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  • Repository of design files for the HardwareX article titled "A low-cost and customizable alternative for commercial implantable cannula for intracerebral administration in mice". Contributing authors: Rogneda B Kazanskaya, Alexander V Lopachev, Tatiana N Fedorova, Raul R Gainetdinov, Anna B Volnova. Abstract: Stereotaxic intracerebral cannula implantation for neuroactive agent administration is a wide-spread method for chronic experiments requiring bypassing the blood-brain barrier in rodents. However, commercially available cannula are bulky and may interfere with animal movement or lead to their dislodging during grooming. As the number of cannula needed in one experiment, and the accompanying costs can be high, it is in the interest of researchers to produce them on their own. Custom cannula manufacturing also offers the flexibility of different cannula lengths, which is required for agent delivery to various brain structures. In this article we present a protocol for making guide cannula along with the accompanying systems required for injection, which are small, cost-effective, light, easy to make, reusable, and can be made from easily procured materials.
    Data Types:
    • Image
    • Dataset
  • Este fichero contiene los códigos de la programación realizada en Arduino de los trabajos prácticos de Física y Química para el nivel de bachillerato. Estos códigos son de software libre: puede redistribuirlo y/o modificarlo bajo los términos de la Licencia Pública General GNU publicada por la Free Software Foundation.
    Data Types:
    • Other
    • Dataset
  • bug data of open-source applications
    Data Types:
    • Dataset
    • File Set
  • This repository contains the image dataset and the manual annotations used to develop the HEPASS algorithm for automated liver steatosis quantification: - Salvi M., Molinaro M., Metovic J., Patrono D., Romagnoli R., Papotti M, and Molinari F., "Fully Automated Quantitative Assessment of Hepatic Steatosis in Liver Transplants", Computers in Biology and Medicine 2020 (DOI: 10.​1016/​j.​compbiomed.​2020.​103836) ABSTRACT Background: The presence of macro- and microvesicular steatosis is one of the major risk factors for liver transplantation. An accurate assessment of the steatosis percentage is crucial for determining liver graft transplantability, which is currently based on the pathologists’ visual evaluations on liver histology specimens. Method: The aim of this study was to develop and validate a fully automated algorithm, called HEPASS (HEPatic Adaptive Steatosis Segmentation), for both micro- and macro-steatosis detection in digital liver histological images. The proposed method employs a hybrid deep learning framework, combining the accuracy of an adaptive threshold with the semantic segmentation of a deep convolutional neural network. Starting from all white regions, the HEPASS algorithm was able to detect lipid droplets and classify them into micro- or macrosteatosis. Results: The proposed method was developed and tested on 385 hematoxylin and eosin (H&E) stained images coming from 77 liver donors. Automated results were compared with manual annotations and nine state-of-the-art techniques designed for steatosis segmentation. In the TEST set, the algorithm was characterized by 97.27% accuracy in steatosis quantification (average error 1.07%, maximum average error 5.62%) and outperformed all the compared methods. Conclusions: To the best of our knowledge, the proposed algorithm is the first fully automated algorithm for the assessment of both micro- and macrosteatosis in H&E stained liver tissue images. Being very fast (average computational time 0.72 seconds), this algorithm paves the way for automated, quantitative and real-time liver graft assessments.
    Data Types:
    • Dataset
    • File Set
  • This DB based on all available reports by the Communicational center of Government of the Russian Federation. Official Russian COVID-19 data published daily by the Government of Russia (on the Russian language) in the form of raw data is a daily updated report in a pdf form. Each piece has daily updates. We are providing a working link on every cell of data in the dataset. This DB is an attempt to manually collect critical variables from the report into a machine-readable format. These datasets are ready to be used for analysis and modeling. Variables: location; date; new cases [diagnosed]; cases [cumulative]; recovered [new]; recovered [cumulative]; deaths [new]; deaths [cumulative]; tests [new tests administered]; tests [cumulative]; test_positive [cumulative]; hospitalization [cumulative]; icu [cumulative or population]; on_invasive_ventilators [cumulative or population]; test_negative [cumulative]; hospital beds; web links. All Data divided by date (time) and regions (Oblast) of the Russian Federation.
    Data Types:
    • Tabular Data
    • Dataset
  • This file provides the detailed dataset on the hydrological characteristics, nutrient concentrations and nitrate isotopic compositions collected in each station in the JRE during the investigation period. This dataset is used to generate Table 3 and Figures 2-7 in the manuscript.
    Data Types:
    • Dataset
    • Document
  • In the datasets, the scale of organizational culture based on eleven items of primary cultural values (PCV) and nine items of secondary cultural values (SCV). Work motivation variables in the datasets used four items of motive motivation (MM), seven items of expectation motivation (ME), and with nine items of incentive motivation (IM). Interpersonal communication variables were seven items of social sensitivity (SS), nine items of social insight (SI), and four items of social communication skills (SCM). The items, including labels and ratings, will be explained later, with more comprehensive details as supplementary material (see "Annotated Questionnaire" in supplementary material). This was because each item had different rankings and choices. To measure teacher performance used three scales from the book of effective teacher performance. The teacher performance scale used three variables of thirteen items of the learning process (LL), four items of scientific work (SW), and three items of service (S).
    Data Types:
    • Software/Code
    • Tabular Data
    • Dataset
    • Document
  • Market survey, lab experiment, field experiment, instructions, energy bills
    Data Types:
    • Dataset
    • File Set
  • Dataset: heavy_metal_pollution_indice_data.xslx Dataset description: Concertation of trace metals (Al, Fe) and heavy metals (V, Cr, Co, Ni, Cu, Sn, As, Cd, Pb) given in mg/kg soil dry weight and calculated pollution indices (Igeo: Geoaccumulation Index; PLI: Pollution load index; RI: Potential ecological risk index). For methods of analyses and index calculation see method chapter in above mentioned publication Dataset: pavement_joint_features_Marburg_city.xlsx Dataset description: Features of pavement joints in the inner-city of Marburg (Hesse, Germany) recorded during field work. Distance_street: Distance (m) of each sampling point to next street. Height: Heigth (m a.s.l.) taken from DEM. Av_joint_size: Average joint size (cm) from 1 square meter pavement measured during field work. Plant_coverage: Plant coverage classified from 0 to 5. For further description of classes see above mentioned publication. Runoff:_accumulation: Runoff accumulation classified in three classes: 0 – 2.5 - 5. For further description of classes see above mentioned publication. pH: pH (KCl) values of pavement joint soil material. OM: Organic matter content (mass-%) of pavement joint soil material.
    Data Types:
    • Tabular Data
    • Dataset
  • This database provides the interviews done to the students at the end of the Teaching Innovation Project: "Writing an academic review in tele-collaborative settings" (PID 181960), carried out at the Universitat de les Illes Balears (UIB) with the collaboration of the Open University of Catalonia (UOC) during the academic year 2018-2019. We have used some of the information obtained from these interviews in the article: "Collaborative Writing at Work: Peer Feedback in a Blended Learning Environment" Article Summary: This exploratory study aims to analyse the nature of peer feedback during a collaborative writing assignment, and to identify the possible effects feedback has on the revision of a text written by university students in a blended learning environment. Under analysis are two different graduate’s courses in academic writing, during which, over a period of a whole semester, the students (n = 85) were divided into 25 work groups to carry out a co-evaluation assignment with the support of a technology platform. The results obtained indicate that, when collaborative writing includes peer feedback, instead of unidirectional corrections from the teacher, the students respond more reflectively and constructively, they discuss the content they are working with, and, as a result, they effect significant changes in their own writing.
    Data Types:
    • Other
    • Dataset