Cloud-based data repository for storing, publishing and accessing scientific data. Mendeley Data creates a permanent location and issues Force 11 compliant citations for uploaded data.

Data post-comment pairs were collected from 13 selected Indonesian public figures (artists) / public accounts with more than 15 million followers and categorized as famous artists. It was collected from Instagram using an online tool and Selenium. Two persons labeled all pair data as an expert in a total of 72874 data. The data contains Unicode text (UTF-8) and emojis scrapped in posts and comments without account profile information.

It contains several fields: -igid: Account ID, -comment: Comment of a post, -post: Post from an ID, -emoji: Whether the data contains emojis or not (1 or 0), -spam: Whether the data is spam or not (1 or 0), -lengthcomment: The character length of the comment, -lengthpost: The character length of the post, -countemojicomment: Number of emoji symbol characters in comments, -countemojicommentuniq: Number of emoji symbol characters in comments (unique), -countemojipost: Number of emoji symbol characters in posts, -countemojipostuniq: Number of emoji symbol characters in the post (unique)

This dataset includes specific information on 3,000 COVID-19 recovery patients from Iraq's Kurdistan Region. The data includes 46 features, 15 of which were rigorously vetted by qualified COVID-19 clinicians and the remaining 31 by committed researchers. The dataset provides a comprehensive picture of the patients' health, recovery progress, and a variety of demographic and clinical characteristics. The use of expert-collected data assures a high level of precision and dependability when analyzing the patients' conditions. Researchers and healthcare professionals can use this comprehensive dataset to gain valuable insights into the recovery patterns of COVID-19 patients in the Kurdistan region, contributing to a better understanding of the virus and enhancing the development of targeted interventions and treatment plans. --Demographic 1.Age 2.Height 3.Gender 4.Blood group 5.Weight 6.Address

--Past medical history 7.Smoking 8.Blood Pressure 9.Past Surgical 10.Diabetes 11.Sensitivity 12.Tuberculosis (T.B) 13.Asthma 14.Hypertension

--Diagnosis 15.Vaccine type 16.Vaccination 17.Expose start date
18.Expose End date 19.Investigation
20.Chest X-ray 21.Red Blood Cells (RBC) 22.Complete blood count (CBC) 23.Polymerase Chain Reaction (PCR) 24.C-reactive protein (CRP)

--Symptoms during COVID 25.Anxiety 26.Cough 27.Sore throat 28.Fever 29.Joint pains 30.Losing taste or smell 31.Headache

--Present illness 6 months after covid 32.Loss of interest 33.Cough 34. Dyspnea 35.Low Mood 36.Chest Pain 37.Depression 38.Short term Memory Loss 39.Disturb sleep 40.Fatigue

--Clinical parameter 41.Hospitalized 42.LV fluid 43. Blood Oxygen Level (SPO2)
44.Dates of emergency treatment 45.Medicine 46.NO-OF-TESTS

The uploaded files include the results of the research presented in this paper.

Online materials including analysis scripts, descriptive data, and resulting topographies Sperber, Rennig & Karnath: Neural correlates of imaging biomarkers for post-stroke primary motor deficits see the readme.text for detailed information The data archive can be extracted e.g. with 7Zip Freeware

Microbial monitoring of marine post-post smolt RAS by 16S amplicon analysis

Samples from biofilter biofilm, production water, tank wall and fish skin. Sampling time notation C for cycle and W for week in cycle

Appendix: data sorted by relative abundance and origin of taxa

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

Data was gathered from 30 patients from March to July 2020 who reported intense hair shedding following a positive COVID-19 diagnosis, as denoted by positive PCR. TE initiated a median of 45 days (IQR=13) after RT-PCR positive test. Among patients that had TE resolved by July 31st (n=20), resolution occurred in a median of 33 days after onset. Among patients with ongoing TE by July 31st (n=10), median duration was at least 35 days, range 30-81 days. In sum, "COVID effluvium" appears to occur sooner and have a shorter duration than TE associated with other triggering events.

40Ar-39Ar age of plagioclase samples.

Principal components analysis for metrics data (n = 33,683).

This data includes the entire code-base for the examples specified in the associated article published in "Computers and Structures". The reader can refer to the "README" file for the installation instructions.

Effectiveness of press needle treatment and electroacupuncture in patients with postherpetic neuralgia: a matched propensity score analysis

The data uploaded describes SNP data.

The PyProcar Python package plots the band structure and the Fermi surface as a function of site and/or s,p,d,f - projected wavefunctions obtained for each k-point in the Brillouin zone and band in an electronic structure calculation. This can be performed on top of any electronic structure code, as long as the band and projection information is written in the PROCAR format, as done by the VASP and ABINIT codes. PyProcar can be easily modified to read other formats as well. This package is particularly suitable for understanding atomic effects into the band structure, Fermi surface, spin texture, etc. PyProcar can be conveniently used in a command line mode, where each one of the parameters define a plot property. In the case of Fermi-surfaces, the package is able to plot the surface with colors depending on other properties such as the electron velocity or spin projection. The mesh used to calculate the property does not need to be the same as the one used to obtain the Fermi surface. A file with a specific property evaluated for each k-point in a k-mesh and for each band can be used to project other properties such as electron–phonon mean path, Fermi velocity, electron effective mass, etc. Another existing feature refers to the band unfolding of supercell calculations into predefined unit cells.

After perceiving cognitive conflicts or errors, children as well as adults adjust their performance in terms of reaction time slowing on subsequent actions, resulting in the so called post-conflict slowing and post-error slowing, respectively. The development of these phenomena has been studied separately and with different methods yielding inconsistent findings. We aimed to assess the temporal dynamics of these two slowing phenomena within a single behavioral task. To do so, 9-13-year-old children and young adults performed a Simon task in which every fifth trial was incongruent and thus induced cognitive conflict and, frequently, also errors. We compared the reaction times on four trials following a conflict or an error. Both age groups slowed down after conflicts and did so even more strongly after errors. Disproportionally high reaction times on the first post-error trial were followed by a steady flattening of the slowing. Generally, children slowed down more than adults. In addition to highlighting the phenomenal and developmental robustness of post-conflict and post-error slowing these findings strongly suggest increasingly efficient performance adjustment through fine-tuning of cognitive control in the course of development.

Resource Mapping data was collected from field survey and all points such as markets, atms, schools were located and appropriate tags were given.

Data was uploaded on Google sheets and addons of Fusion Mas and point map were installed and addons were run to form virtual maps in their own particular webpages.

Source link of those webpages are determined and were added in a iframe in src link.

In web html design a table was made and all three iframe are added in table.

The final html was added as html element in sites.google.com to create a custom website.

The website link: www.sites.google.com/site/pranavrsmap

Webpage and Sheets are the most important data here. Other data are optional and are uploaded for your Geospatial Location research

The data set contains 600 healthy, 1100 damaged, and 395 different diseased (wilt, Anthracnose, canker, rot) guava fruit ( mature, half-mature, and mature) digital images with a 3000x3000 resolution. In addition, a total of 1821 raw thermal images in the same categories with a resolution of 1440 x 1080 were added. The images are uploaded day-wise, maturity-wise, and drop height-wise.

It is well known that aged fragrance is the most outstanding quality characteristic of dark tea, and determines its market value. However, the formation of aged fragrance during the whole process of dark tea still is not much clear, especially in a large scale. Qingzhuan tea as one of famous Chinese dark teas, is known for its aged fragrance quality, and its production has a typical post-fermentation (piling-fermentation, aging and subsequent processes). In this study, we analyzed the volatile compounds of Qingzhuan tea during the whole post-fermentation process, including sun-dried green tea (P1), piling-fermentation for 3 days (P2), 6 days (P3), 9 days (P4), 12 days (P5), 15 days (P6), 18 days (P7), 21 days (P8), 24 days (P9), and 27 days (P10), and aging for 10 days (A2), 20 days (A4), 30 days (A6), 90 days (A8), 180 days (A11), 270 days (A14), and 360 days (A17); and the samples at before steamed (S1) and after steamed (S2), and the pressed brick tea products (S3). About these results will be better to understand the formation of aged fragrance in dark tea and to optimize the process technology of dark tea. Here, we upload the volatile compounds from thee thirty samples to the data repository to support our article conclusion: Supplementary Excel file 1 Identified volatile compounds in samples during pile-fermentation. Supplementary Excel file 2 Identified volatile compounds in samples during aging and subsequent process. Supplementary Excel file 3 Chemical classification of all volatile components in the present study.

Data Collection Sources, Tools and Techniques The data sources, tools and techniques were as follows;

Data Sources Both secondary and primary data were used. The secondary data from patient files (hospital record) for the past 6months from May, 2018 to November, 2018 were retrieved. These were the records of patients admitted to Maternity and General Surgical wards and who underwent surgery. Only these categories were analysed for both cohorts. The use of secondary data in this study is justified based on generation of new insight and possible longitudinal study among other advantages. It also provides basis for comparison between departments. However, eight (8) respondents (three from maternity ward cohort and five from each general surgical ward cohort), were traced to their homes and primary data on their lived experiences was collected.

Data Collection Tools and Techniques The researcher used Data Abstraction Sheet (Document Review Guide), Observation guide and Interview Guide (for the primary data) tools. Data Abstraction Sheet has been used in a number of studies. This Data Abstraction Sheet used had all the types of data that needed to be extracted from each respondent record written on it. This made it easier for the researcher to obtain the needed data per patient record. The researcher used this Sheet as a guide to extract the appropriate data patient-by-patient. This also served as an observation guide for each patient record /file (See appendix III). Additional observation guide was developed and used by the researcher to observe the hygiene practices in the two hospital departments. This guide had all the observatory areas clearly labelled. Likewise, the Interview guide was used to collect data on lived experiences of the respondents. It guided the researcher in knowing which questions to ask. Data was then hand written as well as video recorded for later analysis. Probing was done to dig deeper into the respondents’ experiences.

The research questionnaire was designed by adaptation measures from previous researchs for Vietnamese context. We conducted the data collection by using Google docs. We upload soft electronic copies of survey questionnaire online. The questionnaires were sent to about 1902 email addresses, which were collected from student alumni of 5 universities in Hanoi –the capital of Vietnam. We received 510 responses (response rate of 26.8%). After screening the questionnaires, bias answers were eliminated. The final sample size consists of 502 responses.