfth natural size. C 1-C 10 cervical series, D 1-D 13 dorsal or thoracic series, S 1-S 5 sacral series, Cd 1- Cd 53 caudal series. The caudals actually preserved are shaded; those drawn in outline are conjectural and restored. The total number of caudals is conjectural in Skeletal Adaptations of Ornitholestes, Struthiomimus, Tyrannosaurus
Pelvic arcade and vertebral structure of the second chief specimen of Tyrannosaurus rex, Amer. Mus. 5027, discovered in 1908. An orthogonal projection executed on a very large scale and reproduced one-twelfth natural size. C 1-C 10 cervical series, D 1-D 13 dorsal or thoracic series, S 1-S 5 sacral series, Cd 1- Cd 53 caudal series. The caudals actually preserved are shaded; those drawn in outline are conjectural and restored. The total number of caudals is conjectural
Draft-Laboratory observation of turbulence and wave shear stresses under large scale breaking waves over a mild slope
<p>figures with PNG format and dataset for generation of plots</p>
Figure 3 in Detailed integrative taxonomic analysis reveals large-scale species misidentification of barnacles based on DNA barcoding data
Figure 3. Neighbour-joining tree for the three investigated barcode index numbers. Ŋe coloured sequences in each clade indicate our newly generated sequences of specimens with unequivocal morphological identification.
Figure 2 in Detailed integrative taxonomic analysis reveals large-scale species misidentification of barnacles based on DNA barcoding data
Figure 2. Morphology of unidentified Balanus sp. individuals of clade A (BOLD:AAG0069) (A), Balanus balanus (B), Balanus crenatus
Figure 1 in Detailed integrative taxonomic analysis reveals large-scale species misidentification of barnacles based on DNA barcoding data
Figure 1. Bubble plot of the correspondence between species assignments and the barcode index number (BIN; genetic clade). Ŋe size of the bubbles corresponds to the number of sequences contained in each combination of BIN and species assignment. Vertical lines highlight BINs that contain more than one species assignment, and horizontal lines highlight species assignments that are present in more than one BIN. Grey bubbles indicate a one-to-one match between BIN and species assignment. Coloured bubbles represent the three BINs that were investigated further by integrative taxonomic analysis.
zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation
<p>Data and code used to generate the analyses and figures in paper "zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation" are provided here.</p>
Feldman et al. 2024 "Large global scale vegetation sensitivity to daily rainfall variability"
<p>These data and scripts are a part of the Feldman et al. manuscript accepted in principle titled "Large global scale vegetation sensitivity to daily rainfall variability".</p> <p>The data and scripts here can be used to generate all steps of the analysis. It includes:</p> <p>(1) The final generated outputs of the analysis and the python script to generate the figures in the main text, extended data, and supplemental information. This figure generation script is: "MainScript_GenerateFigures_V4.py"</p> <p>(2) The python script to conduct the main analysis in an example region in Africa, which is named: "MainScript_SatelliteVegetationProcessing_Example.py". </p> <p>(3) The python script to conduct the main analysis across the globe (to generate figure 1), which is named: "MainScript_SatelliteVegetationProcessing_V2.py". Input data for this script are available at a separate Zenodo account here: https://zenodo.org/records/10947071.</p> <p>(4) Scripts and input data to conduct rainfall trend analyses and mechanism analyses.</p> <p>(5) Scripts to show how the gridded datasets were processed into their one degree gridding to be input into other components of the analysis.</p> <p>Due to their size, the full global datasets to conduct the global analysis are available on a separate Zenodo account here: https://zenodo.org/records/10947071.</p> <p>Please consult the README.txt file for more information about python and names of files and scripts being used. </p> <p>For any use of the datasets and scripts, please cite (1) the Feldman et al "Large global scale vegetation sensitivity to daily rainfall variability" publication. Additionally, (2) please acknowledge the use of the scripts and/or datasets in the acknowledgements section of your publication. The use of any scripts or data are not allowed unless the paper is in print. For questions, please contact Andrew Feldman at andrew.feldman@nasa.gov. </p>
Amateur Radio Spot Data for "First Observations Linking Large-Scale Traveling Ionospheric Disturbances to Polar Vortex Strength"
<p>This repository contains the raw data amateur radio data used to compute the Large Scale Traveling Ionosphic Disturbance activity presented the<br>Geophysical Research Letters manuscript<br>"First Observations Linking Large-Scale Traveling Ionospheric Disturbances to Polar Vortex Strength".</p> <p>This dataset has monthy *.tar files containing daily bzip2 Comma Separated Value (CSV) files of data from the following amateur radio spotting networks for the time period November 2018 - May 2019:</p> <ol> <li><a href="https://pskreporter.info/" target="_blank" rel="noopener">PSKReporter (https://pskreporter.info/)</a></li> <li><a href="https://www.wsprnet.org/" target="_blank" rel="noopener">Weak Signal Propagation Reporter Network (WSPRNet, https://www.wsprnet.org/)</a></li> <li><a href="https://reversebeacon.net/" target="_blank" rel="noopener">Reverse Beacon Network (https://reversebeacon.net/)</a></li> </ol> <p>Where necessary, spots have been geolocated using latitudes and longitudes from <a href="https://www.qrz.com/">https://www.qrz.com/</a>.</p> <p>We are grateful to the operators of PSKReporter, WSPRNet, and the Reverse Beacon Network for the use of their data.</p> <p>The columns of each CSV file correspond to the following values:</p> <p># [0] UTC Datetime <br># [1] Transmitter Call Sign<br># [2] Transmitter Maidenhead Grid Square<br># [3] Transmitter Geographic Latitude<br># [4] Transmitter Geographic Longitude<br># [5] Source of Transmitter Gride Information<br>#<br># [6] Receiver Maidenhead Grid Square<br># [7] Receiver Geographic Latitude<br># [8] Receiver Geographic Longitude<br># [9] Source of Receiver Gride Information<br>#<br># [11] Frequency (Hz)<br># [12] Signal to Noise Ration (SNR) [dB]<br># [13] Mode<br>#<br># [14] Source<br># [15] Sender Status<br># [16] Inter-Modulation Distortion (IMD)<br># [17] ipOriginID<br># [18] sendeMobileLocator<br># [19] c1<br># [10] c2<br># [21] mode2</p> <p># Computed Fields<br># [22] Great Circle Short Path [km]<br># [23] Great Circle Short Path Midpoint Geographic Latitude<br># [24] Great Circle Short Path Midpoint Geographic Longitude</p> <p>This dataset is designed to be used with the LSTID autodetection software available at <a href="https://doi.org/10.5281/zenodo.13630867" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13630867</a>.</p>
A multi-species benchmark for training and validating large scale mass spectrometry proteomics machine learning models
<p>This is a de novo sequencing benchmark dataset derived from nine<br>publicly available mass spectrometry datasets. There are two versions<br>of the benchmark: main and balanced. The balanced version randomly<br>eliminates some spectra associated with some species in order to<br>create a smaller, more evenly balanced dataset. Also provided are two<br>zip files containing the raw data as well as intermediate results.<br>Details about how the benchmark was created are provided in an<br><a href="../records/13653420">associated zenodo release</a>, which contains the source code as well as a<br>manuscript describing the benchmark.</p> <p>This release fixes a bug that incorrectly detected shared peptides <br>between different species. It also includes the annotated spectra in <br>mzSpecLib format.</p> <p> </p>
Boosting large scale capacitive harvesting of osmotic power by dynamical matching of ion exchange kinetics
<p>Raw data for the article "Boosting large scale capacitive harvesting of osmotic power by dynamical matching of ion exchange kinetics" by N. Chapuis and L.Bocquet.</p>
GEOLAB-PEBSTER-TU Darmstadt: Large-scale model test on Piled Embankments with Basal Steel Mesh Reinforcement - Measurements.
<h1><strong>Large-scale model test on basal steel-mesh reinforced piled embankments (PEBSTER, a transnational access project of GEOLAB)</strong></h1> <p>Welcome to the GEOLAB PEBSTER TU Darmstadt project on Zenodo! This repository contains the measurement data of the large-scale model test on Piled Embankments with a Basal Steel Reinforcement (PEBSTER). Pile-supported embankments with basal geosynthetic reinforcement are commonly built in soft soil areas. Steel mesh reinforcement is particularly appealing for high embankments (Topolnicki et al., 2019). Its high axial stiffness, compared to geosynthetics, reduces horizontal deformation at the embankment base, minimizes bending moments in the piles, and ultimately enhances embankment stability.</p> <h2>PEBSTER</h2> <p>This dataset includes measurements from a large-scale test on steel-reinforced piled embankments, conducted at the Institute of Geotechnics of the Technical University of Darmstadt (Schneider et al., 2024a,b,c). This test is part of a broader research initiative called Piled Embankments with Basal Steel Reinforcement (PEBSTER), which also includes four small-scale test (van Eekelen et al., 2024), conducted at Deltares, Delft, Netherlands.</p> <h2>GEOLAB</h2> <p>GEOLAB is a project of the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 101006512, addressing Europe's Critical Infrastructure (CI) challenges in the water, energy, urban, and transport sectors.</p> <p>The GEOLAB Research Infrastructure (RI) consists of 11 unique installations across Europe to study subsurface behavior and its interaction with structural CI elements and the environment. During the GEOLAB Transnational Access (TA), users outside the consortium gained access to the GEOLAB installations to perform research and innovation.</p> <h2>PEBSTER Research group</h2> <p>The project was conducted by a research group that includes the Institute of Geotechnics of the Technical University of Darmstadt, Germany, Deltares, Netherlands, Keller (Germany, France, Poland), and SHM System, Poland.</p>
Knowledge needed for large-scale landscape restoration projects: scientific and experiential. 13.2B Session, Society for Ecological Restoration Conference 2024, Room Hurt, 16:30 -18:00, Thursday, 29th August
<p><strong>Disclaimer: </strong>The DOI of this video does not apply to the content of the slides of the contributors' presentations during this session. The authors of each communication maintain their copyright on the content of the presentations' slides.</p> <p><strong>The creators' roles were as follows:</strong> I. V. A., chair, session coordinator, and presenter; B. S., session coordinator; E. P. M., session coordinator, and presenter; H. J., presenter; F. S., presenter; W. E., presenter; G. M., presenter; and P. I., presenter.</p> <p><strong>Session description</strong></p> <p>This session aimed to explore the critical capacities and functions relevant to the large-scale ecosystem restoration (LER) economy and point out the complementarity of scientific and experiential knowledge and the key role of case studies in increasing this resilience. The restoration economy can be defined as “the market consisting of a network of businesses, investors, consumers, and government initiatives engaging in or driving the economic activity related to ecological restoration.” Its segment dealing with LER can be delimited by the large complexity of the system related to the natural, legislative, institutional, funding, project structure, and organizational network to implement the project, as well as of the economic sectors involved in implementing the projects. Success or failure depends on the interplay between the variables describing the environment of the project, its structure, and its functioning. For success, the uptake of scientific information is as important as the experiential (tacit) knowledge of practitioners, stakeholders, and policymakers. Scientific knowledge can be delivered to the LER economy by structures (models) and data (variables and measurements/estimations of their values). Still, in practice, it is often not a limiting resource. Experiential knowledge can be transferred to some extent by narratives (stories, opinions, examples in common language). The role of case studies for LER is to provide hints about what worked and did not work in other situations and, on this basis, to prepare the adaptive management and increase the resilience of each new project. The session provided opportunities for scientists to interact with practitioners about the problem of LER adaptive management and illustrate the issues with examples of scientific knowledge transfer to current or potential projects and case studies. The session was conceived as crosscutting the missions of the LER and European sections of the Society of Ecological Restoration. It is relevant for the development of the restoration economy in the EU through the implementation of the future European Nature Restoration Law, its effectiveness, and its efficiency.</p> <p> </p> <p><strong>Session report</strong></p> <p><em><strong>Three key elements emerged from the session:</strong></em></p> <p>· Even though there is a widespread need for this kind of knowledge in all phases of project and program cycle development, the role of experiential knowledge/ know-how in ecological restoration is not explicitly treated.</p> <p>· The proprietary know-how can be included in portfolios of patents for technologies and services needed in landscape restoration, which are open-access or undisclosed by practitioners. These types are functionally complementary in the restoration industry and appropriate for specific circumstances.</p> <p>· One can test hypotheses about the role of experiential knowledge in the success of landscape restoration through changes in human resources and human resource formation practice by including people or practices that increase the role of experiential knowledge in the overall knowledge capital available in the social ecosystem implementing the restoration programs and projects.</p> <p> </p> <p><em><strong>Narrative of the session:</strong></em></p> <p>The session showed exciting complementarity between various restoration project phases and provided solid arguments about the importance of experiential knowledge [1]. In the first communication, Jeoelen showed us social science research about what factors positively and negatively control cooperation in the restoration ecosystem of individuals, private organizations, NGOs, and public institutions. Then, she provided information about guidelines for practitioners about how to build trustful relations. She was asked what solutions one has when there are people in the team with undesirable traits, and the answer was to avoid such persons from the very beginning or, if one needs that in the team for other qualities, to discuss with them explicitly the problems. The second speaker, Sebastian, contributed with a complex process-based socio-ecological model presentation and its use in decision-making. His question was about the possibility of including details of cost-benefit analysis in the models of stakeholders' decisions, and the answer was that, in principle, it is possible. However, one must also consider the model's optimal complexity in terms of incertitude propagation. In the third presentation, Pal and Erik presented the practical approach to peatland restoration in catchments from Norway. In their context, it seemed feasible because of data available for hydrological modeling from other state monitoring systems and the fact that most restoration projects occurred on public land.</p> <p>Several examples demonstrated the approach's success in a catchment near Oslo, where the public's interest in recreation is high, and there is support for restoration. The question from the audience related their work to the first presentation of the session topic, namely human resources and trust building with stakeholders. In their context, the usual interactions and roundtables with stakeholders before the start of the projects, as well as continuous formal and informal communication in the implementation phase, were enough to build the needed social capital for a successful restoration outcome. In the first online presentation, Martina provided details about non-formal education projects in marine areas that need restoration ecology. The target group was children, but the projects also engaged adults and environmental professionals from the local socio-ecological systems, thus propagating the informed trust building to the whole community. Her details pointed out the experiential approach in education. Iulia from WWF Romania underlined the importance of case studies in communicating restoration's positive and negative aspects to practitioners. She touched on critical points of experiential knowledge related to the genuine national-scale social interest in restoration, despite the significant interest of local users and local authorities and even the existence of funding for restoration. The lack of cooperation between institutions is a limiting factor for up-scaling the restoration to the full Lower Danube River scale. Even in these conditions, WWF-Romania managed to implement many restoration projects on the Danube River and built a solid relationship with the local people by putting an equal accent on nature and people in the design and implementation phase of the projects. Iulia was asked what they do when there are problems with environmental management (such as illegal garbage disposal) that are not formally relevant to the restoration project but occur in the communities they work with. The answer was that they adopted a proactive and solution-oriented approach and started discussions with the people to identify potential solutions and even develop new projects in cooperation that targeted those problems. Finally, Virgil referred to the audience to the content of this supplementary material, as available on the WhatsApp group of the conference in its preliminary form, and communicated about the cross-cutting session 13.0 from Friday as a framework for further discussion of the issues and disentangling opportunities and challenges in landscape restoration. His presentation showed the relationship between scientific knowledge and experiential one (know-how) in the technology readiness level system. It illustrated this notion by developing a TRL6 environmental service for the cumulative impact assessment of multiple active management actions in contaminated river basins, including restoration of buffer zones and control of mining point sources by remediation projects [2].</p> <p> </p> <p><em><strong>Operational concept of experiential knowledge</strong>:</em></p> <p>· Experiential knowledge is undiscursive knowledge needed for practical activities, particularly ecological restoration.</p> <p>· Related terms: know-how, tacit knowledge.</p> <p>· We acquire it by working with experienced people, and the chains of transfer lead to practical traditions.</p> <p>· It can be transferred to some extent by narratives, films, etc, formally communicated by case studies in professional societies, but personal interactions are decisive for the successful transfer.</p> <p>· It complements scientific knowledge, forming the knowledge capital needed for ecological restoration.</p> <p>· It can be proprietary, non-proprietary, disclosed, and undisclosed.</p> <p>· Its availability geographically and in specific phases of the restoration programs and project cycles limits the success of the restoration.</p> <p>· Due to its complexity, large-scale ecosystem/landscape restoration is highly sensitive to the impact of a lack of experiential knowledge.</p> <p>· Experiential knowledge is needed at the first level in all phases of the program and project cycles and in all themes associated with landscape restoration (at general objectives formulation, implementation/monitoring, financing, and supporting the local economies). Appendix 1 of the full report [1] shows a potential distribution of the contributions to landscape restoration sessions and workshops by these themes, referring in several cases to experiential knowledge).</p> <p>· An extra level of experiential knowledge is needed to successfully transfer first-level know-how/ experiential knowledge.</p> <p>· Despite its unstructured and informal/unprocedural reality, one can test hypotheses about the role of experiential knowledge in the success of landscape restoration through changes in human resources and human resource formation practice by including people or practices that increase the role of experiential knowledge in the overall knowledge capital available in the social ecosystem implementing the restoration programs and projects. This complements the challenge of hypotheses testing at scientific standards of active measures in restoring ecological networks (large-scale ecosystems, landscapes).</p> <p><em><strong>Notes</strong></em></p> <p>[1] For the place of the contributions of this session in the general landscape of the conference, one can download the full report here: <a href="https://virgiliordache.com/2024/08/31/scientific-vs-experiential-knowledge-13-2b-session-report-sere-2024-tartu-estonia/">https://virgiliordache.com/2024/08/31/scientific-vs-experiential-knowledge-13-2b-session-report-sere-2024-tartu-estonia/</a></p> <p>[2] The pdf of his presentation is available for download at <a href="https://virgiliordache.com/2024/08/24/grounding-the-landscape-scale-restoration-for-biogeochemical-services-on-cumulative-impact-assessment-a-process-based-approach/">https://virgiliordache.com/2024/08/24/grounding-the-landscape-scale-restoration-for-biogeochemical-services-on-cumulative-impact-assessment-a-process-based-approach/</a></p> <p><strong>Acknowledgments</strong></p> <p>We thank the organizers of the SERE 2024 conference and the technical team involved in broadcasting and registering this video. We also thank to the persons from the audience who contributed with questions and comments.</p>
A fMRI dataset in response to large-scale short natural dynamic facial expression videos
<p>Facial expression is among the most natural methods for human beings to convey their emotional information in daily life. Although the neural mechanism of facial expression has been extensively studied employing lab-controlled images and a small number of lab-controlled video stimuli, how the human brain processes natural facial expressions still needs to be investigated. To our knowledge, this type of data specifically on large<span>-scale</span> natural facial expression videos is currently missing. We describe here the natural Facial Expressions Dataset (NFED), a fMRI dataset including responses to 1,320 short (3-second) natural facial expression video clips. These video clips is annotated with three types of labels: emotion, gender, and ethnicity, along with accompanying metadata. We validate that the dataset has good quality within and across participants and, notably, can capture temporal and spatial stimuli features. NFED provides researchers with fMRI data for understanding of the visual processing of large number of natural facial expression videos.</p>
The downside of large-scale events: (un)sustainable consumption & production in São João festivities
<p>Video resumen de un artículo presentado en el VI Congreso Latinoamericano de Marketing Social en Brasil</p>
CRISP: Critical Path Analysis of Large-Scale Microservice Architectures (Artifact)
<p>This zip includes the original artifact used in the <a href="https://www.usenix.org/system/files/atc22-zhang-zhizhou.pdf">CRISP</a> paper.</p> <p>The appendix in the original paper contains the details on the setup and execution of the artifact. There are three components in this artifact: top-down analysis, bottom-up analysis, and anomaly detection.</p> <p><strong>Note</strong></p> <ul> <li>Due to privacy and security concerns, most unrelated fields and tags are removed.</li> <li>All trace is sanitized consistently. The same service or endpoint will have identical mapping across three directories. (i.e., service 1 represents the same service in all traces). However, the mapping is <strong>inconsistent</strong> with <a href="https://zenodo.org/records/13947828">https://zenodo.org/records/13947828</a>, so please do not mix the traces between the two artifacts. </li> <li>To preserve privacy, the start time of each trace has been randomly shifted. As a result, the start and end times in the traces do not reflect the actual collection times, and users should not attempt to infer when the traces were gathered.</li> <li>Within each trace, the relative durations and timestamps of all spans remained consistent, as the shift was applied uniformly across the entire trace.</li> </ul> <h3>Top-down analysis</h3> <p>Simply run <code>bash top-down.sh</code> should be sufficient. Note the result looks slightly different since this artifact contains a subset of traces from the original paper.</p> <h3>bottom-up analysis</h3> <p><code>bash bottom-up.sh</code> should generate the figure 13-20 from the original paper. Note instead of having 1m+ traces in the original paper, this artifact contains a subset of 100k traces. So generated figure may look slightly different.</p> <h3>Anomaly detection</h3> <p>This part contains the original repo of TraceAnomaly. The dataset contains the original trace of <strong>Service 3</strong> in <strong>Table 2</strong> from the original paper.</p> <p>To run the experiment, first please refer to the appendix for the setup.</p> <p>Then please run the following:</p> <ul> <li><code>bash preprocess.sh</code> to generate the matrices that are used in the training</li> <li>within <code>TraceAnomaly</code> repo <ul> <li>run <code>bash run-training-WG.sh</code> to run the training using the whole graph. Correspond to <strong>Service 3</strong> row, column <strong>TA</strong> in <strong>Table 2</strong>.</li> <li>run <code>bash run-training-CS.sh</code> to run the training using the critical path only. Correspond to <strong>Service 3</strong> row, column <strong>CRISP</strong> in <strong>Table 2</strong>.</li> </ul> </li> <li>run <code>python3.6 parse-rnvp.py -i path_to_rnvp_file</code> to check the result</li> </ul> <p>Note</p> <ul> <li>The traces among <code>train</code>, <code>normal</code>, and <code>abnormal</code> are split randomly from normal traces. The error generation part can be found in lines 137-144 in <code>preprocess.py</code>. It is also described in Section 7.3 Methodology in the original paper. For real error traces, please refer to <a href="https://zenodo.org/records/13947828">https://zenodo.org/records/13947828</a>.</li> </ul> <p>If you use the traces in your research, please cite our paper</p> <blockquote> <pre>@inproceedings{zhang2022crisp, title={$\{$CRISP$\}$: Critical path analysis of $\{$Large-Scale$\}$ microservice architectures}, author={Zhang, Zhizhou and Ramanathan, Murali Krishna and Raj, Prithvi and Parwal, Abhishek and Sherwood, Timothy and Chabbi, Milind}, booktitle={2022 USENIX Annual Technical Conference (USENIX ATC 22)}, pages={655--672}, year={2022} }</pre> </blockquote> <p>If you have more questions, you can reach out to <a href="https://lollllcat.github.io/">Chris(Zhizhou) Zhang</a>.</p>
Large-scale metagenomic analysis of oral microbiomes reveals markers for autism spectrum disorders, MetaPhlAn 3 profiles
Open the record for dataset details and reuse information.
FloodCastBench: A Large-Scale Dataset and Foundation Models for Flood Modeling and Forecasting
<div>Effective flood forecasting is crucial for informed decision-making and emergency response. Existing flood datasets mainly describe flood events but lack dynamic process data suitable for machine learning (ML). This work introduces the FloodCastBench dataset, designed for ML-based flood modeling and forecasting, featuring four major flood events: Pakistan 2022, UK 2015, Australia 2022, and Mozambique 2019. FloodCastBench provides comprehensive low-fidelity and high-fidelity flood forecasting datasets specifically for ML.</div> <div> </div> <div>This dataset comprises three folders: the low-fidelity flood forecasting folder, the high-fidelity flood forecasting folder, and the relevant data folder. The low-fidelity flood forecasting folder includes data on the 2022 Pakistan flood and the 2019 Mozambique flood, both with a spatial resolution of 480 m. The high-fidelity flood forecasting folder contains two subfolders: one for the 2022 Australia flood and the 2015 UK flood with a spatial resolution of 30 m, and another for the same floods with a spatial resolution of 60 m. All data files are stored in TIFF format, with a temporal resolution of 300 seconds, and file names are numbered sequentially, incremented every 300 seconds until the simulation endpoint. The relevant data folder includes five subfiles: DEM, land use and land cover, rainfall data, georeferenced files, and initial condition files. The DEM, land use and land cover, rainfall, and initial condition data are all provided in TIFF format. The rainfall data is organized in a format of year-month-day-hour-minute-second. Georeferenced files provide geographic extent and spatial reference to support viewing and analysis of the associated TIFF files in GIS.</div> <div> </div> <div>FloodCastBench details the process of flood dynamics data acquisition, starting with input data preparation (e.g., topography, land use, rainfall) and flood measurement data collection (e.g., SAR-based maps, surveyed outlines) for hydrodynamic modeling. We deploy a widely recognized finite difference numerical solution to construct high-resolution spatiotemporal dynamic processes with 30-m spatial and 300-second temporal resolutions. Flood measurement data are used to calibrate the hydrodynamic model parameters and validate the flood inundation maps. Furthermore, we establish a benchmark of foundational models for neural flood forecasting using FloodCastBench, validating its effectiveness in supporting ML models for spatiotemporal, cross-regional, and downscaled flood forecasting.</div>
Example dataset for in vivo blood sO2 imaging with ultrasound-aided large-scale optoacoustic microscopy
<p>"mouse1_r_5um_dualwave.mat" and "mouse1_r_10um_us.mat" contain the raw datasets in dual-wavelength optoacoustic and ultrasound modes acquired with ultrasound-aided large-scale optoacoustic microscopy on mouse dorsal skin.</p> <p>"mouse1_r.mat" contains the estimated skin surface based on the ultrasound dataset.</p> <p>After downloading the above 3 files, make a subfolder under the current working folder and name it "skinCropDepths", and put "mouse1_r.mat" under that subfolder.</p> <p>The code can be fetched from this Github repository: https://github.com/razanskylab/dualwaveProcessor. Refer to README file for explanation on the code base.</p> <p>The datasets shared here were acquired on healthy mouse dorsal skin and are intended for testing the unmixing scripts.</p> <p>The longitudinal datasets acquired during dorsal skin wound healing are too large to be publically shared, but is available for research purposes upon reasonable request: weiye.li@uzh.ch.</p> <p> </p> <p> </p>
Data from: A review of the defining chemical properties of soda lakes and pans: an assessment on a large geographic scale of Eurasian inland saline surface waters
The aim of this study is to evaluate the definition of water chemical type, with particular attention to soda brine characteristics by assessing ionic composition and pH values on a large geographic scale and broad salinity (TDS) range of Eurasian inland saline surface waters, in order to rectify the considerable confusion about the exact chemical classification of soda lakes and pans. Data on pH and on the concentration of eight major ions were compiled into a database drawn from Austria, China, Hungary, Kazakhstan, Mongolia, Russia, Serbia, and Turkey. The classification was primarily based on dominant ions exceeding an equivalent percentage of 25 (> 25e%) of the total cations or anions, and the e% rank of dominant ions was also identified. We identified four major types: waters dominated by (1) Na-HCO3 (10.0%), (2) Na-HCO3 + CO3 (31.4%), (3) Na-Cl (45.9%), and (4) Na-SO4 (12.7%), considering only the first ion by e% rank. These major types can be divided into 30 subtypes in the dataset, taking into account the e% rank of all dominant ions. The major and subtypes of soda brine can be divided into "Soda" and "Soda-Saline" types. "Soda type" when Na+ and HCO3– + CO32– are the first in the rank of dominant ions (> 25e%), and "Soda-Saline type" when Na+ is the first in the rank of dominant cations and the sum of HCO3– + CO32– concentration exceeds 25e%, but it is not the first in the rank of dominant anions. Soda-saline type can be considered as a separate evolutionary stage between Soda and Saline types respect to the geochemical interpretation by saturation indexes of brines. The obtained overlapping ranges in distribution demonstrate that a pH measurement alone is not a reliable indicator to classify the permanent alkaline "soda type" and various other types of temporary alkaline waters.
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.