PUBLIC DATA: ERP Differences in Processing Canonical and Noncanonical Finger-Numeral Configurations Citation for the data: Soylu, F. (2019). Public dataset: ERP differences in processing canonical and noncanonical finger-numeral configurations. Harvard Dataverse. https://doi.org/10.7910/DVN/BNNSRG Related bublication: Soylu, F., Rivera, B., Anchan, M., & Shannon, N. (2019). ERP differences in processing canonical and noncanonical finger-numeral configurations. Neuroscience Letters, 705, 74–79. https://doi.org/10.1016/j.neulet.2019.04.032 Keywords: Numerical cognition, Finger counting, Montring, Gestures, EEG, ERP Access to dataset (Harvard Dataverse): https://doi.org/10.7910/DVN/BNNSRG Created by: Firat Soylu (fsoylu@ua.edu) on 2018-03-11 The data was collected in the ELDEN Lab (http://elden.ua.edu) at The University of Alabama, Tuscaloosa. DESCRIPTION OF DATA The stimuli for the EEG session included 24 pictures of finger-number configurations; 4 finger montring, 4 finger counting, and 4 non-canonical finger configurations, separately for left and right hands, all showing the palm and matching with numerosities from one to four. The non-canonical configurations were based on a previous study comparing montring and non-canonical configurations (Di Luca et al., 2010). The configuration images were shot with a digital camera, and were edited to replace the background with a uniform black and to balance color and luminance. The experiment included a total of 960 trials in 10 blocks, each block including 96 trials, generated by combining four sets of the 24 configurations, each of them randomized separately, which allowed an even distribution of different stimuli across each block while avoiding predictability. In each trial a configuration was presented for 500 ms, followed by a validation step, where a single-digit Arabic numeral was presented. Participants pressed one of the two buttons on the controller using either their left or right index finger to indicate whether the Arabic numeral shown matches the number presented in the preceding configuration. To counterbalance use of response buttons, participants used one of the two (right: match, left:no-match, or, left:match, right:no-match) response button configurations in the first five blocks, and the other one in the remaining five blocks, the order randomly chosen for each subject. The dataset includes data from 38 participants. Please check the related publication for more information about the subjects. The analysis in the paper involves a comparison of participants who start counting on their index and thumb fingers. All subjects started counting on their right hands but differed in terms of which finger they started counting with: % Right-thumb starters (N=20) subject_thumb_starter = {'1163', '1164', '1168', '1182', '1184', '1185', '1221', '1223', '1226', '1230', '1233', '1234', '1235', '1237', '1248', '1255', '1261', '1262', '1279', '1280'}; % Right-index starters (N=18) subject_index_starter = {'1161', '1165', '1169', '1170', '1172', '1174', '1176', '1177', '1178', '1179', '1180', '1181', '1183', '1220', '1222', '1224', '1225', '1227'}; In addition to the grand-average ERPs for the entire sample, the analysis script generates separate grand-average ERPs for thumb-starters and index-starters. The EEG part of the experiment took place in a sound attenuated experiment room. Neurobs Presentation (www.neurobs.com) was used for stimulus presentation and data collection. EEG Data was collected using a BrainVision 32 Channel ActiChamp system (www.brainvision.com), with Easy Cap recording caps using Ag/AgCl electrodes. The 32 electrodes were attached according to the international 10-20 system at the locations Fp1/2, F7/8, F3/4, Fz, FT9/10, FC1/2, FC5/6, T7/8, C3/4, Cz, TP9/10, CP1/2, CP5/6, P7/8, P3/4, Pz, O1/2, Oz and recording-referenced to Cz. BrianVision Recorder was used to record data (electrode impedance<10 kΩ, 0.5-70 Hz, 500 samples/sec). A custom MATLAB script using ERPLAB (http:// erpinfo.org/erplab/) and EEGLAB (http://sccn.ucsd.edu/eeglab) functions were used to analyze data. Inferential statistics was conducted with JASP (https://jasp-stats.org/). A Logitech F310 game controller was used as the input device. HOW TO USE 1) Because the total size of the compressed data is 4.5GB, the compressed file is divided into four parts, each less than 1GB. Download the four parts of the compressed data, "Soylu_2019_DataversePublicData_part_a b, c & d" and put them in the same folder. 2) Open a terminal screen (in MAC & LINUX) and go to the folder where the four compressed files are located. Enter the command: "cat Soylu_2019_DataversePublicData_part_* > Soylu_2019_DataversePublicData.tar.gz" This will create a single compressed file "Soylu_2019_DataversePublicData.tar.gz" 3) To uncompress the combined compressed file enter the command: "tar -zxvf Soylu_2019_DataversePublicData.tar.gz" This will uncompress the folder. The uncompressed folder will have the raw data (already converted from the BrainVision format to EEGLAB), the scripts, and the necessary folders for the scripts to work. SCRIPTS "AnalysisScript.m": Execute the "AnalysisScript.m" script (under the "scripts" folder), which includes all steps of data analysis, to produce the ERP results reported in the related publication. You will need to replace "home_path" variable in the script with the path of the main uncompressed folder for the script to work. You will also need to have EEGLAB & ERPLAB and the stats & signal processing toolboxes installed on your MATLAB installation for the script to work. "BehavioralAnalysis.py": This Python script can be run to generate a text file that aggregates the behavioral data across all participants, which then can be opened in a spreadsheet software for further analysis. This script uses the "elist.txt" files generated for each participant after the "AnalysisScript.m" script is executed. "CombineMeasures.py": This Python script aggregates the ERP measures for the P1, N1, P3 components across all participants. The script uses the measurement text files for each participant generated after the "AnalysisScript.m" is run.

Ukraine NBU Forecast: Public Sector: Balance: % of GDP data was reported at -2.000 % in 2020. This records an increase from the previous number of -2.600 % for 2019. Ukraine NBU Forecast: Public Sector: Balance: % of GDP data is updated yearly, averaging -2.200 % from Dec 2017 (Median) to 2020, with 4 observations. The data reached an all-time high of -1.300 % in 2017 and a record low of -2.600 % in 2019. Ukraine NBU Forecast: Public Sector: Balance: % of GDP data remains active status in CEIC and is reported by National Bank of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.F006: Government Budget: Forecast: National Bank of Ukraine.

AbstractThe dataset provided here contains the efforts of independent data aggregation, quality control, and visualization of the University of Arizona (UofA) COVID-19 testing programs for the 2019 novel Coronavirus pandemic. The dataset is provided in the form of machine-readable tables in comma-separated value (.csv) and Microsoft Excel (.xlsx) formats.Additional InformationAs part of the UofA response to the 2019-20 Coronavirus pandemic, testing was conducted on students, staff, and faculty prior to start of the academic year and throughout the school year. These testings were done at the UofA Campus Health Center and through their instance program called "Test All Test Smart" (TATS). These tests identify active cases of SARS-nCoV-2 infections using the reverse transcription polymerase chain reaction (RT-PCR) test and the Antigen test. Because the Antigen test provided more rapid diagnosis, it was greatly used three weeks prior to the start of the Fall semester and throughout the academic year.As these tests were occurring, results were provided on the COVID-19 websites. First, beginning in early March, the Campus Health Alerts website reported the total number of positive cases. Later, numbers were provided for the total number of tests (March 12 and thereafter). According to the website, these numbers were updated daily for positive cases and weekly for total tests. These numbers were reported until early September where they were then included in the reporting for the TATS program.For the TATS program, numbers were provided through the UofA COVID-19 Update website. Initially on August 21, the numbers provided were the total number (July 31 and thereafter) of tests and positive cases. Later (August 25), additional information was provided where both PCR and Antigen testings were available. Here, the daily numbers were also included. On September 3, this website then provided both the Campus Health and TATS data. Here, PCR and Antigen were combined and referred to as "Total", and daily and cumulative numbers were provided.At this time, no official data dashboard was available until September 16, and aside from the information provided on these websites, the full dataset was not made publicly available. As such, the authors of this dataset independently aggregated data from multiple sources. These data were made publicly available through a Google Sheet with graphical illustration provided through the spreadsheet and on social media. The goal of providing the data and illustrations publicly was to provide factual information and to understand the infection rate of SARS-nCoV-2 in the UofA community.Because of differences in reported data between Campus Health and the TATS program, the dataset provides Campus Health numbers on September 3 and thereafter. TATS numbers are provided beginning on August 14, 2020.Description of Dataset ContentThe following terms are used in describing the dataset.1. "Report Date" is the date and time in which the website was updated to reflect the new numbers2. "Test Date" is to the date of testing/sample collection3. "Total" is the combination of Campus Health and TATS numbers4. "Daily" is to the new data associated with the Test Date5. "To Date (07/31--)" provides the cumulative numbers from 07/31 and thereafter6. "Sources" provides the source of information. The number prior to the colon refers to the number of sources. Here, "UACU" refers to the UA COVID-19 Update page, and "UARB" refers to the UA Weekly Re-Entry Briefing. "SS" and "WBM" refers to screenshot (manually acquired) and "Wayback Machine" (see Reference section for links) with initials provided to indicate which author recorded the values. These screenshots are available in the records.zip file.The dataset is distinguished where available by the testing program and the methods of testing. Where data are not available, calculations are made to fill in missing data (e.g., extrapolating backwards on the total number of tests based on daily numbers that are deemed reliable). Where errors are found (by comparing to previous numbers), those are reported on the above Google Sheet with specifics noted.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

Ukraine NBU Forecast: Public Sector: Balance data was reported at -81.000 UAH bn in 2020. This records an increase from the previous number of -98.900 UAH bn for 2019. Ukraine NBU Forecast: Public Sector: Balance data is updated yearly, averaging -81.900 UAH bn from Dec 2017 (Median) to 2020, with 4 observations. The data reached an all-time high of -39.300 UAH bn in 2017 and a record low of -98.900 UAH bn in 2019. Ukraine NBU Forecast: Public Sector: Balance data remains active status in CEIC and is reported by National Bank of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.F006: Government Budget: Forecast: National Bank of Ukraine.