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9,152 results for “Networking”
NoSyms: A neural network approach to detecting data structures in raw memory
<p>This data was used for a experiments with graph convolutional neural networks for memory forensics as part of a bachelor thesis (included as pdf).<br> <br> Abstract:<br> <br> This work presents a neural network based approach for data structure detection in raw memory that does not require an entirely matching description of the target data structure. Instead, it’s merely necessary to provide multiple descriptions of data structures similar to the target as training data in the form of debugging symbols. The core contribution of this work is a formal description and implementation of encoding data structure definitions as well as raw memory contents such that they can be processed by graph convolutional neural networks. A description and implementation of a neural network meant to detect data structures in the memory contents of a Linux Kernel demonstrates the practical applicability of the described approach.<br> <br> The Code is available on GitHub <a href="https://github.com/NiklasBeierl/nosyms">https://github.com/NiklasBeierl/nosyms</a>.<br> <br> nokaslr_dump is the qemu memory snapshot used to test the model.<br> nokaslr.raw is the "raw" form of the snapshot as produced by Volatility 3's layerwriter plugin.<br> symbols-training-data contains the Volatility symbol JSON files from which training data was derived.<br> nokaslr_pointers.csv lists the kernel space pointers in the snapshot and<br> nokaslr_tasks.csv lists task structs in the snapshot. Both were extracted via a Volatility plugins that are included in the GitHub Repo.<br> vmlinux-5.4.0-58-generic.json is the symbol file for the kernel the snapshot was taken from.<br> other-symbols.zip contains symbol files I generated vor various other kernels but did not end up using, use at your own discretion.</p>
Self-Assessment Questions - Operating Systems and Computer Networks
<p><strong>What does the dataset contain?</strong><br> The dataset contains a list of XML files and JPG images. The XML files contain a description of a certain type of self-assessment questions to be load and processed by a plugin called <em>qtype_selfassess</em> [1] for the Moodle learning management system.</p> <p>The XML files were names as follow:</p> <pre><code><number of course unit>-<question-ID>-selfassess-<short name of the qestion>.xml</code></pre> <p>The file unit_1-262-selfassess-Caching.xml for instance, is about a self-assessment question of the first course unit ("unit_1") that has the ID 263, and is about the topic "Caching". Most of the short names are in German since the content of the files is German as well.<br> The JPG files are referenced in the XML files.</p> <p><strong>What is the purpose and origin of the data?</strong><br> In order to give students the chance of iterative improvement, the SelfAssess-plugin [1], that allows students to create and assess solutions unlimited times, was implemented in Moodle. Students can open a question to start work. The plugin shows the question and asks the student to create and upload a solution. Solutions may be uploaded as image (e.g. photo, scan) or PDF file. Thereafter, the plugin presents a list of instructor-defined assessment criteria and a link to the sample solution, and asks to assess the uploaded solution. The plugin then presents feedback to the students based on their self-assessment. For this purpose, instructors defined an error tree for each question that maps typical errors to detect one or a combination of not considered criteria on one feedback text. Feedback texts help the student to improve their solution, e.g. by providing learning goal feedback (such as misconceived concepts) and learning process feedback (such as links to relevant resources, things to improve, or activities to do), until they self-assess their solution as correct or as good enough. After receiving the feedback, students could either improve their solution by performing a new iteration of the process (create, upload, self-assess the improved solution again, receive feedback, and take or decline another iteration) or finish the exercise.</p> <p>For the mentioned plugin 43 self-assessment questions including assessment criteria and feedback have been created for fundamental computer science course about operating systems and computer networks. The course was offered at FernUniversität in Hagen, Germany, in winter term 2019/20 and 2020/21 by Professor Dr. Jörg M. Haake.</p> <p>Further information about the plugin and the proposed self-assessment question type can be found in [2] and [3].</p> <p><strong>References</strong><br> [1] Steinkohl, K., Burchart, M., Haake, J.M., Seidel, N. (2020). SelfAssess Question Type Plugin for<br> Moodle. https://github.com/D2L2/qtype_selfassess<br> [2] Haake, J. M.; Seidel, N.; Karolyi, H.; Ma, L.: Self-Assessment mit High-Information Feedback. DELFI 2020–Die 18. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V., 2020.<br> [3] Haake, J. M., Seidel, N., Karolyi, H., & Burchart, M. (2021). Accuracy of self-assessments in higher education. In DELFI 2020 – Die 19. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V. Bonn.</p> <p> </p> <p><strong>Acknowledgments.</strong></p> <p>This research was supported by the Research Cluster "Digitalization, Diversity and Lifelong Learning – Consequences for Higher Education" (D2L2) of the FernUniversität in Hagen, Germany.</p>
Dataset of 'Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks'
<p>Dataset of the article 'Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks' (https://doi.org/10.1016/j.cma.2023.116652). The codes processing data here are on https://github.com/AlvaroMS90/Complete-flow-characterization-from-snapshot-PIV-fast-probes-and-physics-informed-neural-networks.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085) and by MCIN/AEI /10.13039/501100011033 and the European Union ‘NextGenerationEU/PRTR’ as part of the grant FJC2020-044342-I.</p>
Data release for "OrchID: a Generalized Framework for Taxonomic Classification of Images Using Evolved Artificial Neural Networks"
<p><strong>Abstract</strong></p> <p>Taxonomic expertise for the identification of species is rare and costly. On-going advances in computer vision and machine learning have led to the development of numerous semi- and fully automated species identification systems. However, these systems are rarely agnostic to specific morphology, rarely can perform taxonomic “approximation” (by which we mean partial identification at least to higher taxonomic level if not to species), and frequently rely on costly scientific imaging technologies.</p> <p>We present a generic, hierarchical identification system for automated taxonomic approximation of organisms from images. We assessed the effectiveness of this system using photographs of slipper orchids (Cypripedioideae), for which we implemented image pre-processing, segmentation, and colour and shape feature extraction algorithms to obtain digital phenotypes for 116 species. The identification system trained on these digital phenotypes uses a nested hierarchy of artificial neural networks for pattern recognition and automated classification that mirrors the Linnean taxonomy, such that user-submitted photos can be assigned a genus, section, and species classification by traversing this hierarchy.</p> <p>Performance of the identification system varied depending on photo quality, number of species included for training, and desired taxonomic level for identification. High quality photos were scarce for some taxa and were under-represented in the training set, resulting in imbalanced network training. The image features used for training were sufficient to reliably identify photos to the correct genus but less so to the correct section and species.</p> <p>The outcomes of this project include a library of feature extraction algorithms called <em>ImgPheno</em>, a collection of scripts for neural network training called <em>NBClassify</em>, a library for evolutionary optimization of artificial neural network construction called <em>AI::FANN::Evolving</em> and a planned web application called <em>OrchID</em> for identification of user-submitted images. All project outcomes are open source and freely available.</p> <p><strong>About this release</strong></p> <p>This release corresponds belongs with our response to the reviewers of PLoS One. At this stage of the review cycle the manuscript is assessed as 'minor revision'. Consequently, we don't anticipate making more releases until publication.</p>
Supplement for "Using Phylogenetic Networks to Model Chinese Dialect History"
<p>This is the supplementary material accompanying the paper "Using Phylogenetic Networks to Model Chinese Dialect History", which appeared in 2014 in "Language Dynamics and Change" (volume 4, issue 2).</p>
Convex inference for community discovery in signed networks (European Parliament Voting Dataset)
<p>This repository contains the necessary tools to reproduce the experiments of the paper</p> <ul> <li>G. Santatmaría, V. Gómez (2015)<br> Convex inference for community discovery in signed networks.<br> NIPS 2015 Workshop: Networks in the Social and Information Sciences</li> </ul> <p>The method first maps the MAP problem on the Potts model as a hinge-loss minimization problem (see the paper for details). To run the code you need to install psl (included here) and if you want to additionally compare with other inference methods, such as max prod belief propagation or junction tree, you need to install the libDAI library (also included here)</p> <p>The directory europeanCongressData/ (~500 Mb) contains the votings of the EU parlament, including 300 votings events from the actual term, from May 2014 to June 2015, obtained from http://www.votewatch.eu/</p> <ul> <li>data/ : json files with the european votes</li> <li>network.net : signed network built from the votes</li> <li>political_parties.txt : "ground truth" party</li> <li>community_results/ : results for different number of communities and initial vertices</li> <li>dataComputations.py : used to build the signed network</li> <li>dataProcessing.py : used to build the signed network</li> </ul> <p>We would appreciate if you cite the paper after using the data or the code.</p> <p>DEPENDENCIES</p> <p>The code has been tested in Linux Mint 18.1 Serena and Ubuntu 14.04</p> <p>- For PSL library, you need to have<br> java 1.8<br> you may need to export JAVAHOME='/usr/lib/jvm/YOURJAVA1.8FOLDER'<br> maven 3.x</p> <p>- For libDAI you will need:<br> make doxygen graphviz libboost-dev libboost-graph-dev libboost-program-options-dev libboost-test-dev libgmp-dev cimg-dev libgmp-dev</p> <p>CODE TO RUN THE FOLLOWING EXPERIMENTS:</p> <p>Compare the performance in terms of structural balance of max prod bp and our method against an exact inference method (junction tree), with different number of communities</p> <p>INSTALL</p> <p>To install the experiments you have to follow the next steps:</p> <p>1 Build the libdai library by doing: make -B on the folder (libdai)</p> <p>2 Generate the class path of the groovy project:<br> mvn clean install<br> mvn dependency:build-classpath-Dmdep.outputFile=classpath.out</p> <p>on the psl root folder (You need to have java 1.8 and maven 3.x installed)</p> <p>3 Grant exec permissions to the run.sh script</p> <p>Options</p> <p>The main python file to run the experiments is</p> <p>evaluatebalanceon_sn.py.</p> <p>It accepts the following parameters:</p> <p>1 (Int) Nodes of the graph. In order to run the junction tree we recommend to set this paremeter to 150 or less<br> 2 (Int) The number of underlying communities<br> 3 (Float) The maximum amount of unbalance for the experiments. We recommend 0.45<br> 4 (Bool) Whether to use an heuristic to find the initial node for each community or to use directly random nodes from the ground truth communities. This heuristic looks alternatively for the nodes with highest negative degree and highest positive degree. For the case when the number of communities is equal to 2 (Ising Model), the heuristic is used by default.</p> <p>An example of execution would be:</p> <p>python evaluate_balance_on_sn.py 120 3 0.45 True True</p> <p>The results of the experiments are save in the folder results/<br> Scripts</p> <p>The main script of the hinge-loss method can be found in the folder psl/psl-example/src/main/java/edu/umd/cs/example/PottsCommunities.groovy</p> <p>Authors:</p> <p>Guillermo Santamaria & Vicenc Gomez<br> Mar 5, 2017</p> <p>For further questions, please contact vicen.gomez@upf.edu</p>
Identification of strengths and weaknesses of cooperative efforts within the wider Caribbean using a network approach
<p>Dataset associated to Ramírez-Ramírez RD, Montilla LM, Cavada-Blanco F and Croquer A. Identification of strengths and weaknesses of cooperative efforts within the wider Caribbean using a collaboration network approach [version 1; not peer reviewed]. <em>F1000Research</em> 2016, <strong>5</strong>:799 (poster) (doi: 10.7490/f1000research.1111809.1)</p>
ANEE Lexical Networks v. 2.0 - the dataset
<p>The Dataset of the <a href="https://urn.fi/urn:nbn:fi:lb-2024121101" target="_blank" rel="noopener">ANEE Lexical Networks v.2.0</a> created in the Semantic Domains in Akkadian Texts project at the University of Helsinki. This version 1.1 also includes the networks as GEXF files.</p>
Data archive: CICT for single cell RNA-seq network inference
<p>This archive contains benchmarking input data and results for using single cell gene expression data to infer gene regulatory networks (GRN) by the Causal Inference with Composition of Transactions (CICT) method and a selected set of published methods. This accompanies the manuscript "Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions" (Shojaee and Huang, Brief in Bioinform 2023. DOI: 10.1093/bib/bbad370). The CICT code is available at the GitHub repo (https://github.com/hlab1/scRNAseqWithCICT/).</p><p>The original CICT algorithm was described in Shojaee et al. (arXiv:1608.02658, 2016). The benchmarked methods were included in the BEELINE benchmarking pipeline (Pratapa et al., Nat Methods 2020), to which we added DEEPDRIM (Chen et al., Brief Bioinform 2021), SCENIC (Aibar et al., Nat Methods 2017), Inferelator 3.0 (Gibbs et al., Bioinformatics 2022), and CellOracle (Kamimoto et al., Nature 2023). The output directory names are (subdirectories within each dataset):</p><p>* CICT_ewMIshrink_RFmaxdepth10_RFntrees20/: CICT for simulated data<br>* CICT_v2/: CICT for experimental data<br>* CELLORACLEDB/: CellOracle for experimental data<br>* DEEPDRIM72_ewMIshrink_RFmaxdepth10_RFntrees20/: DEEPDRIM for simulated data<br>* DEEPDRIM72_v2/: DEEPDRIM for experimental data<br>* INFERELATOR38_ewMIshrink_RFmaxdepth10_RFntrees20/: Inferelator-Prior for simulated data<br>* INFERELATOR38_v2/: Inferelator-Prior for experimental data<br>* INFERELATOR34_ewMIshrink_RFmaxdepth10_RFntrees20/: Inferelator-NoPrior for experimental data<br>* INFERELATOR34_v2/: Inferelator-NoPrior for experimental data<br>* GENIE3/: GENIE3<br>* GRNBOOST2/: GRNBOST2<br>* LEAP/: LEAP<br>* PIDC/: PIDC<br>* PPCOR/: PPCOR<br>* SCENICDB/: SCENIC for experimental data<br>* SCNS/: SCNS<br>* SCODE/: SCODE<br>* SCRIBE/: SCRIBE<br>* SINCERITIES/: SINCERITIES<br>* SINGE/: SINGE<br>* RANDOM/: RANDOM</p><p>The methods were benchmarked against two kinds of scRNA-seq datasets:<br>* Simulated datasets produced by the SERGIO simulator from a synthetic network (Dibaeinia et al., Cell Systems 2020), including complete datasets and datasets with dropouts with shape parameter k=6.5 and rate parameter q=10, 30, 50, 70, 80. <br>* Experimental datasets compiled by the BEELINE pipeline, evaluated at three different levels L0, L1 and L2, with three types of ground truth networks.<br> * Evaluation levels:<br> * L0: 500 highly varying genes plus TFs<br> * L1: 1000 highly varying genes plus TFs<br> * L2: 500 highly varying genes, TFs and 500 genes randomly selected that excluded the 1000 highly varying genes from L1.<br> * Types of ground truths:<br> * Cell-type-specific ChIP-seq ground truth (L0, L1, L2)<br> * Non-specific ChIP-seq ground truth (L0_ns, L1_ns, L2_ns)<br> * Loss-of-function/gain-of-function ground truth (L0_lofgof, L1_lofgof, L2_lofgof)</p><p>The directory structure is organized in accordance with the BEELINE benchmarking pipeline. For complete details please please see the BEELINE documentation (https://murali-group.github.io/Beeline/) and Github repo (https://github.com/Murali-group/Beeline).</p><p> </p>
Robustness assessment of a C++ implementation of a quantized (int8) version of the LeNet-5 convolutional neural network
<p>The architecture of the LeNet-5 convolutional neural network (CNN) was defined by LeCun in its paper "Gradient-based learning applied to document recognition" (<a href="https://ieeexplore.ieee.org/document/726791">https://ieeexplore.ieee.org/document/726791</a>) to classify images of hand written digits (MNIST dataset).</p><p>This architecture has been customized to use Rectified Linear Unit (ReLU) as activation functions instead of Sigmoid, and 8-bit integers for weights and activations instead of floating-point.</p><p>It consists of the following layers:</p><ul><li><strong>conv1</strong>: Convolution 2D, 1 input channel (28x28), 3 output channels (28x28), kernel size 5, stride 1, padding 2.</li><li><strong>relu1</strong>: Rectified Linear Unit (3@28x28).</li><li><strong>max1</strong>: Subsampling buy max pooling (3@14x14).</li><li><strong>conv2</strong>: Convolution 2D, 3 input channels (14x14), 6 output channels (14x14), kernel size 5, stride 1, padding 2.</li><li><i><strong>relu2</strong></i>: Rectified Linear Unit (6@14x14).</li><li>max2: Subsampling buy max pooling (6@7x7).</li><li><i><strong>fc1</strong></i>: Fully connected (294, 147)</li><li><i><strong>fc2</strong></i>: Fully connected (147, 10)</li></ul><p>The fault hypotheses for this work include the occurrence of:</p><ul><li><strong>BF</strong>: single, double-adjacent and triple-adjacent bit-flip faults</li><li><strong>S0</strong>: single, double-adjacent and triple-adjacent stuck-at-0 faults</li><li><strong>S1</strong>: single, double-adjacent and triple-adjacent stuck-at-1 faults</li></ul><p>In the memory cells containing all the parameters of the CNN: </p><ul><li><strong>w</strong>: weights (int8)</li><li><strong>zw</strong>: zero point of the weights (int8)</li><li><strong>b</strong>: biases (int32)</li><li><strong>z</strong>: zero point (int8)</li><li><strong>m</strong>: m (int32)</li></ul><p>Images 200 to 249 from the MNIST dataset have been used as workload.</p><p>This dataset contains the raw data obtained from running exhaustive fault injection campaigns for all considered fault models, targeting all considered locations and for all the images in the workload.</p><p>In addition, the raw data have been lightly processed to obtain global data related to the particular bits and parameters affected by the faults, and the obtained failure modes.</p><h3>Files information</h3><ul><li><i>golden_run.csv</i>: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. </li><li><i>single_faults/bit_flip</i> folder: Prediction obtained for all the images considered in the workload in presence of single bit-flip faults. There is one file for each parameter of each layer.</li><li><i>single_faults/stuck_at_0</i> folder: Prediction obtained for all the images considered in the workload in presence of single stuck-at-0 faults. There is one file for each parameter of each layer.</li><li><i>single_faults/stuck_at_1</i> folder: Prediction obtained for all the images considered in the workload in presence of single stuck-at-1 faults. There is one file for each parameter of each layer.</li><li><i>double_adjacent_faults/bit_flip</i> folder: Prediction obtained for all the images considered in the workload in presence of double adjacent bit-flip faults. There is one file for each parameter of each layer.</li><li><i>double_adjacent_faults/stuck_at_0</i> folder: Prediction obtained for all the images considered in the workload in presence of double adjacent stuck-at-0 faults. There is one file for each parameter of each layer.</li><li><i>double_adjacent_faults/stuck_at_1</i> folder: Prediction obtained for all the images considered in the workload in presence of double adjacent stuck-at-1 faults. There is one file for each parameter of each layer.</li><li><i>triple_adjacent_faults/bit_flip</i> folder: Prediction obtained for all the images considered in the workload in presence of triple adjacent bit-flip faults. There is one file for each parameter of each layer.</li><li><i>triple_adjacent_faults/stuck_at_0</i> folder: Prediction obtained for all the images considered in the workload in presence of triple adjacent stuck-at-0 faults. There is one file for each parameter of each layer.</li><li><i>triple_adjacent_faults/stuck_at_1</i> folder: Prediction obtained for all the images considered in the workload in presence of triple adjacent stuck-at-1 faults. There is one file for each parameter of each layer.</li></ul><h3>Methodology information</h3><p>First, the CNN was used to classify all the images of the workload in the absence of faults to get a reference to determine the impact of faults. This is golden_run.csv file.</p><p>After that, one fault injection experiment was executed for each bit of each element of each parameter of the CNN.</p><p>Each experiment consisted in:</p><ul><li>Affecting the bits (inverting it in case of bit-flip faults, setting it to 0 or 1 in case of stuck-at-0 or atuck-at-1 faults) identified by the mask.</li><li>Classifying all the images of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Removing the fault from the CNN by restoring the affected bits to its previous value.</li></ul><h3>List of variables (Name : Description (Possible values))</h3><ul><li><strong>IMGID</strong>: Integer number identifying the considered image (200-249).</li><li><strong>TENSORID</strong>: Integer number identiying the parameter affected by the fault (0 - No fault, 1 - conv1.w, 2 - conv1.zw, 3 - conv1.m, 4 - conv1.b, 5 - conv1.z, 6 - conv2.w, 7 - conv2.zw, 8 - conv2.m, 9 - conv2.b, 10 - conv2.z, 11 - fc1.w, 12 - fc1.zw, 13 - fc1.m, 14 - fc.b, 15 - fc1.z, 16 - fc2.w, 17 - fc2.zw, 18 - fc2.m, 19 - fc2.b, 20 - fc2.z)</li><li><strong>ELEMID</strong>: Integer number identiying the element of the parameter affected by the fault (-1 - No fault, [0-2] - {conv1.b, conv1.m, conv1.zw}, [0-74] - conv1.w, 0 - conv1.z, [0-5] - {conv2.b, conv2.m, conv2.zw}, [0-149] - conv2.w, 0 - {conv1.z, conv2.z, fc1.z, fc2.z}, [0-146] - {fc1.b, fc1.m, fc1.zw}, [0-43217] - fc1.w, [0-9] - {fc2.b, fc2.m, fc2.zw}, [0-1469] - fc2.w)</li><li><strong>MASK</strong>: 8-digit hexadecimal number identifying those bits affected by the fault ([00000000 - No fault, FFFFFFFF - all 32 bits faulty])</li><li><strong>FAULT</strong>: String identiying the type of fault (NF - No fault, BF - bit-flip, S0 - Stuck-at-0, S1 - Stuck-at-1)</li><li><strong>OUTPUT</strong>: 10 integer numbers provided by the CNN as output after processing the image. The highest value identifies the selected category for classification.</li><li><strong>SOFTMAX</strong>: 10 decimal numbers obtained after applying the softmax function to the provided output. They represent the probability of the image of belonging to the corresponding category for classification.</li><li><strong>PRED</strong>: Integer number representing the category predicted for the processed image.</li><li><strong>LABEL</strong>: integer number representing the actual category for the processed image.</li></ul>
Future Blood Testing Network+ Overview and Recap - Dr Weizi (Vicky) Li
<p>This video is the first talk from our Future of Healthcare: Remote Blood Testing, Monitoring & AI Meeting that took place on 07-08/11/2023. </p><p>Future Blood Testing Network+ Overview and Recap - Dr Weizi (Vicky) Li (Henley Business School, University of Reading). </p><p>Bio: Weizi (Vicky) Li is a Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is a Fellow of Charted Institute of IT (British Computer Society). She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is currently the Principal Investigator and Director of EPSRC Future Blood Testing for Inclusive Monitoring and Personalised Analytics NetworkPlus; and EPSRC AI for Health project: Advancing machine learning to achieve real-world early detection and personalised disease outcome prediction of inflammatory arthritis. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud-based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 and 4*/3* impact case study in REF 2021 for her research impact on healthcare quality improvement. She is the academic lead of a machine learning-based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received the Research Engagement and Impact award in 2020, shortlisted for 2022 impact award and Health Service Journal (HSJ) patient safety award. </p><p>Further details on this event can be found at: https://www.futurebloodtesting.org/fbtn2023 </p><p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p><p>YouTube Link: https://youtu.be/Fqqekmhg79Q?si=l74rXxTFMx42mEsV</p>
Artificial Neural Networks-generated Dataset: pH, Total Alkalinity, and Hydrogen Ion Concentration in Ría de Vigo (NW Spain), 1995–2020
<p>This dataset comprises input data from INTECMAR and the predicted outcomes. The variables and their units are as follows:</p> <p>station: 'Station ID [1-6]'</p> <p>year: 'Year [1995-2020]'</p> <p>month: 'Month [1-12]'</p> <p>day: 'Day'</p> <p>latitude: 'Latitude (decimal degrees)'</p> <p>longitude: 'Longitude (decimal degrees)'</p> <p>depth: 'Depth (meters)'</p> <p>temperature: 'Temperature (degrees Celsius)'</p> <p>salinity: 'Salinity (psu)'</p> <p>phosphate: 'Phosphate (umol/kg)'</p> <p>nitrate: 'Nitrate (umol/kg)'</p> <p>silicate: 'Silicate (umol/kg)'</p> <p>cweek: 'Cosine week'</p> <p>sweek: 'Sine week'</p> <p>TA: 'Total Alkalinity predicted (umol/kg)'</p> <p>NTA: 'Normalized Total Alkalinity (umol/kg)'</p> <p>NAT_st: 'Normalized per station Total Alkalinity (umol/kg)'</p> <p>NTA_gl: 'Normalized globally Total Alkalinity (umol/kg)'</p> <p>pHTS_insitu: 'pH insitu (pH units)'</p> <p>HT: 'Hydrogen ion concentration predicted (nmol/kg)'</p> <p> </p> <p>The authors gratefully acknowledge the financial support by the Programa de axudas á etapa predoutoral da Xunta de Galicia (Axencia Galega de Innovación) (Grant nº IN606A-2022/025). F.F.P. and A.V. were supported by REDEIRA (TED2021-132188B-I00) project, funded by MCIN/AEI/10.13039/501100011033. The authors also express their gratitude to the Instituto Tecnolóxico para o Control do Medio Mariño de Galicia (INTECMAR), for the analyses and production of the database used to make predictions.</p>
Toxic Content Detection in online social networks: a new dataset from Brazilian Reddit Communities
<p>This is new dataset of 2,500 manually annotated examples of comments extracted from the top 10 largest Brazilian subreddits on Reddit. The dataset has been annotated by crowd-sourcing efforts with contributions from the departments of computer science (DCC) and the linguistic group @ UFMG. As part of our contribution to the toxicity automatic detection and moderation of online social networks, we're making the dataset public for research.</p> <h3>Dataset</h3> <p>The dataset contains 2,500 manually annotated comments from the most popular brazilian communities on Reddit. The data sampling proccess was a stratified sampling by the number of generated publications by subreddit and the month of publication. The list of communities collected is presented below. The collected data period ranges from January 2022 to December 2022.</p> <p> </p> <table> <tbody> <tr> <td><strong>Subreddit</strong></td> <td><strong>Posts</strong></td> <td><strong>Comments</strong></td> </tr> <tr> <td>r/brasil</td> <td>110,829 </td> <td>2,136,866</td> </tr> <tr> <td>r/desabafos</td> <td>115,876</td> <td>1,211,643</td> </tr> <tr> <td>r/futebol</td> <td>35,826</td> <td>1,214,412</td> </tr> <tr> <td>r/saopaulo</td> <td>7,308</td> <td>81,969</td> </tr> <tr> <td>r/eu_nvr</td> <td>12,631</td> <td>188,620</td> </tr> <tr> <td>r/botecodoreddit</td> <td>7,059</td> <td>57,298</td> </tr> <tr> <td>r/conversas</td> <td>21,967</td> <td>326,061</td> </tr> <tr> <td>r/investimentos</td> <td>9,756</td> <td>141,823</td> </tr> <tr> <td>r/tiodopave</td> <td>2,371</td> <td>11,584</td> </tr> <tr> <td>r/brasilivre</td> <td>67,301</td> <td>1,219265</td> </tr> <tr> <td>Total</td> <td>390,924</td> <td>6,589,541</td> </tr> </tbody> </table> <p> </p> <h3><strong>Annotation proccess</strong></h3> <p>The annotators were divided into groups of raters and each group was assigned a batch of comments to label. The raters were then asked to label a comment as <strong>Toxic</strong>, <strong>Non-toxic</strong>, <strong>I do not know</strong> and <strong>Missing info</strong>. During the annotation process, the raters were encouraged to assign one of the uncertain labels when they're not sure about the toxicity of a comment or the context is missing. </p> <h3>Available data</h3> <p>The dataset is available as csv file and the label was assigned as a majority vote among the raters. The available data are the original collected comment id and body. The label was created from the original classification from the annotators. No data processing has been done on this version of the dataset. The overall schema of the dataset if presented below.</p> <p>- <strong>id</strong>: The unique identifier of the comment on the Reddit platform<br>- <strong>body</strong>: The original comment text publication<br>- <strong>is_toxic</strong>: The final label of a given comment. The label is <strong>0</strong> for non-toxic comments, <strong>1</strong> for toxic comments and <strong>-1</strong> for comments where the raters disagreed about the toxicity.</p>
Dataset from: Spatially heterogeneous shifts in vegetation phenology induced by climate change threaten the integrity of the avian migration network
<p>Original data and code for the study:</p> <p>Wei, J., Xu, F., Cole, E. F., Sheldon, B. C., de Boer, W. F., Wielstra, B., Fu, H., Gong, P., & Si, Y. (2024, Accepted). Spatially heterogeneous shifts in vegetation phenology induced by climate change threaten the integrity of the avian migration network. Global Change Biology.</p> <p>The dataset mainly contains data showing the climate change-induced heterogeneous shifts in vegetation phenology and the migration integrity change from 2000 to 2020 for 16 Asian herbivorous waterfowl species. These data were derived from the following resources available in the public domain.</p> <p>The Global Lakes and Wetlands Database is available from “https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database”. The global land cover datasets are available from European Space Agency (ESA) Climate Change Initiative (CCI) products, “https://maps.elie.ucl.ac.be/CCI/viewer/download.php”. The Global Multi-resolution Terrain Elevation Data are available from “https://www.usgs.gov/centers/eros/science/terrain-monitoring-and-modeling”. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance product is available from “https://modis.gsfc.nasa.gov/data/dataprod/mod09.php”. The bird distribution maps are available from Birdlife International, “https://www.birdlife.org/”. The bird foraging attribute data are available from EltonTraits 1.0, “https://figshare.com”. The bird occurrence data are available from eBird Basic Dataset (EBD), “https://science.ebird.org/en/use-ebird-data/download-ebird-data-products”. The Hackett backbone phylogenetic trees are available from “https://birdtree.org/”.</p> <p>The code contains the R scripts and MATLAB scripts that we used for this study.</p> <p>For details please see the file “Readme.txt”, and the research paper.</p>
Supplementary Material - The conservation status of the Cretan Endemic Arthropods under Natura 2000 network
<p>Arthropod decline has been globally and locally documented, yet they are still not sufficiently protected. Crete (Greece), a Mediterranean biodiversity hotspot, is a continental island renowned for its diverse geology, ecosystems and endemicity of flora and fauna, with continuous research on its Arthropod fauna dating back to the 19th century. Here we investigate the conservation status of the Cretan Arthropods using Preliminary Automated Conservation Assessments (PACA) and the overlap of Cretan Arthropod distributions with the Natura 2000 protected areas. Moreover we investigate their endemicity hotspots and propose candidate Key Biodiversity Areas. In order to perform these analyses, we assembled occurrences of the endemic Arthropods in Crete located in the collections of the Natural History Museum of Crete together with literature data. These assessments resulted in 75% of endemic Arthropods as potentially threatened. The hotspots of endemic taxa and the candidate Key Biodiversity Areas are distributed mostly on the mountainous areas where the Natura 2000 protected areas have great coverage. Yet human activities have significant impact even in those areas, while some taxa are not sufficiently covered by Natura 2000. These findings call for countermeasures and conservation actions.</p> <p><strong></strong><br>The code is available here <a href="https://github.com/savvas-paragkamian/arthropods_assessment_crete">https://github.com/savvas-paragkamian/arthropods_assessment_crete</a> and a zipped version of the repository is included here (arthropods_assessment_crete-1.2.0.tar). </p> <p>Supplementary Material 1 is the Supplementary-material-1.xlsx file which contains the Arthropod occurrences as compiled from the literature and the specimens of the NHMC. In addition all references of the literature are included in a separate sheet.</p> <p>Supplementary Material 2 1 is the Supplementary-material-2.docx which contains two (2) supplementary figures and two (2) supplementary tables.</p> <p> </p>
Plant-flower visitor network from Avon Gorge, UK
<table> <tbody> <tr> <td>Abstract</td> <td>This dataset gathers information on interactions between plants<br>and their flower visitors collected throughout 2004 (11 surveys covering local flowering season) the Avon Gorge (England), an iconic field site well known for its rare plant populations. The study area (1480 m2 ) included a broad<br>range of flowering plants, and overall the dataset shows information for 260 species (81 plant species, 179 insect species and morphospecies).</td> </tr> <tr> <td>Classification System</td> <td>all taxa were identified by specialist taxonomists</td> </tr> <tr> <td>Sampling Description</td> <td>A total of 11 survey visits were carried out from 10 May to 27 September 2004, this covering the main period of insect activity. Flower and insect surveys took place approximately every 14 days under dry conditions. In each flower abundance survey, a stratified random design was used to select 1 m2 quadrats in the study area. The area was divided into nine sub-areas based on habitat type and accessibility. Each sub-area was divided into 1 m2 quadrats and 2·5% (37) of these were randomly selected per sampling occasion. In each quadrat, the number of floral units of each plant species was recorded, defined as the distance that a small bee (c.1 cm length) would fly, rather than walk (Saville 1993). For example, in the Asteraceae, a flower unit is the entire inflorescence while in the Rosaceae, a flower unit is a single flower. Thus, the floral unit is defined from the bee’s perspective rather than by flower anatomy. Rare flowers which were missed using this method were included in the food web data as rare species with an abundance of two flower units (which was the lowest number of units observed in the plot for any species).<br>In the insect surveys, an observation point was chosen for each flowering plant species by randomly selecting one of the quadrats where the species was present. All the flowering units that could be surveyed by a single observer (approximately a semi-circle with 1-m radius) were observed for 20 min. On consecutive sampling occasions, plant species were rotated through three time slots, the morning (09.00–12.00 h), early afternoon (12.00–15.00 h) and late afternoon (15.00–18.00 h), to allow each species to be observed equally over time. At least two floral units were observed per plant species per sample. All flower–visitor interactions were recorded, and all visitors observed were collected for identification. To estimate the overall abundance of each plant species, the average number of flower units per 1 m 2 quadrat was multiplied by the total area of the study site. To estimate the interaction frequency for each visitor–plant species pair, we divided the total number of visits recorded by the number of flower units observed (per 20 min) and then multiplied by the total number of floral units in the study plot. By collecting the insects, we did not allow for repeated visits by the same individual; hence, some visitation frequencies may be underestimated. However, collecting specimens is essential for identification of most visitor species. Hymenoptera, Diptera, and Coleoptera were identified by taxonomists either to species or to morphospecies. Lepidoptera were identified to species by the authors and Heteroptera and parasitoids were morphotyped by the authors.</td> </tr> </tbody> </table>
Shapefiles showing the locations of long-term climate change refugia and hotspots identified in the FairSeas report "A Climate Resilient Path for Ireland's Marine Protected Areas Network"
<p>Shapefiles created for the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network", an addendum chapter to "Revitalising Our Seas report: Identifying<br>Areas of Interest for Marine Protected Area Designation in Irish Waters"</p> <p>These shapefiles summarise long-term patterns that emerge from the spatial-meta analysis of physical-biogeochemical and species distribution modelling data, providing an overview of the distribution of climate change refugia and climate change hotspots across Ireland's National Marine Planning Framework between 2026 - 2069, and across the two emissions scenarios considered in the report (RCP4.5 and RCP8.5). </p> <p>Filenames refer to the specific analysis each set of shapefiles belong to: Benthic habitats, benthic megafauna, pelagic habitats, pelagic megafauna and forage fish. Details of the modelling datasets used in each of these analyses, the meta-analysis method and shapefile creation can be found in Annex A1 in the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network".</p>
Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network [Dataset]
<p>data07B.csv: dataset for the study of the SARS-CoV-2 transmission.</p> <p>CPTNetica.txt: conditional probabilities tables of each variable given through Netica once the BN obtained in R code is loaded.</p> <p>code01.R: code to learn structure and parameters of the SARS-CoV-2 BN model.</p>
Dataset and neural network weights to the paper: "Generative diffusion for regional surrogate models from sea-ice simulations"
<p>All the needed code and data to reproduce the results from the paper: "Generative diffusion for regional surrogate models from sea-ice simulations".<br>While most of the code is a frozen clone of the original <a href="https://github.com/cerea-daml/diffusion-nextsim-regional">Repository</a>, this capsule also includes the dataset and neural network weights to train and apply the surrogate models.</p> <p>The <strong>dataset</strong> for training and evaluation can be found at <em>data/nextsim</em>, which includes three different Zarr folders for training/validation/testing. The dataset is based on neXtSIM simulation data and ERA5 forcing data and extracted from the <a href="https://ige-meom-opendap.univ-grenoble-alpes.fr/thredds/catalog/meomopendap/extract/catalog.html">SASIP shared data OpenDAP server</a>:</p> <ul> <li>The neXtSIM simulations were performed by Gauillaume Boutin and published in the paper "<a href="https://doi.org/10.5194/tc-17-617-2023">Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework</a>" (Boutin et al., 2023) and available as Zenodo <a href="../records/7277523">dataset</a> (Boutin et al., 2022).</li> <li>The forcing data is based on the ERA5 reanalysis dataset published in the paper: "<a href="https://doi.org/10.1002/qj.3803">The ERA5 global reanalysis</a>" (Hersbach et al., 2020) and available as dataset from the Copernicus Climate Change Service (C3S, Copernicus Climate Change Service, 2023). The here used forcing data is based on the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels">hourly reanalysis data on single levels</a> and interpolated with nearest neighbors to the curvilinear grid as used in the output from the neXtSIM simulations. <strong>Disclaimer:</strong> The results contain modified Copernicus Climate Change Service information, 2023. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</li> </ul> <p>The <strong>neural network weights</strong> are included under <em>data/models </em>and split into weights for the deterministic models and the diffusion models.<br>These neural network weights have been used to generate the results presented in the paper.</p> <p>In this capsule, the <em>notebooks</em> folder includes also the figures used within the paper and additional trajectory data used in the qualitative analysis of the paper.</p> <p>Generally, we recommend to just download the <em>data.tar.gz </em>file and use otherwise the original <a href="https://github.com/cerea-daml/diffusion-nextsim-regional">Repository</a>, since the here included code can be outdated. We further refer to the repository for additional information.</p> <p> </p> <p>Contained in this capsule:</p> <ul> <li>configs.tar.gz: The configuration files for the experiments.</li> <li>data.tar.gz: The dataset and neural network weights.</li> <li>diffusion_nextsim.tar.gz: The main code for the neural network etc.</li> <li>environment.yaml: The anaconda environment file, can be used to install the needed packages.</li> <li>notebooks.tar.gz: The notebooks that were used to create the figures in the paper. The figures from the paper and the data from the qualitative analysis are included as well.</li> <li>readme.md: The readme file from the repository.</li> <li>scripts.tar.gz: The scripts used for the experiments.</li> <li>setup.py: the file to install the <em>diffusion_nextsim</em> package in a python environment.</li> </ul> <p>References:</p> <p>Guillaume Boutin, Heather Regan, Einar Ólason, Laurent Brodeau, Claude Talandier, Camille Lique, & Pierre Rampal. (2022). Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7277523</p> <p>Boutin, G., Ólason, E., Rampal, P., Regan, H., Lique, C., Talandier, C., Brodeau, L., and Ricker, R.: Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework, The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, 2023.</p> <p>Copernicus Climate Change Service (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: <a href="https://doi.org/10.24381/cds.adbb2d47">10.24381/cds.adbb2d47</a>.</p> <p>Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis. <em>Q J R Meteorol Soc</em>. 2020; 146: 1999–2049. <a href="https://doi.org/10.1002/qj.3803">https://doi.org/10.1002/qj.3803</a></p> <p> </p>
Data to "The olfactory network of larval Xenopus laevis regenerates accurately after olfactory nerve transection"
<p>This record contains analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Hawkins S. J., Gärtner Y., Offner T., Weiss L., Maiello G., Hassenklöver T., and Manzini I. (under review) The olfactory network of larval <em>Xenopus laevis</em> regenerates accurately after olfactory nerve transection. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.