cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c5s17t34v
title: MassIVE MSV000092172 - TGFbeta released by Schwann cells increases Cholangiocarcinoma malignant features inducing migration and proliferation in an in vitro model of perineural invasion
title: 30 ug of proteins extracted from each sample were loaded on a pre cast acrylamide gradient gel and run for about 10 minutes in MOPS buffer. Stained bands were excised, destained in ammonium bicarbonate/water/ acetonitrile, and after the reduction and alkylation of cysteine residues, an enzymatic digestion with trypsin was performed at 37C O.N. After digestion, the resulted peptide solution was removed from bands and stored in a new tube; a peptide extraction step was done with a Acetonitrile/Formic acid 2.5% (50:50, v:v) solution in order to extract peptides entirely from bands; this solution was collected with the previous O.N. digested mixtures. Then, the mixture was dried down in a vacuum centrifuge and resuspended in 100 uL of a solution containing 95% water, 5% acetonitrile, and 0.1% formic acid. 5uL of the resulted peptide solution was injected in an Ultimate 3000 UHPLC (Dionex, Thermo Fisher Scientific) coupled with an Orbitrap Fusion Tribrid mass spectrometer (Thermo Fisher Scientific, CA, United States). Peptides were desalted on a trap column (Acclaim PepMap 100 C18, Thermo Fisher Scientific) and then separated on a 20 cm long silica capillary (Silica Tips FS 360 75 8, New Objective, MA, United States), packed in house with a C18, 1.9 um, 100 Angstrom resin (Michrom BioResources, CA, United States). The analytical separation was run for 180 min using a gradient of buffer A (95% water, 5% acetonitrile, and 0.1% formic acid) and buffer B (95% acetonitrile, 5% water, and 0.1% formic acid). The chromatographic starting point was 5% of buffer B, in 5 minutes B rose to 6% , in 130 minutes to 32%, after 25 minutes to 55% and in 4 minutes up to 80% of B. Full scan MS data were acquired in the 350 1,550 m/z range in the Orbitrap with 120K resolution. Data dependent acquisition was performed in top speed mode (3 s long maximum total cycle): the most intense precursors were selected through a monoisotopic precursor selection (MIPS) filter and with charge &gt;1, quadrupole isolated and fragmented by higher energy collision dissociation (HCD) (32% collision energy). Fragment ions were analyzed in the ion trap with rapid scan rate. Raw data were analyzed by Proteome Discoverer 2.4 (Thermo Fisher Scientific) using the Homo Sapiens database from UniProtKB/Swiss Prot database. Spectral matches were filtered using Percolator node, based on q values, with 1% false discovery rate (FDR). Only master proteins were taken into account and only specific trypsin cleavages with two miscleavages were admitted. Cysteine carbamydomethylation was set as static modification, while methionine oxidation and N acetylation on protein terminus were set as variable modifications. 15 ppm and 0.6 Da tolerance were considered for MS an MS /MS data assignment, respectively. Quantification was based on precursor intensity of unique and razor peptides using match between runs option.
DOI: 10.17632/m5jwhx69zk.1
title: Schisandrin A alleviates spatial learning and memory impairment in diabetic rats by inhibiting inflammatory response and through modulation of PI3K/AKT pathway

description: Original data for Schisandrin A alleviates spatial learning and memory impairment in diabetic rats by inhibiting inflammatory response and through modulation of PI3K/AKT pathway.
DOI: 10.17632/m5jwhx69zk.2
title: Schisandrin A 

description: Original data for Schisandrin A
DOI: 10.5281/zenodo.8027138
title: BISCUIT: Causal Representation Learning from Binary Interactions

description: This repository contains the datasets from the paper "BISCUIT: Causal Representation Learning from Binary Interactions" (link) by Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves. <strong>iTHOR Embodied AI </strong>- The Embodied AI dataset is generated with the iTHOR simulator. We use the default kitchen environment, FloorPlan10, and position the robot in front of the kitchen counter. The robot interacts with different objects in the environment, including a Microwave, cabinet, stove, and an egg. For more details on the dataset, see our GitHub repository and the appendix of our paper. <strong>CausalWorld </strong>- The CausalWorld environment implements a tri-finger robot which can interact with a cube in the center of a stage. We additionally introduce causal variables for the friction parameters of the stage, floor and cube, as well as color changes. For more details on the dataset as well as the code to generate this dataset, see our GitHub repository and the appendix of our paper. <strong>Voronoi</strong> - The Voronoi benchmark allows for creating causal systems with arbitrary number of causal variables and causal graphs. We provide datasets with 6 and 9 variables, as well as systems with minimal number of interactions. For details, see our GitHub repository and our paper.
description: {"references": ["Lippe, P., Magliacane, S., L\u00f6we, S., Asano, Y. M., Cohen, T., & Gavves, E. (2023). BISCUIT: Causal Representation Learning from Binary Interactions. In: Uncertainty in Artificial Intelligence (UAI), 2023."]}
DOI: 10.5281/zenodo.8027137
title: BISCUIT: Causal Representation Learning from Binary Interactions

description: This repository contains the datasets from the paper "BISCUIT: Causal Representation Learning from Binary Interactions" (link) by Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves. <strong>iTHOR Embodied AI </strong>- The Embodied AI dataset is generated with the iTHOR simulator. We use the default kitchen environment, FloorPlan10, and position the robot in front of the kitchen counter. The robot interacts with different objects in the environment, including a Microwave, cabinet, stove, and an egg. For more details on the dataset, see our GitHub repository and the appendix of our paper. <strong>CausalWorld </strong>- The CausalWorld environment implements a tri-finger robot which can interact with a cube in the center of a stage. We additionally introduce causal variables for the friction parameters of the stage, floor and cube, as well as color changes. For more details on the dataset as well as the code to generate this dataset, see our GitHub repository and the appendix of our paper. <strong>Voronoi</strong> - The Voronoi benchmark allows for creating causal systems with arbitrary number of causal variables and causal graphs. We provide datasets with 6 and 9 variables, as well as systems with minimal number of interactions. For details, see our GitHub repository and our paper.
description: {"references": ["Lippe, P., Magliacane, S., L\u00f6we, S., Asano, Y. M., Cohen, T., & Gavves, E. (2023). BISCUIT: Causal Representation Learning from Binary Interactions. In: Uncertainty in Artificial Intelligence (UAI), 2023."]}
DOI: 10.17632/9kjr9kj84d.1
title: Datasets of Paper Titled "Digital Society Social Interactions and Trust Analysis Model"  

description: It is Python code for constructing a simulator code for simulating a digital society. The simulator is configurable with multiple parameters for simulating the behavior of people who are influencing each other by writing online social media such Tweeter in context of Covid-19 event and a level of trust they feel in the society.
DOI: 10.17632/9kjr9kj84d.2
title: Content  Analysis of Twitter Data related to Trust and Covid-19

description: This is a public repository of the ongoing research work on ‘Content  Analysis of Twitter Data related to Trust and Covid-19’ . For accessing the live streams of tweets , for downloading the existing tweets (hydration) based on the filters and for dehydrating the tweeter data twac tool, twitter api , tweepy libraries were utilized. The python code consists of two notebooks for the said purposes
DOI: 10.17632/9kjr9kj84d.3
title: Content  Analysis of Twitter Data related to Trust and Covid-19

description: This is a public repository of the ongoing research work on ‘Content  Analysis of Twitter Data related to Trust and Covid-19’ . For accessing the live streams of tweets , for downloading the existing tweets (hydration) based on the filters and for dehydrating the tweeter data twac tool, twitter api , tweepy libraries were utilized. The python code consists of two notebooks for the said purposes. it includes Master Data (Tweeter Data ) notebook having data cleaning function.The cleaning of the tweeter data include removal of emojis, unwanted characters and symbols , image links and other unwanted objects.
DOI: 10.17632/9kjr9kj84d.4
title: Content  Analysis of Twitter Data related to Trust and Covid-19

description: This is a public repository of the ongoing research work on ‘Content  Analysis of Twitter Data related to Trust and Covid-19’ . For accessing the live streams of tweets , for downloading the existing tweets (hydration) based on the filters and for dehydrating the tweeter data twac tool, twitter api , tweepy libraries were utilized. The python code consists of two notebooks for the said purposes. it includes Master Data (Tweeter Data ) notebook having data cleaning function.The cleaning of the tweeter data include removal of emojis, unwanted characters and symbols , image links and other unwanted objects.
DOI: 10.17632/4myr9st5pz.1
title: SarsCov-2 data KCMC

description: Immunoglobulin G responses to SARS-CoV-2: Trends and Patterns of Adverse Vaccination Effects among Health Care Workers in North-Eastern Tanzania
DOI: 10.17632/2dw98w9hg3.1
title: Supplemental material 1: Flowchart of the course of the study

description: Flowchart of the course of the study
DOI: 10.17632/w8fxrg4pv5.1
title: EDABE

description: EDABE dataset
DOI: 10.17632/mzsc9c4yk5.1
title: Network dataset of the Afrikaans film industry (1916–2016)

description: The dataset includes all role players listed at the end credits of all full-length Afrikaans feature films (excluding short films and documentaries) released within the first century of the Afrikaans film industry (1916-2016). The nodes table includes the type of node (film or person) and, in the case of films, also the release date. The edges table includes the person, the film to which the person made a contribution, the date of the contribution (identical to the release date of the film), the type of contribution (acting or production), as well as the detailed contribution (e.g. colour grading, editing or camerawork). The dataset allows for the construction of a bipartite network, which can be filtered to be a film-actor network or film-production crew network, or the entire industry, and by including dates, the network can be constructed as a dynamic network. This dataset is the most comprehensive dataset on the Afrikaans film industry, since it was compiled using the films themselves.
DOI: 10.17632/fcyfn32mv6.1
title: Dataset containing posts and comments from university publics on the social media VKontakte

description: The dataset consists of content published in groups of Russian universities on the social media VKontakte. The dataset contains posts and comments from 9,187 university publics from September 2021 to June 2022 (one academic year).
DOI: 10.17632/27t4vpcrj5.1
title: Occurrence and origin of analcime in shale of faulted-depressed lacustrine basin: micro scale response of deep fluid activity

description: The information in the "Element Content" includes: 
1.POINT NUMBER (test number)
2.MINERALS(analcime and clay)
3.ANALCIME TYPE(analcime types based on occurrence mentioned in manuscript)
and Figure 6,7,8,9,10,12; Table 2,3 is based on File "Element Content"
The information in the "Analcime Content" includes:
1.SAMPLE NUMBER
2.DEPTH(m)
3.ANALCIME TYPE(analcime types based on occurrence mentioned in manuscript)
Figure 2,13 is based on File "Analcime Content"
DOI: 10.5281/zenodo.7919725
title: Dataset - Mumme et al. 2023 Global Change Biology

description: Dataset used in Mumme et al. (2023) Wherever I may roam – Human activity alters movements of red deer (<em>Cervus elaphus</em>) and elk (<em>Cervus canadensis</em>) across two continents. For full list of funding information, please see the acknowledgement section of the original article (https://doi.org/10.1111/gcb.16769).
DOI: 10.5281/zenodo.7919724
title: Dataset - Mumme et al. 2023 Global Change Biology

description: Dataset used in Mumme et al. (2023) Wherever I may roam – Human activity alters movements of red deer (<em>Cervus elaphus</em>) and elk (<em>Cervus canadensis</em>) across two continents. For full list of funding information, please see the acknowledgement section of the original article (https://doi.org/10.1111/gcb.16769).
DOI: 10.17632/6rb2sfz8dk.1
title: Supplementary material : The role of forests, peatlands and lakes on catchment response to rainfall events in boreal landscapes

description: Folder "data" contains all data needed to produce figures 3, 4, 5, 7, and 8 and supplementary figures using the "R code" script
DOI: 10.17632/6rb2sfz8dk.2
title: Supplementary material : The role of forests, peatlands and lakes on catchment response to rainfall events in boreal landscapes

description: Folder "data" contains all data needed to produce figures 3, 4, 5, 7, and 8 and supplementary figures using the "R code" script
DOI: 10.17632/bj48vtrph7.1
title: HYDRABOT: AN ARDUINO-BASED ROBOT FOR MEASURING WATER CONDUCTIVITY AND TURBIDITY TO DETERMINE WATER QUALITY

description: Water is essential to both bodies and the environment since it moves nutrients, waste products, and oxygen to and from cells and organs, supporting various actions and results across the state. Turbidity is the measure of the relative clarity of a liquid. According to Fondriest Environmental Learning Center (2019), turbidity is caused by suspended sediment, such as silt or clay, as well as organic or inorganic matter, such as algae, plankton, and decaying material. On the other hand, water conductivity is important because it can tell you how much dissolved substances, chemicals, and minerals are present in the water. Higher amounts of these impurities will lead to higher conductivity. These substances are toxic to aquatic life and humans, especially when present in high concentrations. O'Donnell (2019) reported that higher temperatures, in most cases, imply higher electrical conductivity. An increase in water temperature of one degree Celsius causes an increase in electrical conductivity of 2-3%. Furthermore, technology has impacted and changed society in significant ways. One of the products of technology that helped humans in their daily activities are robots. Robotics for Society (2021) stated that robots are used in multiple areas, significantly where they can alleviate strenuous tasks or complete missions that are dangerous for a human to undertake. Apart from being precise and consistent, robots can work in any environment, adding to their flexibility. Meanwhile Arduino is an open-source platform primarily used for building robots or electronics projects. It is small but packed with powerful features, such as IDE (Integrated Development Environment) software for writing and uploading code to the device and a programmable circuit board. It can drive motors, light-emitting diodes, sensors, and other components. On the other hand, water analysis is a method in monitoring water quality which is very expensive. With this issue, the researchers conducted this study to help the people know the water conductivity and turbidity of water by just placing the sensors in the water also the robot is affordable and it can measure simultaneously. This study will determine if the Hydrabot is accurate and precise in water analysis in terms of water conductivity and turbidity also the researchers will determine if there is a significant difference in the water quality of the readings from the water analysis and the Hydrabot in terms of water conductivity and turbidity.
DOI: 10.17632/bj48vtrph7.2
title: HYDRABOT: ARDUINO-BASED ROBOT FOR MEASURING CONDUCTIVITY AND TURBIDITY AS WATER QUALITY PARAMETERS

description: Water is essential to both bodies and the environment since it moves nutrients, waste products, and oxygen to and from cells and organs, supporting various actions and results across the state. Turbidity is the measure of the relative clarity of a liquid. According to Fondriest Environmental Learning Center (2019), turbidity is caused by suspended sediment, such as silt or clay, as well as organic or inorganic matter, such as algae, plankton, and decaying material. On the other hand, water conductivity is important because it can tell you how much dissolved substances, chemicals, and minerals are present in the water. Higher amounts of these impurities will lead to higher conductivity. These substances are toxic to aquatic life and humans, especially when present in high concentrations. O'Donnell (2019) reported that higher temperatures, in most cases, imply higher electrical conductivity. An increase in water temperature of one degree Celsius causes an increase in electrical conductivity of 2-3%. Furthermore, technology has impacted and changed society in significant ways. One of the products of technology that helped humans in their daily activities are robots. Robotics for Society (2021) stated that robots are used in multiple areas, significantly where they can alleviate strenuous tasks or complete missions that are dangerous for a human to undertake. Apart from being precise and consistent, robots can work in any environment, adding to their flexibility. Meanwhile Arduino is an open-source platform primarily used for building robots or electronics projects. It is small but packed with powerful features, such as IDE (Integrated Development Environment) software for writing and uploading code to the device and a programmable circuit board. It can drive motors, light-emitting diodes, sensors, and other components. On the other hand, water analysis is a method in monitoring water quality which is very expensive. With this issue, the researchers conducted this study to help the people know the water conductivity and turbidity of water by just placing the sensors in the water also the robot is affordable and it can measure simultaneously. This study will determine if the Hydrabot is accurate and precise in water analysis in terms of water conductivity and turbidity also the researchers will determine if there is a significant difference in the water quality of the readings from the water analysis and the Hydrabot in terms of water conductivity and turbidity.
DOI: 10.17632/w3y584hsw4.1
title: Non-classical clinical presentation of ICI-BP

description: The pictures of non-classical clinical presentation of Immune checkpoint inhibitor-induced bullous pemphigoid.
DOI: 10.17632/phvmsyz7ps.1
title: Brick wall, sensor, ML thermal confort

description: Enhancing the brick wall into smart wall with sensors and machine learning tools, to predict the indoor thermal comfort
DOI: 10.17632/4s9cyj78mk.1
title: Typical and atypical pathological features of ICI-BP

description: Typical and atypical pathological features of Immune checkpoint inhibitor-induced bullous pemphigoid.
DOI: 10.17632/fhv9n6kjs6.1
title: Comparison of clinical manifestations of classical and non-classical BP

description: Comparison of clinical manifestations of classical and non-classical bullous pemphigoid.
DOI: 10.17632/2b3twfpr37.1
title: Characterizing Immunostimulatory Lipids in Drosophila melanogaster

description: Raw data for the manuscript entitled: Characterizing Immunostimulatory Lipids in Drosophila melanogaster
DOI: 10.17632/pf3r8fbc5x.1
title: Impact of Perceived Organizational and Supervisor Support on Turnover Intention among the Bangladeshi Nurses: Does Work-family Conflict Matter?

description: Impact of Perceived Organizational and Supervisor Support on Turnover Intention among the Bangladeshi Nurses: Does Work-family Conflict Matter?
DOI: 10.17632/x36hhpfm9d.1
title: badminton traffic

description: Badminton traffic of teenager
DOI: 10.17632/grzgwydtg5.1
title: Daily Labor Mobility and the Timing of First Childbearing

description: The data present a supplementary material (SM) to the paper "Daily labor mobility and the timing of entry into motherhood" published in Applied Econometrics. Estimation strategy addresses the problem of potential endogeneity of commuting decision. The main estimation approach is a bivariate probit model developed by Heckman (1978) for a model with a binary choice and an endogenous binary regressor. Two other methods, probit model in assumption of exogeneity of commuting decision, and a instrumental variables approach are employed for robustness check. The direct effects of commuting are estimated as the average treatment effects in the age-specific FBRs. Spillover effects of daily labor mobility on the entry into motherhood are tested through the link between FBRs and total fertility rates, relative earnings, and matching indicators in the marriage market, namely, the sex ratio in the night-time and daytime population, and by the proportion of commuters in the population.
The study uses individual register data from Sweden for women born in 1974 and residing in the Stock-holm area following them from 19 to 32 years old. The study uses data from an open source, Swedish Statistics, scb.se, and Individual register data collected by Statistics Sweden and compiled into the longitudinal database of Sweden on Health Insurance and Labour Market Research (Longitudinell Integrationsdatabas för Sjukförsäkrings och Arbets-marknads studier, LISA). The data obtained through the Demographic Data Base, Umeå University.

The SM contains 4 tables:
Table S1. Marginal Effects at Means for Commuters in the Bivariate Probit Model
Table S2. Marginal Effects at Means for Non-Commuters in the Bivariate Probit Model
Table S3. Estimates in the Linear Probability Model with Endogenous Treatment
Table S4. Marginal Effects at Means in the Probit Model
DOI: 10.17632/fydxshd38j.1
title: Satisfaction with Work in the Public and Private Sector in Russia (2002-2021)

description: The tables serve as illustration to the results in the paper 'Not by Bread alone: Satisfaction with Work in the Public and Private Sector'.
Data contain estimates of public-private gap in job satisfaction in Russia over the period 2002-2021. Panel data fixed effects model is employed on the sample of 18 thou. individual and subsamples by gender, educational level (primary, secondary, higher), age groups (17-34, 35-44, 45-54, 55-69 years old) and city size (below 100 thou. people, 100-250, 250-500, over 500 thou. people). The data set is received from the Russia Longitudinal Monitoring Survey - Higher School of Economics (RLMS-HSE) (https://www.hse.ru/en/rlms/). The estimates are in App. 3, Tables 1-14. Tables 1 - 3 of App. 2 present a descriptive statistics on public-private differences in working conditions and human capital characteristics. App. 1 presents an example of transformation applied to the ordered data to use a linear regression analysis. All materials are in Russian.