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Computistical objects and intellectual networks in the Carolingian age

Roughly 400 manuscripts written in the Carolingian period (pre-AD 900) contain a considerable corpus of calendrical knowledge. In most manuscripts, this material amounts to a loosely structured compilation of tables, diagrams, definitions, and algorithms. What is the nightmare of modern cataloguers provides the researcher of intellectual networks with a significant sample size of (often datable) data. The first step is to design a methodology for classifying this data, which is done by providing each item with an unambiguous identifier (termed ‘object oriented cataloguing’). This creates a data set of c. 4000 objects. If network analysis is applied to this data set, this reveals datable patterns for the transmission of ideas and the construction of intellectual networks in the Carolingian age.

A saga in a network and a network of a saga

Computer-assisted methods of analysis open wide possibilities for asking new research questions and exploring new areas of textual and manuscript studies, which lie outside the reach of traditional methods. These can be questions of historical reception of texts, ethnic genre in literature, or authorship attribution. At the same time, computer-assisted analysis of texts preserved in manuscript form poses numerous challenges, from the instability of manuscripts as artefacts, through orthographic and textual variations, to multiple reworkings of texts. The proposed paper illustrates some of these possibilities and challenges on an example of the transmission history of a single Old Norse saga: Hrómundar saga Greipssonar. The presentation will be based my doctoral thesis defended at the University of Copenhagen in December 2018. In my unpublished thesis I examined the textual transmission of Hrómundar saga in multiple manuscripts from perspective of material philology (Nichols 1990; 1997) and transmission history (Ruh 1985; Löser 2004). Inspired by Alaric Hall’s (2013) work on Konráðs saga keisarasonar, the main focus of my study was on the stemmatic relationships among various material texts of the saga (witnesses) and on the manuscript context in which the saga appears (co-occurrence with other texts). These networks of texts and manuscripts played a significant role in understanding the transmission and reception of the saga throughout centuries. This research resulted in a discovery of a new saga of Hrómundur, which appears in a quite different manuscript context than the older saga.

Travelling Annotations: Network Analysis as a Tool to Study Glossing Networks in Carolingian Europe

While traditionally, the transmission of medieval texts is studied by means of stemmatics, certian types of textuality are well-known as being particularly resistent to traditional methods. This is also the case with annotations. While annotations can behave text-like, it is more often the case that each individual gloss must be treated as an autonomous entity. In different manuscripts different combinations of glosses are combined so that two manuscripts may contain a very different assembly of glosses and look dissimilar, while being closely related. In such cases, network analysis proves handy as a mean to reveal connection between manuscripts and trace the patterns of transmission of particular annotations, while opening new ways of using this transmission as a proxy for studying the intellectual networks that participated in such an exchange. In this presentation, I will exemplify this approach on the corpus of early medieval annotations to the Etymologiae of Isidore of Seville, the most important medieval Latin encyclopaedia. More specifically, it can be presupposed that most of the glosses to this text came into being in the context of its use for teaching in Carolingian period (c. 750 – 900). Their transfer, thus, may be related to the circulation of schoolmasters, students, and books through the networks of Carolingian schools.

Europe Road Network extracted from OpenStreetMap data

The data extracts of Europe region downloaded on 04/07/2020 was used to create this dataset. From this the highways tagged as motorway, trunk, primary, secondary, tertiary, unclassified and residential are selected and the information was saved as line strings. CRS: WGS84 (EPSG:4326) Files available are in parquet and csv format. Please feel free to convert the files in to desired file formats.

Chinese Rotarians' Affiliation Network Data

This set of files contains the edge list and the node list used for building the network of affiliation of the Chinese members of the Rotary Club of Shanghai. The network contains two categories of nodes: the persons (Chinese Rotarians) and the institutions in which they held positions in their lifetime. The edge list specifies the nature of the position, the date (year) when and the place (city) where it was held, and the strength of the connection for each pair (weight). The additional file (Rotary_ZH_position_clean) contains the original data extracted from contemporary Who's Who.

opencc-by-4.0Aug 2020View details →

Network services after reconfiguration

Data before and after reconfiguration of network services using the ETSI standard workflow

Figure 4 Network plots for CD013155

High resolution image of network plots for the Cochrane review: "Treatment for bleeding oesophageal varices in people with decompensated liver cirrhosis: a network meta-analysis" (DOI: 10.1002/14651858.CD013155)

Figure 4 Network plots for CD013122

This is the high-resolution image of network plots for the Cochrane review: "Secondary prevention of variceal bleeding in adults with previous oesophageal variceal bleeding due to decompensated liver cirrhosis: a network meta-analysis (DOI: 10.1002/14651858.CD013122)"

opencc-zeroDec 2020View details →

dryad36/100

COVID-CDR: An integrative resource for network-based investigation of COVID-19 combinatorial drug repositioning and mechanism of action

Repurposing of the existing medications has become the mainstream focus of anti-COVID-19 drug discovery as it offers rapid and cost-effective solutions for therapeutic development. However, a great effort is still required to enhance the efficacy of repurposing therapeutic options through combination therapy, in which promising drugs with varying mechanisms of action are administered together. Nonetheless, our ability to identify and validate effective combinations is limited due to the vast number of possible drug pairs. Yet, there is no available resource that can systematically guide to identify or choose the effective individual drugs or best possible synergistic drug combinations for the treatment of SARS-CoV-2 infection. To address this resource gap, we developed a web-based platform that displays the network-based mechanism of action of drug combinations, thus simultaneously providing a visual of the cellular interactome involved in modes of action of the chosen drugs. The platform allows the freedom to select two or more drug combinations and provides the options to investigate the network-based efficacy of drug combinations and understand the similarity score, primary indications, and pairwise synergistic scores on different cell-lines. In a nutshell, the platform (accessible via <a href="http://vafaeelab.com/COVID19_repositioning.html">http://vafaeelab.com/COVID19_repositioning.html</a>) is the first of its type, which provides a systematic approach for pre-clinical investigation of combination therapy for treating COVID-19 at the fingertips of the clinicians or researchers.

Measures of Freight Network Resiliency During the Covid-19 Pandemic

Recent headlines depict significant shifts in operations within the freight community in particular, e.g., HOS laws suspended at a national level for the first time in 82 years1; national carriers shifting operations completely to grocery supply chains2; fleet operators laying off employees in response to manufacturing closures3.  As a result of the current COVID-19 pandemic, there is a great need to capture freight movement data (not otherwise collected) to measure the effects of the COVID-19 response and recovery practices on freight network resiliency.  In this project, we consider an expanded definition of the freight network, beyond roads and warehouses, to include truck drivers and driver support systems.   Driver support systems include physical infrastructure like public and private rest stops as well as operational protections like Hours of Service (HOS). COVID-19 responses by public agencies and private citizens have affected drivers and driver support systems by three mechanisms. First, increased demand for medical supplies, food and packaged goods creates a need for more trucks and drivers, and the increased need for quick shipments promotes an environment in which speeding and unsafe driving practices may prevail.  Second, with HOS restrictions lifted by the National Highway Transportation Safety Administration (NHTSA) driver fatigue may occur at greater frequency leading to unsafe driving conditions and higher likelihood of accidents.  Third, the effects of social distancing mandates can lead to closures of critical, but oft forgotten, freight infrastructure like rest areas and truck stops, leaving drivers without necessary rest opportunities. While any single mechanism has detrimental effects on driver health and safety, the economy, and national recovery efforts, when combined, the system can be pushed to failure. Pandemic responses have only exacerbated critical industry issues like driver shortages, lack of available parking, and HOS compliance issues stemming from electronic logbooks.   The purpose of this work was to develop and implement a driver health and safety survey during the pandemic.

DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks

Dataset and code used in V Wåhlstrand-Skärström, et al, "DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks", published in Journal of Microscopy. In this work, we develop a new approach for FRAP analysis based on deep neural networks. From a numerical FRAP model developed in previous work, we generate a very large set of realistic, simulated recovery curve data. The data is used for training deep neural network regression models for prediction of e.g. the diffusion coefficient. We compare the performance of the neural network estimation framework to conventional least squares estimation on simulated and  experimental data. Herein, the simulated FRAP data used for the training, validation, and test data sets, the experimental data, and the Matlab and Python/Tensorflow code are supplied.

Palmitic Acid Sophorolipid Biosurfactant: From Self-Assembled Fibrillar Network (SAFiN) To Hydrogels with Fast Recovery

Data are zipped using Winrar 5.40 version Folder "Figures": it contains the images of Figure 1 through 6. Images are available as datasets readable with Origin software (any version) or QTplot software. Folder "Figure 4": it contains images corresponding to Figure 4 and Figure S5. Images can be read nd processed with Fiji software. Folder also contains ascii files corresponding to the plots. File "Figures": this is the powerpoint file containing all composed images of the main text and Supporting Information. Plots can be opened with Origin software.

Dataset of alternative process plan networks for dynamic integrated process planning and scheduling

This dataset includes 3D models of representative manufacturing parts (jobs) as well as their features. For each of the manufacturing part, alternative process plan networks are given containing alternative machine tools, cutting tools, Tool Access Directions (TADs) and manufacturing times. The dataset also includes a detailed technical specification for all parts and calculated manufacturing times for all operations based on given alternative manufacturing resources.

Data: Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy

We make available all the brain network data, and metadata of 83 patients and 29 healthy controls included in our study. Nishant Sinha, Natalie Peternell, Gabrielle M. Schroeder, Jane de Tisi, Sjoerd B. Vos, Gavin P. Winston, John S. Duncan, Yujiang Wang, and Peter N. Taylor "Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy." Epilepsia 2021 doi:10.1111/epi.16819. Methodological details on MRI acquisition and data processing are provided in our manuscript. We request users to kindly cite our article and data appropriately.

Data used in 'The joint role of coevolutionary selection and network structure in shaping trait complementarity in mutualisms' manuscript

The files in this repository correspond to the raw and processed data described in the 'The joint role of coevolutionary selection and network structure in shaping trait complementarity in mutualisms' manuscript. Once expanded, the zip files contains two directories and one documentation file, named data_documentation. Please refer to this file for a thorough description of the organization and contents of raw and processed data files.

Convolutional Neural Network Formulation to Compare 4D Seismic and Reservoir Simulation Models

This dataset contains the .npy (numpy) files of the simulation models and reference discussed in the paper "Convolutional Neural Network Formulation to Compare 4D Seismic and Reservoir Simulation Models". The folders contain all simulation models and reference maps already divided in subregions. Each .npy file is a numpy 2D array with delta IP or delta Sw values. The csv files contain the 3-tuples and the selected model in each. There are two csv files: the first is the dataset used for training the CNN, with 1280 labeled tuples evaluated by a single specialist. The second is the ground-truth, with 164 tuples evaluated by three specialists (in which 2 or more agreed on the selected model), used for validating the models and comparing different approaches. We also provide a Python code to read and visualize the .npy files.

Glottis Analysis Tools - Deep Neural Networks

Netron Overview Diagrams of Deep Neural Networks (DNNs) shipped with Glottis Analysis Tools (GAT) 2020.

opencc-zeroJan 2021View details →

dryad36/100

Analysing landscape effects on dispersal networks and gene flow with genetic graphs

Graph-theoretic approaches have relevant applications in landscape genetic analyses. When species form populations in discrete habitat patches, genetic graphs can be used i) to identify direct dispersal paths followed by propagules or ii) to quantify landscape effects on multigenerational gene flow. However, the influence of their construction parameters remains to be explored. Using a simulation approach, we constructed genetic graphs using several pruning methods (geographical distance thresholds, topological constraints, statistical inference) and genetic distances to weight graph links (FST, DPS, Euclidean genetic distances). We then compared the capacity of these different graphs to i) identify the precise topology of the dispersal network and ii) to infer landscape resistance to gene flow from the relationship between cost-distances and genetic distances. Although not always clear-cut, our results showed that methods based on geographical distance thresholds seem to better identify dispersal networks in most cases. More interestingly, our study demonstrates that a subselection of pairwise distances through graph pruning (thereby reducing the number of data points) can counter-intuitively lead to improved inferences of landscape effects on dispersal. Finally, we showed that genetic distances such as the DPS or Euclidean genetic distances should be preferred over the FST for landscape effect inference as they respond faster to landscape changes.

Repository: Irregular Polygonal Ridge Networks in Ancient Noachian Terrain on Mars

This repository contains: <ol> <li>Shapefiles of irregular polygonal ridge networks within mapping extent.</li> <li>MOLA elevation and slope data extracted over mapped ridge networks.</li> </ol>

A Large Open Dataset from the Parler Social Network

There are two main zip files in this dataset. Each zip file contains multiple <a href="http://ndjson.org/">newline delimited JSON files</a>. The JSON objects in each file contain all the key/value pairs returned from their respective Parler API endpoints. NOTE: All identifying data has been redacted by removing the "name" field from posts, comments and user profiles. <a href="https://arxiv.org/abs/2101.03820">Current paper</a> for more details and preferred citation information.   Parler Users (parler_user.zip) Returned by the /v1/profile endpoint. Each line consists of an individual user profile object. Parler Posts and Comments (parlerpostcomments.zip) Returned by the /v1/post[comment] endpoints. Each line consists of an individual post or comment object. How to extract the data: The data provided is compressed in zip format. See the instructions below on how to extract it: <ul> <li>Linux and Mac</li> </ul> Navigate to the file in a terminal window or file browser. Run the following command (or typically you can double click if you are in a file browser): User profiles: <pre><code>unzip parler_users.zip</code></pre> Posts and comments: <pre><code>unzip parler_data.zip</code></pre> The unzipped version contains multiple files for each data type. <ul> <li>Windows</li> </ul> Microsoft provides instructions on to extract a zip file. The authors cannot recommend a particular method or software.