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Pre-processed functional data for the github repo: gecthomas/Spectral_DCM_in_PD_VH/tree/spm_course

This repository was created for the purposes of a practical demonstration for the May 2024 SPM course at UCL. This upload contains subject-level anonymised functional data that have undergone the following pre-processing: <ul> <li>first 5 volumes discarded</li> <li>spatial realingment</li> <li>unwarping</li> <li>normalisation to MNI space</li> <li>smoothing</li> <li>denoising with ICA-AROMA</li> </ul> There are also pre-processed SPM GLM files. These data are to be used in conjuction with the code in the repositry found <a href="https://github.com/gecthomas/Spectral_DCM_in_PD_VH/tree/spm_course" target="_blank" rel="noopener">here</a>. They only contain a subset of the participants included in the full analysis and are for demonstrattion purposes only.

Synergistic olfactory processing for social plasticity in desert locusts (widefield imaging sub-repo-04)

Main Zenodo repository: https://doi.org/10.5281/zenodo.10931394 Data deposited here: Physiology\Cal520_Widefield\COL_HEX3_MOL_N2_OC3L2_ZHAE2\solitarious

Synergistic olfactory processing for social plasticity in desert locusts (widefield imaging sub-repo-03)

Main Zenodo repository: https://doi.org/10.5281/zenodo.10931394 Data deposited here: Physiology\Cal520_Widefield\COL_HEX3_MOL_N2_OC3L2_ZHAE2\gregarious

Synergistic olfactory processing for social plasticity in desert locusts (widefield imaging sub-repo-02)

Main Zenodo repository: https://doi.org/10.5281/zenodo.10931394 Data deposited here: Physiology\Cal520_Widefield\BERRYLEAF_COL_MOL_N2_ZHAE2\solitarious

Synergistic olfactory processing for social plasticity in desert locusts (widefield imaging sub-repo-01)

Main Zenodo repository: https://doi.org/10.5281/zenodo.10931394 Data deposited here: Physiology\Cal520_Widefield\BERRYLEAF_COL_MOL_N2_ZHAE2\gregarious

Neutral processes and taxonomic scale drive beta species-genetic diversity correlations in a submesophotic tropical reef fish

This dataset is associated to the following publication: Vilcot M, Faure N, Andrews KR, Bowen BW, Leprieur F, Manel S. (2024) Neutral processes and taxonomic scale drive beta species-genetic diversity correlations in a submesophotic tropical reef fish. Molecular Ecology 33, e17423. (doi:<a href="https://doi.org/10.1111/mec.17423">10.1111/mec.17423</a>)   <h2>Data:  </h2> <ul> <li> "Report_DEtel22-6705_SNP_2_ordered_Bowen.csv": SNP data from Dart Sequencing on Etelis coruscans, from Andrews et al. (2020) samples </li> <li>"PA_Mat_GaspObis.RDS": fish species presence data, gathered from an updated version of Albouy et al. (2019)</li> <li> "metadata_samples_full.csv": all Etelis coruscans sample information, from Andrews et al. (2020) and the SEAMOUNTS project </li> <li> "metadata_samples.csv": Etelis coruscans sample information, only for samples that have passed the genetic filtering and were used for subsequent analyses  </li> <li> "metadata_stations.csv": sampling station information </li> <li> "Taxonomy_Fishbase.csv": species taxonomic information, downloaded with rfishbase::load_taxa()  </li> <li> "traits_Luiz_et_al_2013.csv": species trait information from Luiz et al. (2013)   </li> </ul> <h2>Related dataset:  </h2> Etelis coruscans SNP data on samples from the SEAMOUNTS project are available at <a href="https://doi.org/10.5281/zenodo.11201065">https://doi.org/10.5281/zenodo.11201065</a> <h2>Scripts:  </h2> Scripts used to reproduce the analyses and figures of the final article are available at <a href="https://github.com/mvilcot/etelis_SGDCs">https://github.com/mvilcot/etelis_SGDCs</a>    <h2>References:  </h2> Albouy, C., Archambault, P., Appeltans, W., Araújo, M. B., Beauchesne, D., Cazelles, K., Cirtwill, A. R., Fortin, M.-J., Galiana, N., Leroux, S. J., Pellissier, L., Poisot, T., Stouffer, D. B., Wood, S. A., & Gravel, D. (2019). The marine fish food web is globally connected. Nature Ecology & Evolution, 3(8), Article 8. <a href="https://doi.org/10.1038/s41559-019-0950-y" target="_blank" rel="noopener">https://doi.org/10.1038/s41559-019-0950-y</a>    Andrews, K. R., Copus, J. M., Wilcox, C., Williams, A. J., Newman, S. J., Wakefield, C. B., & Bowen, B. W. (2020). Range-Wide population structure of 3 deepwater Eteline snappers across the Indo-Pacific Basin. Journal of Heredity, 111(5), 471‑485. <a href="https://doi.org/10.1093/jhered/esaa029" target="_blank" rel="noopener">https://doi.org/10.1093/jhered/esaa029</a>   Luiz, O. J., Allen, A. P., Robertson, D. R., Floeter, S. R., Kulbicki, M., Vigliola, L., Becheler, R., & Madin, J. S. (2013). Adult and larval traits as determinants of geographic range size among tropical reef fishes. Proceedings of the National Academy of Sciences, 110(41), 16498‑16502. <a href="https://doi.org/10.1073/pnas.1304074110" target="_blank" rel="noopener">https://doi.org/10.1073/pnas.1304074110</a>   Boettiger, C., Lang, D. T., & Wainwright, P. C. (2012). rfishbase: Exploring, manipulating and visualizing FishBase data from R. Journal of Fish Biology, 81(6), 2030‑2039. <a href="https://doi.org/10.1111/j.1095-8649.2012.03464.x" target="_blank" rel="noopener">https://doi.org/10.1111/j.1095-8649.2012.03464.x</a>

Kinematic Process and Characteristic of Root-Induced Water Flow Using a Visualizing Point Source Infiltration Experiment

The video is the temporal results of the 2-D point source infiltration experiment in this paper. ‘G-’ represents the granular experimental group, and ’RG-’ represents the root-granular experimental group. The injection rate of G-1 and RG-1 is 0.25 mm/min, corresponding to the boundary flow rate of 0.03 ml/min.  In this experiment, the glass beads have a particle diameter ranging from 0.6-0.8 mm. The selected root has diameters ranging from 1.3-1.5 mm.

Trends in Natural Language Processing

This dataset contains the supporting parsed corpus as described in the publication: "Analyzing a Decade of Evolution: Trends in Natural Language Processing"  This dataset contains a single zip containing data between the years 2010 and 2022, for the conferences: <ul> <li>Meeting of the Association for Computational Linguistics (ACL)</li> <li>Conference on Empirical Methods in Natural Language Processing (EMNLP)</li> <li>American Chapter of the Association for Computational Linguistics (NAACL)</li> <li>Conference on Computational Linguistics (COLING)</li> <li>International Conference on Language Resources and Evaluation (LREC)</li> <li>Conference on Computational Natural Language Learning (CoNLL)</li> <li>European Chapter of the Association for Computational Linguistics (EACL)</li> <li>International Joint Conference on Natural Language Processing (IJCNLP)</li> </ul> The data inlcuded is a PDF and a JSON file for each confernce. The JSON file is constucted from using the python packages SciPDF and PyPDF2. PyPDF2 extract all text from a page and is presented in the 'full_text' field, where as the SciPDF parser utilize machine lerarning to create a smart representation of the data, which is presented in the remaining fields. For further details on how this dataset was generated, please see our <a href="https://github.com/ieeta-pt/nlp-trends" target="_blank" rel="noopener">GitHub</a> repository, and our paper. Citation: AWAITING PUBLICATION

Data for "Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression"

The dataset for the manuscript titled "Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression". <blockquote> Oh, G. and Austin, P. H.: Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-352, 2024. </blockquote>   The dataset contains input parameters used for the SAM LES model run (Khairoutdinov and Randall, 2003) based on the CGILS S2 parameters (CGILS.zip) and the main raw output used for the manuscript (clusters.zip). The latter contains a list of Parquet files for the 12-hour model run, sampled every minute. The raw output consists of the coordinates of all cloudy cells in the model domain, which is used to construct the cloud size distribution as described in the manuscript.

Supplementary Data from manuscript entitled: Cloud processing dominates the vertical aerosol profiles in marine air masses over the Great Barrier Reef

The data used in the manuscript "Cloud processing dominates the vertical aerosol profiles in marine air masses over the Great Barrier Reef" is available. It contains information from different instruments on board the research aircraft. The manuscript describes the instrumentation details. There is a read_me file in each folder with further information.

processed 1994 BP benchmark model

This zip file contains our processed 1994 BP model, some of the synthetic seismic data, and the Matlab drawing code. Each part has its own folder and README file, check these files for more information

opencc-by-4.0Mar 2024View details →

Autistic-like Behaviour in Fibromyalgia is Associated with Hypersensitisation in Sensory Processing Sensitivity dataset

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A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing

<h1>Overview</h1> This dataset is derived from the <a href="https://quijote-simulations.readthedocs.io/en/latest/bsq.html">Big Sobol Sequence (BSQ) of the Quijote simulations</a>, a collection of N-body simulations designed for machine learning applications. Each simulation consists of a point cloud (points in space, with 3D coordinates attached to them) generated under a varying set of cosmological parameters. Each point represents a simulated galaxy and is accompanied by associated properties such as velocity and mass. The cardinality of each point cloud is 5000 points. The dataset is split into 11,200 simulations in the training set, 608 in the validation set, and 576 in the test set.  <h1>File structure</h1> The dataset is provided in <a href="https://www.tensorflow.org/tutorials/load_data/tfrecord">TFRecord format</a>. The training simulations are split across 50 TFRecord files following the naming convention <code>halos_train_.tfrecord</code>. The validation and test sets are provided in <code>halos_val_1.tfrecord</code> and <code>halos_test_1.tfrecord</code>, respectively. Each dataset can be loaded using TensorFlow as shown in the code example below: <div> <blockquote> <div>import tensorflow as tf files = tf.io.gfile.glob(f"halos*train*.tfrecord") # replace 'train' with 'val' or 'test'</div> <div>dataset = tf.data.TFRecordDataset(files)</div> </blockquote> </div> <h2>TFRecord structure</h2> Each record (corresponding to a point cloud) in a TFRecord file contains the following feature fields: <ul> <li><code>"x"</code>: (tensor of shape [5000], dtype=float) - Position along x axis</li> <li><code>"y"</code>: (tensor of shape [5000], dtype=float) - Position along y axis</li> <li><code>"z"</code>: (tensor of shape [5000], dtype=float) - Position along z axis</li> <li><code>"v_x"</code>: (tensor of shape [5000], dtype=float) - Velocity along x axis</li> <li><code>"v_y"</code>: (tensor of shape [5000], dtype=float) - Velocity along y axis</li> <li><code>"v_z"</code>: (tensor of shape [5000], dtype=float) - Velocity along z axis</li> <li><code>"J_x"</code>: (tensor of shape [5000], dtype=float) - Angular momentum along x axis</li> <li><code>"J_y"</code>: (tensor of shape [5000], dtype=float) - Angular momentum along y axis</li> <li><code>"J_z"</code>: (tensor of shape [5000], dtype=float) - Angular momentum along z axis</li> <li><code>"M200c"</code>: (tensor of shape [5000], dtype=float) - Virial mass</li> <li><code>"Omega_m"</code>: (float) - Matter density</li> <li><code>"Omega_b"</code>: (float) - Baryon density</li> <li><code>"h"</code>: (float) - Hubble parameter</li> <li><code>"n_s"</code>: (float) - Density perturbation spectral index</li> <li><code>"sigma_8"</code>: (float) - RMS matter fluctuation amplitude on a scale of 8 Mpc/h</li> <li><code>"tpcf"</code>: (tensor of shape [24], dtype=float) - Two-point correlation function</li> </ul>

Raw and post-processed data from the study "Mapping the spectrotemporal regions influencing perception of French stop consonants in noise"

The current dataset contains all data needed to replicate the study from Carranante et al. "Mapping the spectrotemporal regions influencing perception of French stop consonants in noise" Use these data: <ol> <li>Download all these data, place them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="http://github.com/aosses-tue/fastACI">GitHub</a>) you can recreate the figures of our paper.</li> <li>After initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB) and then type either of the following commands, to recreate the figure you want. To recreate the figures in the main text: <pre><code>publ_carranante2024_figs('fig1','zenodo'); </code></pre> To generate the figures of the supplementary materials: <pre><code>publ_carranante2024_figs('fig1_suppl','zenodo');</code></pre> </li> </ol> References: <ul> <li>Paper not published yet</li> <li>fastACI toolbox: Alejandro Osses, Léo Varnet. fastACI toolbox: the MATLAB toolbox for investigating auditory perception using reverse correlation (v1.2). Zenodo. doi:<a href="https://doi.org/10.5281/zenodo.7314014">10.5281/zenodo.7314014</a>. Supplement to: <a href="http://github.com/aosses-tue/fastACI/tree/v1.2">https://github.com/aosses-tue/fastACI/tree/v1.2</a></li> </ul>

Dataset for time evolution of classical contact process with 100 sites

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Supplementary Material for "Decomposing Process Performance based on Actor Behavior"

==================================================================================== Supplementary Material for "Decomposing Process Performance based on Actor Behavior" ==================================================================================== The results of the approach applied to the BPIC17 event log and an R script to  produce the figures of the paper. Files: - actor_behavior_all_instances_per_edge/*            The raw data files (.pkl) - output_BPIC17_performance_decomposed_forR.csv        Precomputed means - performance_decomposed_paper.R                    Script to produce figures The R script requires a recent R (>4.1) and the following packages: readr, dplyr, reticulate, ggplot2, purrr, xtable It also requires a working Python distribution with reticulate: https://rstudio.github.io/reticulate/index.html

Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression

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Strain partitioning, transfer and implications for ongoing processes of the intra-continental graben formation in the NW margin of the Ordos block, China: insights from densified GNSS measurements

GNSS data and processing 1. Intensive observations In this study, in addition to GNSS data surveyed around the Ordos block provided by Hao et al. (2021), we collected GNSS data surveyed west of 103°E from Phases I and II of the Crustal Movement Observation Network of China (CMONOC), which has conducted measurements every one or two years from 1999 to 2019. In particular, we also collected data from 70 GNSS sites deployed by <a name="OLE_LINK49"></a><a name="OLE_LINK50"></a><a name="OLE_LINK82"></a>the National Geodetic Control Network of China (NGCNC). These sites were distributed in the northwestern boundary of the Ordos block and the interior of the Alashan and Ejina blocks and first measured 72 hours of data in 2014 or 2015. From September to October 2021, we conducted the second set of measurements for these GNSS sites of 96 continuous hours.   2. Data processing The GAMIT package (Herring et al., 2015a) was employed to process the double-differenced carrier phase observations, and daily loosely constrained solutions were obtained. The primary geophysical models and parameters used in the processing, such as the FES2004 tidal loading model, GPT2 global pressure and temperature model (Lagler et al., 2013), and VMF1 mapping function (Boehm et al., 2006), are the same as those in Hao et al. (2021). Instead of incorporating global daily solutions provided by the IGS analysis centers, we also processed data from ~70 evenly distributed global International Terrestrial Reference Frame (ITRF) core tracking GNSS sites using the GAMIT package with the same models. We estimated daily coordinates and uncertainties by constraining the daily regional solutions with global solutions, and the daily free network solutions were transformed into the ITRF2014 (Altamimi et al., 2017) reference frame by utilizing the GLOBK package (Herring et al., 2015b). For campaign GNSS sites, the weighted least-squares adjustment was used to solve the linear velocity with respect to the ITRF2014. To analyze the differential crustal motion between blocks, we selected the stable Ordos block as the regional reference frame and transformed the GNSS velocity field referenced to the ITRF2014 into the Ordos-fixed frame through Euler rotation. The absolute Euler pole of the Ordos block is located at 75.083°N ± 0.850° and 139.692°W ± 3.701°, with an angular rotation rate of 0.352 ± 0.006°/Ma (Hao et al., 2021).   References Altamimi, Z., Métivier, L, Rebischung, P., Rouby, H., Collilieux, X., 2017. ITRF2014 plate motion model.Geophys. J. Int. 209:1906–1912 Boehm, J., Werl, B., and Schuh, H. (2006). Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium-Range Weather Forecasts operational analysis data. Journal of Geophysical Research, 111, B0240<a href="https://doi.org/10.1029/2005JB003629">. doi:10.1029/2005JB003629</a>. Hao, M., Wang, Q., Zhang, P., Li, Z., Li, Y., Zhuang, W., 2021.“Frame wobbling” causing crustal deformation around the Ordos block. Geophysical Research Letters 48, e2020GL091008. https://doi.org/10.1029/2020GL091008. Herring, T.A., King, R.W., McClusky, S.C., 2015b.GAMIT reference manual, global Kalman filter VLBI and GPS analysis program, Release 10.6.Massachusetts Institute of Technology, Cambridge. Herring, T.A., King, R.W.,McClusky, S.C., 2015a.GAMIT reference manual, GPS analysis at MIT, Release 10.6.Massachusetts Institute of Technology, Cambridge. Lagler, K., Schindelegger, M., Böhm, J., Krásná, H., and Nilsson, T. (2013). GPT2: empirical slant delay model for radio space geodetic techniques. GeophysicalResearchLetters, 40, 1069-1073. doi: 10.1002/grl.50288.

Appendix to "Process Mining Pipelines with Controlled Sharing of Data and Algorithms"

Abstract: Process mining leverages execution traces within an organisation's IT systems to gain insights into its processes. Despite being a mature discipline in academia and industry, setting up process mining pipelines is still a complex task and involves programming, manual steps, and considerations of privacy and intellectual property. This paper introduces a platform based on a distributed architecture that helps define, deploy, and execute process mining pipelines across organisations. The requirements for this distributed architecture and platform are derived from a set of process mining scenarios, whose relevance is validated through a survey. Furthermore, this paper introduces a prototype for an initial version of the platform, demonstrating feasibility and supporting the specified requirements. This development is a major step in advancing process mining, offering simpler and more efficient ways of implementing and managing complex process mining pipelines on a larger scale. Description: This dataset presents the support for non-functional requirements identified in the paper "Process Mining Pipelines with Controlled Sharing of Data and Algorithms" by existing process mining platforms. Legend: Green cells indicate complete fulfilment. Yellow indicates partial fulfilment. Blue cells indicate uncertain fulfilment. Red indicates no fulfilmnet.

Enhancing Name Entity Recognition Through Hybrid Deep Learning in Natural Language Processing

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