Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

Identifying change points and/or anomalies in dynamic network structures has become increasingly popular across various domains, from neuroscience to telecommunication to finance. One particular objective of anomaly detection from a neuroscience perspective is the reconstruction of the dynamic manner of brain region interactions. However, most statistical methods for detecting anomalies have the following unrealistic limitation for brain studies and beyond: that is, network snapshots at different time points are assumed to be independent. To circumvent this limitation, we propose a distribution-free framework for anomaly detection in dynamic networks. First, we present each network snapshot of the data as a linear object and find its respective univariate characterization via local and global network topological summaries. Second, we adopt a change point detection method for (weakly) dependent time series based on efficient scores, and enhance the finite sample properties of change point method by approximating the asymptotic distribution of the test statistic using the sieve bootstrap. We apply our method to simulated and to real data, particularly, two functional magnetic resonance imaging (fMRI) datasets and the Enron communication graph. We find that our new method delivers impressively accurate and realistic results in terms of identifying locations of true change points compared to the results reported by competing approaches. The new method promises to offer a deeper insight into the large-scale characterizations and functional dynamics of the brain and, more generally, into the intrinsic structure of complex dynamic networks. Supplemental materials for this article are available online.

This dataset contains the derived connectomes, discriminability scores, and classification performance for structural connectomes estimated from a subset of the Nathan Kline Institute Rockland Sample dataset, and is associated with an upcoming manuscript entitled: Numerical Instabilities in Analytical Pipelines Compromise the Reliability of Network Neuroscience. The associated code for this project is publicly available at: https://github.com/gkpapers/2020ImpactOfInstability. For any questions, please contact Gregory Kiar (gkiar07@gmail.com) or Tristan Glatard (tristan.glatard@concordia.ca).

Below is a table of contents describing the contents of this dataset, which is followed by an excerpt from the manuscript pertaining to the contained data.

• impactofinstability_connect_dset25x2x2x20_inputs.h5 : Connectomes derived from 25 subjects, 2 sessions, 2 subsamples, and 20 MCA simulations with input perturbations.
• impactofinstability_connect_dset25x2x2x20_pipeline.h5 : Connectomes derived from 25 subjects, 2 sessions, 2 subsamples, and 20 MCA simulations with pipeline perturbations.
• impactofinstability_discrim_dset25x2x2x20_both.csv : Discriminability scores for each grouping of the 25x2x2x20 dataset.
• impactofinstability_connect+feature_dset100x1x1x20_both.h5 : Connectomes and features derived from 100 subjects, 1 sessions, 1 subsamples, and 20 MCA simulations with both perturbation types.
• impactofinstability_classif_dset100x1x1x20_both.h5 : Classification performance results for the BMI classification task on the 100x1x1x20 dataset.

Dataset
The Nathan Kline Institute Rockland Sample (NKI-RS) dataset [1] contains high-fidelity imaging and phenotypic data from over 1,000 individuals spread across the lifespan. A subset of this dataset was chosen for each experiment to both match sample sizes presented in the original analyses and to minimize the computational burden of performing MCA. The selected subset comprises 100 individuals ranging in age from 6 – 79 with a mean of 36.8 (original: 6 – 81, mean 37.8), 60% female (original: 60%), with 52% having a BMI over 25 (original: 54%).

Each selected individual had at least a single session of both structural T1-weighted (MPRAGE) and diffusion-weighted (DWI) MR imaging data. DWI data was acquired with 137 diffusion directions; more information regarding the acquisition of this dataset can be found in the NKI-RS data release [1].

In addition to the 100 sessions mentioned above, 25 individuals had a second session to be used in a test-retest analysis. Two additional copies of the data for these individuals were generated, including only the odd or even diffusion directions (64 + 9 B0 volumes = 73 in either case). This allows an extra level of stability evaluation to be performed between the levels of MCA and session-level variation.

In total, the dataset is composed of 100 diffusion-downsampled sessions of data originating from 50 acquisitions and 25 individuals for in depth stability analysis, and an additional 100 sessions of full-resolution data from 100 individuals for subsequent analyses.

Processing
The dataset was preprocessed using a standard FSL [2] workflow consisting of eddy-current correction and alignment. The MNI152 atlas was aligned to each session of data, and the resulting transformation was applied to the DKT parcellation [3]. Downsampling the diffusion data took place after preprocessing was performed on full-resolution sessions, ensuring that an additional confound was not introduced in this process when comparing between downsampled sessions. The preprocessing described here was performed once without MCA, and thus is not being evaluated.

Structural connectomes were generated from preprocessed data using two canonical pipelines from Dipy [4]: deterministic and probabilistic. In the deterministic pipeline, a constant solid angle model was used to estimate tensors at each voxel and streamlines were then generated using the EuDX algorithm [5]. In the probabilistic pipeline, a constrained spherical deconvolution model was fit at each voxel and streamlines were generated by iteratively sampling the resulting fiber orientation distributions. In both cases tracking occurred with 8 seeds per 3D voxel and edges were added to the graph based on the location of terminal nodes with weight determined by fiber count.

Perturbations
All connectomes were generated with one reference execution where no perturbation was introduced in the processing. For all other executions, all floating point operations were instrumented with Monte Carlo Arithmetic (MCA) [6] through Verificarlo [7]. MCA simulates the distribution of errors implicit to all instrumented floating point operations (flop).

MCA can be introduced in two places for each flop: before or after evaluation. Performing MCA on the inputs of an operation limits its precision, while performing MCA on the output of an operation highlights round-off errors that may be introduced. The former is referred to as Precision Bounding (PB) and the latter is called Random Rounding (RR).

Using MCA, the execution of a pipeline may be performed many times to produce a distribution of results. Studying the distribution of these results can then lead to insights on the stability of the instrumented tools or functions. To this end, a complete software stack was instrumented with MCA and is made available on GitHub through https://github.com/gkiar/fuzzy.

Both the RR and PB variants of MCA were used independently for all experiments. As was presented in [8], both the degree of instrumentation (i.e. number of affected libraries) and the perturbation mode have an effect on the distribution of observed results. For this work, the RR-MCA was applied across the bulk of the relevant libraries and is referred to as Pipeline Perturbation. In this case the bulk of numerical operations were affected by MCA.

Conversely, the case in which PB-MCA was applied across the operations in a small subset of libraries is here referred to as Input Perturbation. In this case, the inputs to operations within the instrumented libraries (namely, Python and Cython) were perturbed, resulting in less frequent, data-centric perturbations. Alongside the stated theoretical differences, Input Perturbation is considerably less computationally expensive than Pipeline Perturbation.

All perturbations were targeted the least-significant-bit for all data (t=24and t=53in float32 and float64, respectively [7]). Simulations were performed between 10 and 20 times for each pipeline execution, depending on the experiment. A detailed motivation for the number of simulations can be found in [9].

Evaluation
The magnitude and importance of instabilities in pipelines can be considered at a number of analytical levels, namely: the induced variability of derivatives directly, the resulting downstream impact on summary statistics or features, or the ultimate change in analyses or findings. We explore the nature and severity of instabilities through each of these lenses. Unless otherwise stated, all p-values were computed using Wilcoxon signed-rank tests.

Direct Evaluation of the Graphs
The differences between simulated graphs was measured directly through both a direct variance quantification and a comparison to other sources of variance such as individual- and session-level differences.

Quantification of Variability – Graphs, in the form of adjacency matrices, were compared to one another using three metrics: normalized percent deviation, Pearson correlation, and edgewise significant digits. The normalized percent deviation measure, defined in [8], scales the norm of the difference between a simulated graph and the reference execution (that without intentional perturbation) with respect to the norm of the reference graph. The purpose of this comparison is to provide insight on the scale of differences in observed graphs relative to the original signal intensity. A Pearson correlation coefficient was computed in complement to normalized percent deviation to identify the consistency of structure and not just intensity between observed graphs. Finally, the estimated number of significant digits for each edge in the graph was computed. The upper bound on significant digits is 15.7 for 64-bit floating point data.

The percent deviation, correlation, and number of significant digits were each calculated within a single session of data, thereby removing any subject- and session-effects and providing a direct measure of the tool-introduced variability across perturbations. A distribution was formed by aggregating these individual results.

Class-based Variability Evaluation – To gain a concrete understanding of the significance of observed variations we explore the separability of our results with respect to understood sources of variability, such as subject-, session-, and pipeline-level effects. This can be probed through Discriminability [10], a technique similar to ICC which relies on the mean of a ranked distribution of distances between observations belonging to a defined set of classes.

Discriminability can then be interpreted as the probability that an observation belonging to a given class will be more similar to other observations within that class than observations of a different class. It is a measure of reproducibility, and is discussed in detail in [10].

This definition allows for the exploration of deviations across arbitrarily defined classes which in practice can be any of those listed above. We combine this statistic with permutation testing to test hypotheses on whether

IncRML resources This Zenodo dataset contains all the resources of the paper 'IncRML: Incremental Knowledge Graph Construction from Heterogeneous Data Sources' submitted to the Semantic Web Journal's Special Issue on Knowledge Graph Construction. This resource aims to make the paper experiments fully reproducible through our experiment tool written in Python which was already used before in the Knowledge Graph Construction Challenge by the ESWC 2023 Workshop on Knowledge Graph Construction. The exact Java JAR file of the RMLMapper (rmlmapper.jar) is also provided in this dataset which was used to execute the experiments. This JAR file was executed with Java OpenJDK 11.0.20.1 on Ubuntu 22.04.1 LTS (Linux 5.15.0-53-generic). Each experiment was executed 5 times and the median values are reported together with the standard deviation of the measurements. Datasets We provide both dataset dumps of the GTFS-Madrid-Benchmark and of real-life use cases from Open Data in Belgium.GTFS-Madrid-Benchmark dumps are used to analyze the impact on execution time and resources, while the real-life use cases aim to verify the approach on different types of datasets since the GTFS-Madrid-Benchmark is a single type of dataset which does not advertise changes at all. Benchmarks GTFS-Madrid-Benchmark: change types with fixed data size and amount of changes: additions-only, modifications-only, deletions-only (11 versions) GTFS-Madrid-Benchmark: amount of changes with fixed data size: 0%, 25%, 50%, 75%, and 100% changes (11 versions) GTFS-Madrid-Benchmark: data size with fixed amount of changes: scales 1, 10, 100 (11 versions) Real-life use cases Traffic control center Vlaams Verkeerscentrum (Belgium): traffic board messages data (1 day, 28760 versions) Meteorological institute KMI (Belgium): weather sensor data (1 day, 144 versions) Public transport agency NMBS (Belgium): train schedule data (1 week, 7 versions) Public transport agency De Lijn (Belgium): busses schedule data (1 week, 7 versions) Bike-sharing company BlueBike (Belgium): bike-sharing availability data (1 day, 1440 versions) Bike-sharing company JCDecaux (EU): bike-sharing availability data (1 day, 1440 versions) OpenStreetMap (World): geographical map data (1 day, 1440 versions) Remarks The first version of each dataset is always used as a baseline. All next versions are applied as an update on the existing version. The reported results are only focusing on the updates since these are the actual incremental generation. GTFS-Change-50_percent-{ALL, CHANGE}.tar.xz datasets are not uploaded as GTFS-Madrid-Benchmark scale 100 because both share the same parameters (50% changes, scale 100). Please use GTFS-Scale-100-{ALL, CHANGE}.tar.xz for GTFS-Change-50_percent-{ALL, CHANGE}.tar.xz All datasets are compressed with XZ and provided as a TAR archive, be aware that you need sufficient space to decompress these archives! 2 TB of free space is advised to decompress all benchmarks and use cases. The expected output is provided as a ZIP file in each TAR archive, decompressing these requires even more space (4 TB). Reproducing By using our experiment tool, you can easily reproduce the experiments as followed: Download one of the TAR.XZ archives and unpack them. Clone the GitHub repository of our experiment tool and install the Python dependencies with 'pip install -r requirements.txt'. Download the rmlmapper.jar JAR file from this Zenodo dataset and place it inside the experiment tool root folder. Execute the tool by running: './exectool --root=/path/to/the/root/of/the/tarxz/archive --runs=5 run'. The argument '--runs=5' is used to perform the experiment 5 times. Once executed, you can generate the statistics by running: './exectool --root=/path/to/the/root/of/the/tarxz/archive stats'. Testcases Testcases to verify the integration of RML and LDES with IncRML, see https://doi.org/10.5281/zenodo.10171394

Business process event data modeled as labeled property graphs

Data Format
-----------

The dataset comprises one labeled property graph in two different file formats.

#1) Neo4j .dump format

A neo4j (https://neo4j.com) database dump that contains the entire graph and can be imported into a fresh neo4j database instance using the following command, see also the neo4j documentation: https://neo4j.com/docs/

/bin/neo4j-admin.(bat|sh) load --database=graph.db --from=

The .dump was created with Neo4j v3.5.

#2) .graphml format

A .zip file containing a .graphml file of the entire graph


Data Schema
-----------

The graph is a labeled property graph over business process event data. Each graph uses the following concepts

:Event nodes - each event node describes a discrete event, i.e., an atomic observation described by attribute "Activity" that occurred at the given "timestamp"

:Entity nodes - each entity node describes an entity (e.g., an object or a user), it has an EntityType and an identifier (attribute "ID")

:Log nodes - describes a collection of events that were recorded together, most graphs only contain one log node

:Class nodes - each class node describes a type of observation that has been recorded, e.g., the different types of activities that can be observed, :Class nodes group events into sets of identical observations

:CORR relationships - from :Event to :Entity nodes, describes whether an event is correlated to a specific entity; an event can be correlated to multiple entities

:DF relationships - "directly-followed by" between two :Event nodes describes which event is directly-followed by which other event; both events in a :DF relationship must be correlated to the same entity node. All :DF relationships form a directed acyclic graph.

:HAS relationship - from a :Log to an :Event node, describes which events had been recorded in which event log

:OBSERVES relationship - from an :Event to a :Class node, describes to which event class an event belongs, i.e., which activity was observed in the graph

:REL relationship - placeholder for any structural relationship between two :Entity nodes

The concepts a further defined in Stefan Esser, Dirk Fahland: Multi-Dimensional Event Data in Graph Databases. CoRR abs/2005.14552 (2020) https://arxiv.org/abs/2005.14552


Data Contents
-------------

neo4j-bpic14-2021-02-17 (.dump|.graphml.zip)

An integrated graph describing the raw event data of the entire BPI Challenge 2014 dataset.
van Dongen, B.F. (Boudewijn) (2014): BPI Challenge 2014. 4TU.ResearchData. Collection. https://doi.org/10.4121/uuid:c3e5d162-0cfd-4bb0-bd82-af5268819c35

BPI Challenge 2014: Similar to other ICT companies, Rabobank Group ICT has to implement an increasing number of software releases, while the time to market is decreasing. Rabobank Group ICT has implemented the ITIL-processes and therefore uses the Change-proces for implementing these so called planned changes. Rabobank Group ICT is looking for fact-based insight into sub questions, concerning the impact of changes in the past, to predict the workload at the Service Desk and/or IT Operations after future changes. The challenge is to design a (draft) predictive model, which can be used to implement in a BI environment. The purpose of this predictive model will be to support Business Change Management in implementing software releases with less impact on the Service Desk and/or IT Operations. We have prepared several case-files with anonymous information from Rabobank Netherlands Group ICT for this challenge. The files contain record details from an ITIL Service Management tool called HP Service Manager.

The original data had the information as extracts in CSV with the Interaction-, Incident- or Change-number as case ID. Next to these case-files, we provide you with an Activity-log, related to the Incident-cases. There is also a document detailing the data in the CSV file and providing background to the Service Management tool. All this information is integrated in the labeled property graph in this dataset.

The data contains the following entities and their events

- ServiceComponent - an IT hardware or software component in a financial institute
- ConfigurationItem - an part of a ServiceComponent that can be configured, changed, or modified
- Incident - a problem or issue that occurred at a configuration item of a service component
- Interaction - a logical grouping of activities performed for investigating an incident and identifying a solution for the incident
- Change - a logical grouping of activities performed to change or modify one or more configuration items
- Case_R - a user or worker involved in any of the steps
- KM - an entry in the knowledge database used to resolve incidents


Data Size
---------

BPIC14, nodes: 919838, relationships: 6682386

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

'ogbg-molpcba' is a molecular dataset sampled from PubChem BioAssay. It is a graph prediction dataset from the Open Graph Benchmark (OGB).

This dataset is experimental, and the API is subject to change in future releases.

The below description of the dataset is adapted from the OGB paper:

Input Format

All the molecules are pre-processed using RDKit ([1]).

• Each graph represents a molecule, where nodes are atoms, and edges are chemical bonds.
• Input node features are 9-dimensional, containing atomic number and chirality, as well as other additional atom features such as formal charge and whether the atom is in the ring.
• Input edge features are 3-dimensional, containing bond type, bond stereochemistry, as well as an additional bond feature indicating whether the bond is conjugated.

The exact description of all features is available at https://github.com/snap-stanford/ogb/blob/master/ogb/utils/features.py.

Prediction

The task is to predict 128 different biological activities (inactive/active). See [2] and [3] for more description about these targets. Not all targets apply to each molecule: missing targets are indicated by NaNs.

References

[1]: Greg Landrum, et al. 'RDKit: Open-source cheminformatics'. URL: https://github.com/rdkit/rdkit

[2]: Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding and Vijay Pande. 'Massively Multitask Networks for Drug Discovery'. URL: https://arxiv.org/pdf/1502.02072.pdf

[3]: Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande. MoleculeNet: a benchmark for molecular machine learning. Chemical Science, 9(2):513-530, 2018.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('ogbg_molpcba', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/ogbg_molpcba-0.1.3.png" alt="Visualization" width="500px">

The Graph Database Market size was valued at USD 1.9 USD billion in 2023 and is projected to reach USD 7.91 USD billion by 2032, exhibiting a CAGR of 22.6 % during the forecast period. A graph database is one form of NoSQL database that contains and represents relationships as graphs. Graph databases do not presuppose the data as relations as most contemporary relational databases do, applying nodes, edges, and properties instead. The primary types include property graphs that permit attributes on the nodes and edges and RDF triplestores that center on subject-predicate-object triplets. Some of the features include; the method's ability to traverse relationships at high rates, the schema change is easy and the method is scalable. Some of the familiar use cases are social media, recommendations, anomalies or fraud detection, and knowledge graphs where the relationships are complex and require higher comprehension. These databases are considered valuable where the future connection between the items of data is as significant as the data themselves. Key drivers for this market are: Increasing Adoption of Cloud-based Managed Services to Drive Market Growth. Potential restraints include: Adverse Health Effect May Hamper Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

Reference: Mingxiao Li, Song Gao, Feng Lu, Kang Liu, Hengcai Zhang, Wei Tu. (2021) Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks. International Journal of Geographical Information Science. X(X), XX-XX.Abstract: Dynamic human activity intensity information is of great importance in many location-based applications. However, two limitations remain in the prediction of human activity intensity. First, it is hard to learn the spatial interaction patterns across scales for predicting human activities. Second, social interaction can help model the activity intensity variation but is rarely considered in the existing literature. To mitigate these limitations, we proposed a novel dynamic activity intensity prediction method with deep learning on graphs using the interactions in both physical and social spaces. In this method, the physical interactions and social interactions between spatial units were integrated into a fused graph convolutional network to model multi-type spatial interaction patterns. The future activity intensity variation was predicted by combining the spatial interaction pattern and the temporal pattern of activity intensity series. The method was verified with a country-scale anonymized mobile phone dataset. The results demonstrated that our proposed deep learning method with combining graph convolutional networks and recurrent neural networks outperformed other baseline approaches. This method enables dynamic human activity intensity prediction from a more spatially and socially integrated perspective, which helps improve the performance of modeling human dynamics.

This data collection contains test Word Usage Graphs (WUGs) for English. Find a description of the data format, code to process the data and further datasets on the WUGsite.

The data is provided for testing purposes and thus contains specific data cases, which are sometimes artificially created, sometimes picked from existing data sets. The data contains the following cases:

afternoon_nn: sampled from DWUG EN 2.0.1. 200 uses partly annotated by multiple annotators with 427 judgments. Has clear cluster structure with only one cluster, no graded change, no binary change, and medium agreement of 0.62 Krippendorff's alpha.

arm: standard textbook example for semantic proximity (see reference below). Fully connected graph with six words uses, annotated by author.

plane_nn: sampled from DWUG EN 2.0.1. 200 uses partly annotated by multiple annotators with 1152 judgments. Has clear cluster structure, high graded change, binary change, and high agreement of 0.82 Krippendorff's alpha.

target: similar to arm, but with only three repeated sentences. Fully connected graph with 8 word uses, annotated by author. Same sentence (exactly same string) is annotated with 4, different string is annotated with 1.

Please find more information in the paper referenced below.

Version: 1.2.0, 30.06.2023. Remove instances files as these should be inferred from judgments when aggregating.

Reference

Dominik Schlechtweg. 2023. Human and Computational Measurement of Lexical Semantic Change. PhD thesis. University of Stuttgart.

Using the APROS software, we set up two IESs: a simpler system comprising a microgrid, a steam network, and a compressed air network, where the steam network powers both the microgrid’s generator and the compressor in the compressed air network through a turbine; and a more complex system that builds on this by adding a district heating system fed by the steam network, along with battery storage and photovoltaic units in the microgrid, and a steam storage tank in the steam network. The datasets primarily consist of time-series data reflecting the status of various IES components, recorded at a 1-second resolution to capture how the system’s equipment states respond to external influences.

The code corresponding to the dataset can be found on https://zenodo.org/records/15331160

The Pittsburgh Imaging Project (PIP) fMRI dataset was generated for the research publication Gianaros et al. (2017). Participants conducted interleaved trials of congruent and incongruent phases of the both the Stroop and Multi-Source Inference Tasks (MSIT) with 10 second fixation rest in between trials. There was a separate resting state scan collected from these participants where they simply stared at a fixed crosshair.

Initially this data was meant to be utilized as a stressor evoked task since the Stroop and MSIT tasks are adaptive, whereby higher accuracy scores in the incongruent phases led to shorter intervals between trials. The number of trials were matched to the congruent phases of the task. The data has been processed from functional connectivity matrices to edge time series (Faskowitz et al., 2020) co-fluctuations for the entire timescale of the fMRI task.

In our research, in the linked preprint below, this dataset was used to examine a network perspective of the brain. Cortical flexibility is shown to be paramount to the resolution of a number of tasks, both novel and habitual. During these instances of heightened cortical flexibility there is network change from integrative (more communication across specialized brain regions) to segregative (more communication within regions or hubs) states. We sought to examine whether the basal ganglia and cerebellum act as control states for initiating integration and segregation of cortical networks respectively.

arXiv preprint: https://arxiv.org/abs/2408.07977

References Gianaros, P. J., Sheu, L. K., Uyar, F., Koushik, J., Jennings, J. R., Wager, T. D., Singh, A., & Verstynen, T. D. (2017). A Brain Phenotype for Stressor‐Evoked Blood Pressure Reactivity. Journal of the American Heart Association, 6(9), e006053. https://doi.org/10.1161/JAHA.117.006053 Faskowitz, J., Esfahlani, F. Z., Jo, Y., Sporns, O., & Betzel, R. F. (2020). Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture. Nature Neuroscience, 23(12), 1644–1654. https://doi.org/10.1038/s41593-020-00719-y

The dataset consists of structural vibration data (vertical velocity of floor structure) induced by 10 people’s footsteps as they walk around with 8 different walking speeds, sensed by 5 geophone sensors.

The footstep-induced structural vibration data is stored as footstep traces, each consisting of a series of consecutive footsteps (see the sample plot). The dataset is stored in a MAT-file named People.mat. The dataset includes three layers of labels: 1) person identity i (i = 1, 2, ..., 10), 2) sensor number j (j = 1, 2, ..., 5), and 3) walking speed k (k = 1, 2, ..., 8). The speed k represents the walking speeds of (\mu,\ \ \mu+\sigma,\ \ \mu+2\sigma,\ \ \mu+3\sigma,\ \ \mu-\sigma,\ \ \mu-2\sigma,\ \ \mu-3\sigma), and self-selected speed by each person respectively. (\mu) and (\sigma) refer to the mean and standard deviation of the step frequencies. To access the footstep traces from the person i, sensor j with walking speed k, please use the MATLAB syntax People{i}.Sen{j}.S{k}. This gives a (m\times n) cell structure. (m) denotes the individual trace number, of which the number of traces varies from 10 to 12; (n) represents the level of amplification, including 2000X, 4000X, and 6000X, corresponding to n = 1, n = 2, and n = 3 respectively. To read and plot a sample trace of footstep-induced floor vibration, use the script read_data.m. For more details, please refer to the original FootprintID paper in the following link: https://dl-acm-org.stanford.idm.oclc.org/doi/10.1145/3130954

The human walking experiment involves 10 participants aged between 20 to 29 years old, of which 8 are male and 2 are female. Their walking area is 30ft X 6ft along a hallway with concrete floor. Each of the participants wears flat bottom shoes.

The sensing unit consists of 5 components: 1) the geophone (SM-24), 2) the amplification module, 3) the processor board, 4) the communication module (XBee radio), and 5) the batteries. The sensing unit converts the structural vibration velocity into voltages records. The sampling frequency is 1000Hz.

The hardware unit, experiment setup, and a sample data plot can be found in Experiment Introduction.pdf. Further implementation details can be found in the original FootPrintID paper in the link above.

Please cite this dataset as:

Yiwen Dong, Shijia Pan, Tong Yu, Mostafa Mirshekari, Jonathon Fagert, Amelie Bonde, Ole J. Mengshoel, Pei Zhang, and Hae Young Noh. 2021. The FootprintID Dataset: Footstep-Induced Structural Vibration Data for Person Identification with 8 Different Walking Speeds. Zenodo, DOI: https://doi.org/10.5281/zenodo.4691144

Shijia Pan, Tong Yu, Mostafa Mirshekari, Jonathon Fagert, Amelie Bonde, Ole J. Mengshoel, Hae Young Noh, and Pei Zhang. 2017. FootprintID: Indoor Pedestrian Identification through Ambient Structural Vibration Sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 3, Article 89 (September 2017), 31 pages. DOI: https://doi.org/10.1145/3130954

This paper presents a general framework for simulating plot data in multi-environment field trials with one or more traits. The framework is embedded within the R package FieldSimR, whose core function generates plot errors that capture global field trend, local plot variation, and extraneous variation at a user-defined ratio. FieldSimR’s capacity to simulate realistic plot data makes it a flexible and powerful tool for a wide range of improvement processes in plant breeding, such as the optimisation of experimental designs and statistical analyses of multi-environment field trials. FieldSimR provides crucial functionality that is currently missing in other software for simulating plant breeding programmes and is available on CRAN. The paper includes an example simulation of field trials that evaluate 100 maize hybrids for two traits in three environments. To demonstrate FieldSimR’s value as an optimisation tool, the simulated data set is then used to compare several popular spatial models for their ability to accurately predict the hybrids’ genetic values and reliably estimate the variance parameters of interest. FieldSimR has broader applications to simulating data in other agricultural trials, such as glasshouse experiments.

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Details related to access to the data

• Contact person

Please contact Iris Groen (i.i.a.groen@uva.nl, https://orcid.org/0000-0002-5536-6128) for more information.

Please see the following papers for more details on the data collection and preprocessing:

Groen IIA, Piantoni G, Montenegro S, Flinker A, Devore S, Devinsky O, Doyle W, Dugan P, Friedman D, Ramsey N, Petridou N, Winawer JA (2022) Temporal dynamics of neural responses in human visual cortex. The Journal of Neuroscience 42(40):7562-7580 (https://doi.org/10.1523/JNEUROSCI.1812-21.2022)

Yuasa K, Groen IIA, Piantoni G, Montenegro S, Flinker A, Devore S, Devinsky O, Doyle W, Dugan P, Friedman D, Ramsey N, Petridou N, Winawer JA. Precise Spatial Tuning of Visually Driven Alpha Oscillations in Human Visual Cortex. eLife12:RP90387 https://doi.org/10.7554/eLife.90387.1

Brands AM, Devore S, Devinsky O, Doyle W, Flinker A, Friedman D, Dugan P, Winawer JA, Groen IIA (2024). Temporal dynamics of short-term neural adaptation in human visual cortex. https://doi.org/10.1101/2023.09.13.557378

• Practical information to access the data

Processed data and model fits reported in Groen et al., (2022) are available in derivatives/Groenetal2022TemporalDynamicsECoG as matlab .mat files. Matlab code to load, process and plot these data (including 3D renderings of the participant's surface reconstructions and electrode positions) is available in https://github.com/WinawerLab/ECoG_utils and https://github.com/irisgroen/temporalECoG. These repositories have dependencies on other Matlab toolboxes (e.g., FieldTrip). See instructions on Github for relevant links and guidelines.

Processed data and model fits reported in Yuasa et al., (2023) are available in the Github repositories described in the paper.

Processed data and model fits reported in Brands et al., (2024) are available in derivatives/Brandsetal2024TemporalAdaptationECoGCategories as python .py files. Python code to process and analyze these data is available in the Github repositories described in the paper.

Overview

• Project name

Visual ECoG dataset

• Years that the project ran

Data were collected between 2017-2020. Exact recording dates have been scrubbed for anonymization purposes.

• Brief overview of the tasks in the experiment

Participants sub-p01 to sub-p11 viewed grayscale visual pattern stimuli that were varied in temporal or spatial properties. Participans sub-p11 to sub-p14 additionally saw color images of different image classes (faces, bodies, buildings, objects, scenes, and scrambled) that were varied in temporal properties. See 'Independent Variables' below for more details.

In all tasks, participants were instructed to fixate a cross or point in the center of the screen and monitor it for a color change, i.e. to perform a stimulus-orthogonal task (see the task-specific _events.json files, e.g., task-prf_events.json, for further details).

• Description of the contents of the dataset

The data consists of cortical iEEG recordings in 14 epilepsy patients in response to visual stimulation. Patients were implanted with standard clinical surface (grid) and depth electrodes. Two patients were additionally implanted with a high-density research grid. In addition to the ieeg recordings, pre-implantation MRI T1 scans are provided for the purpose of localizing electrodes. Participants performed a varying number of tasks and runs.

• Independent variables

The data are divided in 6 different sets of stimulus types or events:

1. prf: grayscale, oriented bar stimuli consisting of curved, band-pass filtered lines that were swept across the screen (up to (~16 degree of visual angle) in a fixed order for the purpose of estimating spatial population receptive fields (pRFs).
2. spatialpattern: grayscale, centrally presented pattern stimuli (~16 degree of visual angle diameter) consisting of curved, band-pass filtered lines that were systematically varied in level of contrast and density, as well as various oriented grating stimuli.
3. temporalpattern: grayscale, centrally presented pattern stimuli (~16 degree of visual angle diameter) consisting of curved, band-pass filtered lines that were systematically varied in temporal duration and interval.
4. soc: combination of the spatialpattern and temporalpattern stimuli.
5. sixcatloctemporal: color images of six stimulus classes: faces, bodies (hands/feet only), buildings, objects, scenes and scrambled, systematically varied in temporal duration and interval, whereby interval stimuli consisted of direct repeats of the identical image.
6. sixcatlocisidiff/sixcatlocdiffisi: color images of six stimulus classes: faces, bodies (hands/feet only), buildings, objects, scenes and scrambled, systematically varied in temporal duration and interval, whereby the first interval stimulus was followed by images from either the same or a different category (but not the identical image).

Participant-, task- and run-specific stimuli are provided in the /stimuli folder as matlab .mat files.

• Dependent variables

The main BIDS folder contains the raw voltage data, split up in individual task runs. The /derivatives/ECoGCAR folder contains common-average-referenced version of the data. The /derivatives/ECoGBroadband folder contains time-varying broadband responses estimated by band-pass filtering the common-average-referenced voltage data and taking the average power envelope. The /derivatives/ECoGPreprocessed folder contains epoched trials used in Brands et al., (2024). The /derivatives/freesurfer folder contains surface reconstructions of each participant's T1, along with retinotopic atlas files. The /derivatives/Groen2022TemporalDynamicsECoG contains preprocessed data and model fits that can be used to reproduce the results reported in Groen et al., (2022). The /derivatives/Brands2024TemporalAdaptationECoG contains preprocessed data and model fits that can be used to reproduce the results reported in Brands et al., (2024).

• Quality assessment of the data

Data quality and number of trials per subjects varies considerably across patients, for various reasons.

First, for each recording session, attempts were made to optimize the environment for running visual experiments; e.g. room illumination was stabilized as much as possible by closing blinds when available, the visual display was calibrated (for most patients), and interference from medical staff or visitors was minimized. However, it was not possible to equate this with great precision across patients and sessions/runs.

Second, implantations were determined based on clinical needs and electrode locations therefore vary across participants. The strength and robustness of the neural responses varies greatly with the electrode location (e.g. early vs higher-level visual cortex), as well as with uncontrolled factors such as how well the electrode made contact with the cortex and whether it was primarily situated on grey matter (surface/grid electrodes) or could be located in white matter (some depth electrodes). Electrodes that were marked as containing epileptic activity by clinicians, or that did not have good signal based on visual inspection of the raw data, are marked as 'bad' in the channels.tsv files.

Third, patients varied greatly in their cognitive abilities and mental/medical state, which affected their ability to follow task instructions, e.g. to remain alert and fixation. Some patients were able to perform repeated runs of multiple tasks across multiple sessions, while others only managed to do a few runs.

All patients included in this dataset have sufficiently good responses in some electrodes/tasks as judged by Groen et al., (2022) and Brands et al., (2024). However, when using this dataset to address further research questions, it is advisable to set stringent requirements on electrode and trial selection. See Groen et al., (2022) and associated code repository for an example preprocessing pipeline that selected for robust visual responses to temporally- and contrast-varying stimuli.

Methods

• Subjects

All participants were intractable epilepsy patients who were undergoing ECoG for the purpose of monitoring seizures. Participants were included if their implantation covered parts of visual cortex and if they consented to participate in research.

• Apparatus

Data were collected in a clinical setting, i.e. at bedside in the patient's hospital room. Information about iEEG recording apparatus is provided the meta data for each patient. Information about the visual stimulation equipment and behavioral response recordings are provided in Groen et al., (2022), Yuasa et al., (2023) and Brands et al., (2024).

• Experimental location

Data were collected at NYU University Langone Hospital (New York, USA) or at University Medical Center Utrecht (The Netherlands).

• Missing data

Stimulus files are missing for a few runs of sub-02. These are marked as N/A in the associated event files.

Notes

Further participant-specific notes:

• For sub-03 and sub-04 the spatial pattern and temporal pattern stimuli are combined in the soc task runs, for the remaining participants these are split across the spatialpattern and temporalpattern task runs.

• The pRF task from sub-04 has different prf parameters (bar duration and gap).

• The first two runs of the pRF task from sub-05 are not of good quality (participant repeatedly broke fixation). In addition, the triggers in all pRF runs from sub-05 are not correct due to a stimulus coding problem and will need to be re-interpolated if one wishes to use these data.

• Participants sub-10 and sub-11 have high density grids in addition to clinical grids.

• Note that all stimuli and stimulus parameters can be found in the participant-specific stimulus *.mat files.

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