Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,271
datasets available to search
ShareScore release 0.9.0
Dataset results
1,271 results for “Data Flow”
Fig. 3. A–B, Pyrops candelaria. A in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 3. A–B, Pyrops candelaria. A, Chambok, 1.IX.2014 (S. Phauk). B, Kampong Tralach, 21.IX.2013 (O. Rodriguez). C–D, P. coelestinus, Chambok, 1.IX.2014 (S. Phauk). E, P. condorinus, Koh Kong, Tatai, 24.V.2015 (G. Chartier). F, P. ducalis, Mondulkiri, Seima Forest, 13.V.2015 (B. Barca). G–I, P. peguensis. G, Tumpor, Cardamom, 12.VIII.2009 (J. Holden). H, Chambok, 5.V.2015 (J. Constant). I, idem, biotope. J, P. spinolae, Ratanakiri, Veun Sai Siem Pang, 23.II.2015 (Marduk). K, P. viridirostris, Chambok, 7.V.2015 (J. Constant). L–N, Saiva gemmata. L, nymph, Chambok, 7 May 2015 (J. Constant). M, adult tended by a cockroach, Chambok, 7.V.2015 (J. Constant). N, Mondulkiri, Seima Forest, 13.V.2015 (B. Barca). O–Q, Zanna sp. O–P, Koh Kong, Tatai, 1.XI.2012 (G. Chartier). Q, Kampot, 21.XII.2013 (K.W. Meier-Doernberg).
Scaler: Efficient and Effective Cross Flow Analysis (Pre-recorded data for perf)
<p>This is a dataset complement to https://doi.org/10.5281/zenodo.13336658.</p> <p>All the experiments run on the host machine with the same evaluation script and benchmark applications.</p> <p>We provided this because perf report has a bug and may fail to resolve the symbol inside the docker container.</p> <p> </p>
Flow Cytometry Data files for Settle et al. Nature Communications, B2 integrins impose a mechanical checkpoint on macrophage phagocytosis
<p>FCS Data files for all flow cytometry results for Settle et al, B2 integrins impose a mechanical checkpoint on macrophage phagocytosis, to be published in Nature Communications. </p>
Data for Transparent Porous Medium for Optical Fluid Flow Measurement using Refractive Index Matching
<p>This data repository contains shadowgraph images acquired during the design of three transparent porous media using refractive index matching. The images were captured through various liquid mixtures, including toluene/1-hexanol, potassium thiocyanate (KSCN), and cyclohexanol/toluene, both with (PM) and without glass beads (FM). The images were processed and analysed using ImageJ, an open-source software tool.</p>
Supplementary Data for Bayesian material flow analysis of the construction aggregate cycle in England (2019)
<p>Supplementary Data for Bayesian material flow analysis of the construction aggregate cycle in England (2019) by </p> <p><span>Adam R. Mason <sup>1,a</sup>, Tom Bide <sup>2,b</sup>, Junyang Wang <sup>3,c</sup>, John Morley <sup>4,d</sup>, Mohit Arora <sup>5,e</sup>, Alperen Yayla<sup>1,f</sup>, Julia A. Stegemann <sup>5,g</sup>, Rupert J. Myers <sup>1,h,*</sup></span></p> <p><span> </span></p> <p><sup><span>1</span></sup><span> Department of Civil and Environmental Engineering, Imperial College London, UK</span></p> <p><sup><span>2</span></sup><span> British Geological Survey, UK</span></p> <p><sup><span>3 </span></sup><span>Department of Mathematics, Imperial College London, UK</span></p> <p><sup><span>4</span></sup><sub><span> </span></sub><span>Department of Earth Science and Engineering, Imperial College London, UK</span></p> <p><sup><span>5</span></sup><span> School of Engineering, King’s College London, UK</span></p> <p><sup><span>6</span></sup><span> Department of Civil, Environmental and Geomatic Engineering, University College London, UK</span></p> <p><span> </span></p> <p><span>Author e-mails: <sup>a </sup></span><a href="mailto:a.mason19@imperial.ac.uk"><span>a.mason19@imperial.ac.uk</span></a><span>,<sup> b </sup></span><a href="mailto:tode@bgs.ac.uk"><span>tode@bgs.ac.uk</span></a><span>,<sup> c </sup></span><a href="mailto:junyang.wang21@imperial.ac.uk"><span>junyang.wang21@imperial.ac.uk</span></a><span>,<sup> d </sup></span><a href="mailto:john.morley18@imperial.ac.uk"><span>john.morley18@imperial.ac.uk</span></a><span>,<sup> e </sup></span><a href="mailto:mohit.arora@kcl.ac.uk"><span>mohit.arora@kcl.ac.uk</span></a><span>,<sup> f </sup></span><a href="mailto:a.yayla22@imperial.ac.uk"><span>a.yayla22@imperial.ac.uk</span></a><span>,<sup> g </sup></span><a href="mailto:j.stegemann@ucl.ac.uk"><span>j.stegemann@ucl.ac.uk</span></a><span>; *corresponding author:<sup> h</sup> </span><a href="mailto:r.myers@imperial.ac.uk"><span>r.myers@imperial.ac.uk</span></a></p> <p> </p>
Supplementary Material for "Integrating Security-Enriched Data Flow Diagrams Into Architecture-Based Confidentiality Analysis"
<p>Supplementary material for the paper "Integrating Security-Enriched Data Flow Diagrams Into Architecture-Based Confidentiality Analysis". For more information, please see the README.md. For even more information please visit https://dataflowanalysis.org</p>
Data for Pure Shift NMR in Continuous Flow
Open the record for dataset details and reuse information.
Raw flow cytometry data files (fcs files) part 8
<p>These FCS files are raw data for Figure 6A_2 of the following manuscript:</p> <p><strong>Caerin 1.1/1.9-mediated antitumor immunity depends on IFNAR-Stat1 signalling of tumour infiltrating macrophage by autocrine IFNα and is enhanced by CD47 blockade</strong></p> <p>Junjie Li<sup>#1,6</sup>, Yuandong Luo<sup>3</sup>, Quanlan Fu<sup>3</sup>, Shuxian Tang<sup>2</sup>, Pingping Zhang<sup>2</sup>, Ian H. Frazer<sup>5</sup>, Xiaosong Liu<sup>#1, 2</sup>, Tianfang Wang<sup>#4*</sup>, Guoying Ni<sup>#1, 2*</sup></p> <p>1. Key Laboratory of Cancer Immunotherapy of Guangdong tertiary education, Guangdong CAR-T Treatment-Related Adverse Reaction Key Laboratory, The First Affiliated Hospital, Clinical Medical School, Guangdong Pharmaceutical University, Guangzhou 510080, China</p> <p>2. Cancer Research Institute, Foshan First People’s Hospital, Foshan, Guangdong 528000, China</p> <p>3. Medical School of Guizhou University, Guiyang, Guizhou 550000, China</p> <p>4. Centre for Bioinnovation, University of the Sunshine Coast, Maroochydore BC, QLD 4558, Australia</p> <p>5. Diamantia Institute, Translational Research Institute, University of Queensland, Woolloongabba, Brisbane, QLD 4002, Australia</p> <p>6. Zhongao Biopharmaceutical Technology (Guangdong) Co., Ltd, Zhongshan, Guangdong 528403, China</p> <p>* Corresponding author: twang@usc.edu.au, ngy2003@gmail.com</p>
Raw flow cytometry data files (fcs files) part 7
<p>These FCS files are raw data for Figure 6A_1 the following manuscript:</p> <p><strong>Caerin 1.1/1.9-mediated antitumor immunity depends on IFNAR-Stat1 signalling of tumour infiltrating macrophage by autocrine IFNα and is enhanced by CD47 blockade</strong></p> <p>Junjie Li<sup>#1,6</sup>, Yuandong Luo<sup>3</sup>, Quanlan Fu<sup>3</sup>, Shuxian Tang<sup>2</sup>, Pingping Zhang<sup>2</sup>, Ian H. Frazer<sup>5</sup>, Xiaosong Liu<sup>#1, 2</sup>, Tianfang Wang<sup>#4*</sup>, Guoying Ni<sup>#1, 2*</sup></p> <p>1. Key Laboratory of Cancer Immunotherapy of Guangdong tertiary education, Guangdong CAR-T Treatment-Related Adverse Reaction Key Laboratory, The First Affiliated Hospital, Clinical Medical School, Guangdong Pharmaceutical University, Guangzhou 510080, China</p> <p>2. Cancer Research Institute, Foshan First People’s Hospital, Foshan, Guangdong 528000, China</p> <p>3. Medical School of Guizhou University, Guiyang, Guizhou 550000, China</p> <p>4. Centre for Bioinnovation, University of the Sunshine Coast, Maroochydore BC, QLD 4558, Australia</p> <p>5. Diamantia Institute, Translational Research Institute, University of Queensland, Woolloongabba, Brisbane, QLD 4002, Australia</p> <p>6. Zhongao Biopharmaceutical Technology (Guangdong) Co., Ltd, Zhongshan, Guangdong 528403, China</p> <p>* Corresponding author: twang@usc.edu.au, ngy2003@gmail.com</p>
Raw flow cytometry data files (fcs files) part 3
<p>These FCS files are raw data for Figure 3C, 4A and S2D of the following manuscript:</p> <p><strong>Caerin 1.1/1.9-mediated antitumor immunity depends on IFNAR-Stat1 signalling of tumour infiltrating macrophage by autocrine IFNα and is enhanced by CD47 blockade</strong></p> <p>Junjie Li<sup>#1,6</sup>, Yuandong Luo<sup>3</sup>, Quanlan Fu<sup>3</sup>, Shuxian Tang<sup>2</sup>, Pingping Zhang<sup>2</sup>, Ian H. Frazer<sup>5</sup>, Xiaosong Liu<sup>#1, 2</sup>, Tianfang Wang<sup>#4*</sup>, Guoying Ni<sup>#1, 2*</sup></p> <p>1. Key Laboratory of Cancer Immunotherapy of Guangdong tertiary education, Guangdong CAR-T Treatment-Related Adverse Reaction Key Laboratory, The First Affiliated Hospital, Clinical Medical School, Guangdong Pharmaceutical University, Guangzhou 510080, China</p> <p>2. Cancer Research Institute, Foshan First People’s Hospital, Foshan, Guangdong 528000, China</p> <p>3. Medical School of Guizhou University, Guiyang, Guizhou 550000, China</p> <p>4. Centre for Bioinnovation, University of the Sunshine Coast, Maroochydore BC, QLD 4558, Australia</p> <p>5. Diamantia Institute, Translational Research Institute, University of Queensland, Woolloongabba, Brisbane, QLD 4002, Australia</p> <p>6. Zhongao Biopharmaceutical Technology (Guangdong) Co., Ltd, Zhongshan, Guangdong 528403, China</p> <p>* Corresponding author: twang@usc.edu.au, ngy2003@gmail.com</p>
Raw flow cytometry data files (fcs files) part 6
<p>These FCS files are raw data for Figure 5J-L of the following manuscript:</p> <p><strong>Caerin 1.1/1.9-mediated antitumor immunity depends on IFNAR-Stat1 signalling of tumour infiltrating macrophage by autocrine IFNα and is enhanced by CD47 blockade</strong></p> <p>Junjie Li<sup>#1,6</sup>, Yuandong Luo<sup>3</sup>, Quanlan Fu<sup>3</sup>, Shuxian Tang<sup>2</sup>, Pingping Zhang<sup>2</sup>, Ian H. Frazer<sup>5</sup>, Xiaosong Liu<sup>#1, 2</sup>, Tianfang Wang<sup>#4*</sup>, Guoying Ni<sup>#1, 2*</sup></p> <p>1. Key Laboratory of Cancer Immunotherapy of Guangdong tertiary education, Guangdong CAR-T Treatment-Related Adverse Reaction Key Laboratory, The First Affiliated Hospital, Clinical Medical School, Guangdong Pharmaceutical University, Guangzhou 510080, China</p> <p>2. Cancer Research Institute, Foshan First People’s Hospital, Foshan, Guangdong 528000, China</p> <p>3. Medical School of Guizhou University, Guiyang, Guizhou 550000, China</p> <p>4. Centre for Bioinnovation, University of the Sunshine Coast, Maroochydore BC, QLD 4558, Australia</p> <p>5. Diamantia Institute, Translational Research Institute, University of Queensland, Woolloongabba, Brisbane, QLD 4002, Australia</p> <p>6. Zhongao Biopharmaceutical Technology (Guangdong) Co., Ltd, Zhongshan, Guangdong 528403, China</p> <p>* Corresponding author: twang@usc.edu.au, ngy2003@gmail.com</p>
Raw flow cytometry data files (fcs files) part 4
<p>These FCS files are raw data for Figure 3H of the following manuscript:</p> <p><strong>Caerin 1.1/1.9-mediated antitumor immunity depends on IFNAR-Stat1 signalling of tumour infiltrating macrophage by autocrine IFNα and is enhanced by CD47 blockade</strong></p> <p>Junjie Li<sup>#1,6</sup>, Yuandong Luo<sup>3</sup>, Quanlan Fu<sup>3</sup>, Shuxian Tang<sup>2</sup>, Pingping Zhang<sup>2</sup>, Ian H. Frazer<sup>5</sup>, Xiaosong Liu<sup>#1, 2</sup>, Tianfang Wang<sup>#4*</sup>, Guoying Ni<sup>#1, 2*</sup></p> <p>1. Key Laboratory of Cancer Immunotherapy of Guangdong tertiary education, Guangdong CAR-T Treatment-Related Adverse Reaction Key Laboratory, The First Affiliated Hospital, Clinical Medical School, Guangdong Pharmaceutical University, Guangzhou 510080, China</p> <p>2. Cancer Research Institute, Foshan First People’s Hospital, Foshan, Guangdong 528000, China</p> <p>3. Medical School of Guizhou University, Guiyang, Guizhou 550000, China</p> <p>4. Centre for Bioinnovation, University of the Sunshine Coast, Maroochydore BC, QLD 4558, Australia</p> <p>5. Diamantia Institute, Translational Research Institute, University of Queensland, Woolloongabba, Brisbane, QLD 4002, Australia</p> <p>6. Zhongao Biopharmaceutical Technology (Guangdong) Co., Ltd, Zhongshan, Guangdong 528403, China</p> <p>* Corresponding author: twang@usc.edu.au, ngy2003@gmail.com</p>
Raw flow cytometry data files (fcs files) part 1
<p>These FCS files are raw data for Figure 1C and S2A-C of the following manuscript:</p> <p><strong>Caerin 1.1/1.9-mediated antitumor immunity depends on IFNAR-Stat1 signalling of tumour infiltrating macrophage by autocrine IFNα and is enhanced by CD47 blockade</strong></p> <p>Junjie Li<sup>#1,6</sup>, Yuandong Luo<sup>3</sup>, Quanlan Fu<sup>3</sup>, Shuxian Tang<sup>2</sup>, Pingping Zhang<sup>2</sup>, Ian H. Frazer<sup>5</sup>, Xiaosong Liu<sup>#1, 2</sup>, Tianfang Wang<sup>#4*</sup>, Guoying Ni<sup>#1, 2*</sup></p> <p>1. Key Laboratory of Cancer Immunotherapy of Guangdong tertiary education, Guangdong CAR-T Treatment-Related Adverse Reaction Key Laboratory, The First Affiliated Hospital, Clinical Medical School, Guangdong Pharmaceutical University, Guangzhou 510080, China</p> <p>2. Cancer Research Institute, Foshan First People’s Hospital, Foshan, Guangdong 528000, China</p> <p>3. Medical School of Guizhou University, Guiyang, Guizhou 550000, China</p> <p>4. Centre for Bioinnovation, University of the Sunshine Coast, Maroochydore BC, QLD 4558, Australia</p> <p>5. Diamantia Institute, Translational Research Institute, University of Queensland, Woolloongabba, Brisbane, QLD 4002, Australia</p> <p>6. Zhongao Biopharmaceutical Technology (Guangdong) Co., Ltd, Zhongshan, Guangdong 528403, China</p> <p>* Corresponding author: twang@usc.edu.au, ngy2003@gmail.com</p>
Raw flow cytometry data files (fcs files) part 2
<p>These FCS files are raw data for Figure 2D, 2E, 3B, 3D and 3E of the following manuscript:</p> <p><strong>Caerin 1.1/1.9-mediated antitumor immunity depends on IFNAR-Stat1 signalling of tumour infiltrating macrophage by autocrine IFNα and is enhanced by CD47 blockade</strong></p> <p>Junjie Li<sup>#1,6</sup>, Yuandong Luo<sup>3</sup>, Quanlan Fu<sup>3</sup>, Shuxian Tang<sup>2</sup>, Pingping Zhang<sup>2</sup>, Ian H. Frazer<sup>5</sup>, Xiaosong Liu<sup>#1, 2</sup>, Tianfang Wang<sup>#4*</sup>, Guoying Ni<sup>#1, 2*</sup></p> <p>1. Key Laboratory of Cancer Immunotherapy of Guangdong tertiary education, Guangdong CAR-T Treatment-Related Adverse Reaction Key Laboratory, The First Affiliated Hospital, Clinical Medical School, Guangdong Pharmaceutical University, Guangzhou 510080, China</p> <p>2. Cancer Research Institute, Foshan First People’s Hospital, Foshan, Guangdong 528000, China</p> <p>3. Medical School of Guizhou University, Guiyang, Guizhou 550000, China</p> <p>4. Centre for Bioinnovation, University of the Sunshine Coast, Maroochydore BC, QLD 4558, Australia</p> <p>5. Diamantia Institute, Translational Research Institute, University of Queensland, Woolloongabba, Brisbane, QLD 4002, Australia</p> <p>6. Zhongao Biopharmaceutical Technology (Guangdong) Co., Ltd, Zhongshan, Guangdong 528403, China</p> <p>* Corresponding author: twang@usc.edu.au, ngy2003@gmail.com</p>
Raw flow cytometry data files (fcs files) part 5
<p>These FCS files are raw data for Figure 5E-I, 5M and 5N of the following manuscript:</p> <p><strong>Caerin 1.1/1.9-mediated antitumor immunity depends on IFNAR-Stat1 signalling of tumour infiltrating macrophage by autocrine IFNα and is enhanced by CD47 blockade</strong></p> <p>Junjie Li<sup>#1,6</sup>, Yuandong Luo<sup>3</sup>, Quanlan Fu<sup>3</sup>, Shuxian Tang<sup>2</sup>, Pingping Zhang<sup>2</sup>, Ian H. Frazer<sup>5</sup>, Xiaosong Liu<sup>#1, 2</sup>, Tianfang Wang<sup>#4*</sup>, Guoying Ni<sup>#1, 2*</sup></p> <p>1. Key Laboratory of Cancer Immunotherapy of Guangdong tertiary education, Guangdong CAR-T Treatment-Related Adverse Reaction Key Laboratory, The First Affiliated Hospital, Clinical Medical School, Guangdong Pharmaceutical University, Guangzhou 510080, China</p> <p>2. Cancer Research Institute, Foshan First People’s Hospital, Foshan, Guangdong 528000, China</p> <p>3. Medical School of Guizhou University, Guiyang, Guizhou 550000, China</p> <p>4. Centre for Bioinnovation, University of the Sunshine Coast, Maroochydore BC, QLD 4558, Australia</p> <p>5. Diamantia Institute, Translational Research Institute, University of Queensland, Woolloongabba, Brisbane, QLD 4002, Australia</p> <p>6. Zhongao Biopharmaceutical Technology (Guangdong) Co., Ltd, Zhongshan, Guangdong 528403, China</p> <p>* Corresponding author: twang@usc.edu.au, ngy2003@gmail.com</p>
Raw flow cytometry data files (fcs files) part 9
<p> </p> <p>These FCS files are raw data for Figure 6B of the following manuscript:</p> <p><strong>Caerin 1.1/1.9-mediated antitumor immunity depends on IFNAR-Stat1 signalling of tumour infiltrating macrophage by autocrine IFNα and is enhanced by CD47 blockade</strong></p> <p>Junjie Li<sup>#1,6</sup>, Yuandong Luo<sup>3</sup>, Quanlan Fu<sup>3</sup>, Shuxian Tang<sup>2</sup>, Pingping Zhang<sup>2</sup>, Ian H. Frazer<sup>5</sup>, Xiaosong Liu<sup>#1, 2</sup>, Tianfang Wang<sup>#4*</sup>, Guoying Ni<sup>#1, 2*</sup></p> <p>1. Key Laboratory of Cancer Immunotherapy of Guangdong tertiary education, Guangdong CAR-T Treatment-Related Adverse Reaction Key Laboratory, The First Affiliated Hospital, Clinical Medical School, Guangdong Pharmaceutical University, Guangzhou 510080, China</p> <p>2. Cancer Research Institute, Foshan First People’s Hospital, Foshan, Guangdong 528000, China</p> <p>3. Medical School of Guizhou University, Guiyang, Guizhou 550000, China</p> <p>4. Centre for Bioinnovation, University of the Sunshine Coast, Maroochydore BC, QLD 4558, Australia</p> <p>5. Diamantia Institute, Translational Research Institute, University of Queensland, Woolloongabba, Brisbane, QLD 4002, Australia</p> <p>6. Zhongao Biopharmaceutical Technology (Guangdong) Co., Ltd, Zhongshan, Guangdong 528403, China</p> <p>* Corresponding author: twang@usc.edu.au, ngy2003@gmail.com</p>
Enhancing Distributed Summary Synthesis with Data-Flow Analysis
<p>With increasing software complexity, scalable and precise verification is essential, especially in safety-critical areas. Distributed Summary Synthesis (DSS) supports scalability by enabling parallel processing of program segments (blocks). However, it faces limitations in achieving early-stage abstraction due to the inherent laziness of Predicate Analysis, which only refines abstractions when errors are detected. This thesis addresses this by integrating Data-Flow Analysis (DFA) into DSS, enhancing the initial information shared among program blocks to potentially accelerate and improve verification. Implemented in CPAchecker, DFA runs in parallel with Predicate Analysis, providing coarse summaries that strengthen the preconditions for successor blocks. Experimental evaluation using SV-COMP 2024 benchmarks, however, indicated that while DFA integration occasionally improved verification coverage, it also introduced additional resource demands. This increase in CPU time, wall time and memory usage, due to message handling and serialization and deserialization overhead, limited the number of programs that could be verified compared to the DSS implementation with only predicate analysis. This trade-off suggests that additional optimizations are needed to reduce performance costs and better harness the potential of DFA for scalable and effective verification.<br><br></p>
[Supplementary Data] PowerModel-AI: A First On-the-fly Machine-Learning Predictor for AC Power Flow Solutions.
<h1><strong>Abstract</strong></h1> <p>The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to autonomously build models while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce <em>PowerModel-AI</em>, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures the load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model's performance is sub-optimal, based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using <em>PowerModel.jl</em>, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.</p> <div> <h1><strong>Overview</strong></h1> <p>This dataset, provided as supplementary material for the above-referenced study, includes a comprehensive collection of images (plots) from the study’s analyses, along with Jupyter notebooks containing Python scripts used for the training, validation, and testing phases of PowerModel-AI. Additionally, it includes all training and external test data used in this work, generated via LANL-based open-source power flow solver, PowerModels.jl.</p> <p>The primary objective of this dataset is to ensure full reproducibility of the study’s analyses and facilitate critical examination by the scientific community, thereby maximizing the overall impact of the work.</p> <h1>Directory Structure</h1> <p>The hierarchy of folders and file organization of the dataset is illustrated in the chart below. The directory contains a README.md file which contains the information provided here and a requirements.txt that contains all libraries necessary to run the python scripts or Jupyter notebooks in this directory. A brief description of the folders and what they contain are provided below: </p> </div> <div> <strong>.</strong></div> <div>├── <strong>Models/</strong></div> <div>│ ├── _PM_AI_Models/</div> <div>│ │ ├── Type1/</div> <div>│ │ ├── Type2/</div> <div>│ │ └── Type3/</div> <div>│ │</div> <div>│ ├── _PM_AI_Module/</div> <div>│ │ ├── __init__.py</div> <div>│ │ └── PM_Methods.py</div> <div>│ │</div> <div>│ ├── _PM_JL_Data/</div> <div>│ ├── C1_Model/</div> <div>│ ├── C2_Model/</div> <div>│ ├── C3_Model/</div> <div>│ ├── M1_Model/</div> <div>│ ├── M2_Model/ </div> <div>│ └── M3_Model/</div> <div>│</div> <div>├── <strong>NodeSensitivityAnalysis/</strong></div> <div>│ ├── Sensitivity_Plots/</div> <div>│ ├── Sensitivity_PM_JL_Data/</div> <div>│ ├── get_sensitivity_PMJL_data.py</div> <div>│ └── NodeSensitivityAnalysis.ipynb</div> <div>│</div> <div>├── <strong>PlotsForOnTheFlyAnalysis/</strong></div> <div>│ ├── BaseModel_A/</div> <div>│ ├── BaseModel_B/ </div> <div>│ └── BaseModel_C/</div> <div>│ </div> <div>├── <strong>README.md</strong></div> <div>└── <strong>requirements.txt</strong></div> <p> </p> <h2>Files Description </h2> <h3><strong>1. </strong><strong>Models/</strong></h3> <p><strong><em>_PM_AI_Models/</em></strong> contains the ML models discussed in the manuscript. Type1, Type2, and Type3 refers to the C and M models with numbers "1", "2," and "3". Each Type folder contains individual subfolders for the synthetic grids discussed.</p> <p><strong> <em>_PM_AI_Module/</em></strong> contains a python script that has all the functions used in model training and analysis. It is imported in the Jupyter notebooks in the C and M subfolders in this directory.</p> <p><strong><em>_PM_JL_Data/</em> </strong>contains the following subfolders:</p> <p>a) <strong> </strong><em>_</em><strong><em><strong>G</strong>eneratePowerModelData/</em></strong> has a python script (<u>get_PowerModelJLData.py)</u> that is used to parse <u>PowerModels.jl</u> to compute AC power flow solutions for different power demand configurations. It also contains a subfolder, <strong><em>BusData_MATLAB/</em></strong>, that has all the synthetic grids used in the study and in MATLAB format.</p> <p>b) It also contains other subfolders (not shown in the chart above) that contains AC power flow solutions generated using LANL’s PowerModels.jl, for each synthetic grids and other grid-related data.</p> <p>The folders starting with C and M are the control and candidate models (more details in manuscript). Each folder contains Jupyter notebooks (for each power grid) that has python algorithms used in model training and testing, as well as functions to analyze and plot the prediction performance of the models. It accesses (if already trained) or stores (if newly trained) the models in the <strong><em>_PM_AI_Models/</em></strong> directory. Additionally, they contain 2 subfolders (not shown in the chart above): <strong>AbsoluteErrorPlots/</strong> and <strong>PredictionPlots/</strong> were the results (plots) from the analyses are stored. Some of these results are shown in the manuscript (<em>Figures 4</em>,<em> 5</em>, <em>6</em>, and <em>7</em>).</p> <h3><strong>2. </strong><strong>NodeSensitivityAnalysis/</strong></h3> <p>This folder contains the tools used for the node dependency analysis in Section 3.1.1 of the manuscript.</p> <p>It contains a python script called get_sensitivity_PMJL_data.py, which has similar operation like the <u>get_PowerModelJLData.py</u> script but only compute changes for one bus at a time (see details in manuscript). There is also a Jupyter notebook called <u>NodeSensitivityAnalysis.ipynb </u>that analyzes the bus node dependencies and produces the plots that are shown in Figure 3 in the manuscript. It contains 2 additional sub-folders:</p> <p>a) <strong> <em>Sensitivity_PM_JL_Data/</em></strong> where PowerModels.jl generated AC power flow solution data are stored, and</p> <p>b) <em><strong>Sensitivity_Plots/</strong> </em>where the results from <u>NodeSensitivityAnalysis.ipynb</u> are stored.</p> <h3><strong>3. </strong><strong>PlotsForOnTheFlyAnalysis/</strong></h3> <p>This directory contains only the results for the discussion in Section 3.2 in the manuscript, which is the on-the-fly implementation of PowerModel-AI. The on-the-fly algorithm will be provided and distributed separately in the PowerModel-AI package, which will be publicly available through LANL’s <a href="https://github.com/lanl-ansi" target="_blank" rel="noopener">The Advanced Network Science Initiative</a> (Github). It contains 3 subfolders with similar names but ends with "A", "B," and "C" which correspond to the designations shown and discussed in <em>Figure 8</em> of the manuscript.</p> <h1>Summary</h1> <h3><strong> </strong><strong>Models/</strong></h3> <p><strong>1. _PM_AI_Models/: </strong></p> <p>Contains the machine learning models discussed in the manuscript. The Type1, Type2, and Type3 folders refer to C and M models labeled "1", "2," and "3". Each type folder includes subfolders for the corresponding synthetic grids analyzed.</p> <div><strong>2. _PM_AI_Module/: </strong> </div> <div>Contains Python scripts for model training and analysis. PM_Methods.py is the script that defines functions for model training and analysis, which are imported in the Jupyter notebooks in the C and M subfolders. </div> <div> </div> <div><strong>3. _PM_JL_Data/: </strong></div> <div>-_GeneratePowerModelData/: Contains a Python script (get_PowerModelJLData.py) used to parse PowerModels.jl and compute AC power flow solutions for various power demand configurations. This folder also includes BusData_MATLAB/, which holds the synthetic grid data in MATLAB format. </div> <div>- Other Subfolders: Contain PowerModels.jl AC power flow solutions for each synthetic grid, as well as related data.</div> <div> </div> <div><strong>4. C1_Model/, C2_Model/, C3_Model/, M1_Model/, M2_Model/, M3_Model/: </strong></div> <div>These folders contain the control and candidate models (refer to manuscript details). Each folder includes pre-run Jupyter notebooks for model training and analysis, as well as two subfolders: </div> <div> - <em>AbsoluteErrorPlots/</em>: Contains saved analysis results for each grid.</div> <div> <strong> </strong>- <em>PredictionPlots/</em>: Stores model prediction results. </div> <div>Some results are shown in <em>Figures 4, 5, 6,</em> and <em>7</em> of the manuscript and can be reproduced using the notebooks.</div> <h3>NodeSensitivityAnalysis/</h3> <div><strong>1. Node Dependency Analysis Tools: </strong> </div> <div> This folder contains scripts and data used for the node dependency analysis in Section 3.1.1 of the manuscript.</div> <div> </div> <div><strong>2. Scripts: </strong></div> <div> - get_sensitivity_PMJL_data.py: Computes power flow changes for individual buses (refer to the manuscript for details). </div> <div> - NodeSensitivityAnalysis.ipynb: Analyzes dependencies and generates plots for Figure 3 in the manuscript. </div> <div> </div> <div><strong>3. Subfolders:</strong> </div> <div> - Sensitivity_PM_JL_Data/: Stores PowerModels.jl data for sensitivity analysis. </div> <div> - Sensitivity_Plots/: Contains the generated results from the Jupyter notebook.</div> <h3>PlotsForOnTheFlyAnalysis/</h3> <div><strong>On-the-Fly Learning Results: </strong>Contains the results for the on-the-fly learning analysis discussed in Section 3.2 of the manuscript. The subfolders BaseModel_A/, BaseModel_B/, and BaseModel_C/ correspond to the designations in <em>Figure 8</em> of the manuscript. These subfolders contain plots generated during analysis.</div> <h3>Additional Files</h3> <div>- README.md: This file.</div> <div>- requirements.txt: Contains a list of necessary Python libraries required to run the scripts and Jupyter notebooks.</div> <h3>Notes:</h3> <div>- The PowerModel-AI code is publicly available on GitHub.</div> <div>- The supplementary materials include additional Jupyter notebooks and results for each power grid analysis. </div> <div>- The generated results and predictions in this repository are consistent with those discussed in the manuscript.</div>
Data from: Extensive gene flow over Europe and possible speciation over Eurasia in the ectomycorrhizal basidiomycete Laccaria amethystina complex.
Biogeographic patterns and large-scale genetic structure have been little studied in ectomycorrhizal fungi, despite the ecological and economic importance of ectomycorrhizal symbioses. We coupled population genetics and phylogenetic approaches to understand spatial structure in fungal populations on a continental scale. Using 9 microsatellite markers, we characterised gene flow among 16 populations of the widespread ectomycorrhizal basidiomycete Laccaria amethystina over Europe (over 2900km). We also widened our scope to two additional populations from Japan (104 km away), and compared them with European populations through microsatellite markers and multi-locus phylogenies, using 3 nuclear genes (NAR, G6PD and ribosomal DNA) and two mitochondrial ribosomal genes. European L. amethystina populations displayed limited differentiation (average FST=0.041) and very weak isolation by distance. This panmictic European pattern may result from effective aerial dispersal of spores, high genetic diversity in populations, and mutualistic interactions with multiple hosts that all facilitate migration. The multi-locus phylogeny based on nuclear genes confirmed that Japanese and European specimens were closely related but clustered on a geographical basis. By using microsatellite markers, we found that Japanese populations were strongly differentiated from the European populations (FST=0.416), more than expected by extrapolating the European pattern of isolation by distance. Population structure analyses clearly separated the populations into two clusters, European and Japanese clusters. We discuss the possibility of isolation by distance in a continuous population (considering some evidence for a ring species over the Northern Hemisphere) versus an allopatric speciation over Eurasia, making L. amethystina a promising model of intercontinental species for future studies.
Data from: Scale-dependent effects of landscape variables on gene flow and population structure in bats
Aim: A common pattern in biogeography is the scale-dependent effect of environmental variables on the spatial distribution of species. We tested the role of climatic and land cover variables in structuring the distribution of genetic variation in the grey long-eared bat, Plecotus austriacus, across spatial scales. Although landscape genetics has been widely used to describe spatial patterns of gene flow in a variety of taxa, volant animals have generally been neglected because of their perceived high dispersal potential.Location: England and Europe. Methods: We used a multiscale integrated approach, combining population genetics with species distribution modelling and geographical information under a causal modelling framework, to identify landscape barriers to gene flow and their effect on population structure and conservation status. Genotyping involved 23 polymorphic microsatellites and 259 samples from across the species' range. Results: We identified distinct population structure shaped by geographical barriers and evidence of population fragmentation at the northern edge of the range. Habitat suitability (as captured by species distribution models, SDMs) was the most important landscape variable affecting genetic connectivity at the broad spatial scale, while at the fine scale, lowland unimproved grasslands, the main foraging habitat of P. austriacus, played a pivotal role in promoting genetic connectivity. Main conclusions: The importance of lowland unimproved grasslands in determining the biogeography and genetic connectivity in P. austriacus highlights the importance of their conservation as part of a wider landscape management for fragmented edge populations. This study illustrates the value of using SDMs in landscape genetics and highlights the need for multiscale approaches when studying genetic connectivity in volant animals or taxa with similar dispersal abilities.
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.