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This paper considers the difference-in-differences (DID) method when the data come from repeated cross-sections and the treatment status is observed either before or after the implementation of a program. We propose a new method that point-identifies the average treatment effect on the treated (ATT) via a DID method when there is at least one proxy variable for the latent treatment. Key assumptions are the stationarity of the propensity score conditional on the proxy and an exclusion restriction that the proxy must satisfy with respect to the change in average outcomes over time conditional on the true treatment status. We propose a generalized method of moments estimator for the ATT and we show that the associated overidentification test can be used to test our key assumptions. The method is used to evaluate JUNTOS, a Peruvian conditional cash transfer program. We find that the program significantly increased the demand for health inputs among children and women of reproductive age.

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...

A comma separated values (csv) file that is a snapshot of percent difference between November 19, 2008 and November 14, 2016 peak streamflow. The file lists station identification, water year, original (2008) peak Q, current (2016) peak Q and percent difference calculated per water year. The percent difference was calculated as the absolute value of [(current peak Q - original peak Q)/(original peak Q) x 100], where current peak Q is the 2016 peak and the original peak Q is the 2008 peak. When an original peak Q value is 0, the resultant percent difference calculation is undefined because of division by 0. In these cases, the percent difference field is populated with NA. Those entries are included in the data file so that users can make their own comparisons between the 2008 and 2016 peaks for those cases where the original peak value was 0.

Decentralized finance (DeFi) is known for its unique mechanism design, which applies smart contracts to facilitate peer-to-peer transactions. The decentralized bank is a typical DeFi application. Ideally, a decentralized bank should be decentralized in the transaction. However, many recent studies have found that decentralized banks have not achieved a significant degree of decentralization. This research conducts a comparative study among mainstream decentralized banks. We apply core-periphery network features analysis using the transaction data from four decentralized banks, Liquity, Aave, MakerDao, and Compound. We extract six features and compare the banks' levels of decentralization cross-sectionally. According to the analysis results, we find that: 1) MakerDao and Compound are more decentralized in the transactions than Aave and Liquity. 2) Although decentralized banking transactions are supposed to be decentralized, the data show that four banks have primary external transaction core addresses such as Huobi, Coinbase, and Binance, etc. We also discuss four design features that might affect network decentralization. Our research contributes to the literature at the interface of decentralized finance, financial technology (Fintech), and social network analysis and inspires future protocol designs to live up to the promise of decentralized finance for a truly peer-to-peer transaction network. (2023-07-06)

Recent work on the electoral effects of gun violence in the United States relying on the difference-in-differences design has produced conflicting findings that range from null effects to substantively large effects. At the same time, as the difference-in-difference design on which this research has relied has exploded in popularity, scholars have documented several methodological issues this design faces---including potential violations of parallel-trends and unaccounted for treatment effect heterogeneity. Sadly, these pitfalls (and their solutions) have not been fully explored in political science. In this paper, we apply these advancements to the unresolved debate on the effects of gun violence on electoral outcomes in the United States. We show that studies that find a large positive effect of gun violence on Democratic vote share are a product of a failure to properly specify difference-in-differences models when underlying assumptions are unlikely to hold. Once these biases are corrected, shootings show little evidence of sparking large electoral change. Our work clarifies an important unresolved debate and provides a cautionary guide for the many scholars currently employing difference-in-differences designs.

MD simulation data related to the publication Rashidian et al.: Discrepancy in interactions and conformational dynamics of pregnane X receptor (PXR) bound to an agonist and a novel competitive antagonist. https://doi.org/10.1016/j.csbj.2022.06.020

Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files)

dataset I: systems SRL+Co, C-100 and BAY-1797

dataset I: each file contains all branched replicas and the main replica.

dataset1: C_100_Replica1 contains four branched replicas and the main replica. The two of four branched replicas which stem from the middle of the main replica named: b_c_D1_r1_2285 (corresponding name in the SI data is R1_a) and b_c_D1_2285_r1_2 (corresponding name in the SI data is R1_b).

dataset1: C_100_Replica2–5 , each file contains one main replica and the two branches.

dataset I: system SRL+Co ;each file contains one main replica

dataset I: system BAY-1797; each file contains one main replica

dataset II:system SRL ; each file contains one main replica.

Dataset

This dataset was created by Eugenio Millan S

Contents

Land, building, and total assessed values for all Cook County parcels, from 1999 to present. The Assessor's Office uses these values for reporting, evaluating assessment performance over time, and research. When working with Parcel Index Numbers (PINs) make sure to zero-pad them to 14 digits. Some datasets may lose leading zeros for PINs when downloaded. This data is parcel-level. Each row contains the assessed values for a single PIN for a single year. Important notes:Assessed values are available in three stages: 1) mailed, these are the initial values estimated by the Assessor's Office and mailed to taxpayers. 2) certified, these are values after the Assessor's Office closes appeals. 3) Board of Review certified, these are values after the Board of Review closes appeals. The values in this data are assessed values, NOT market values. Assessed values must be adjusted by their level of assessment to arrive at market value. Note that levels of assessment have changed throughout the time period covered by this data set. This data set will be updated roughly contemporaneously (monthly) with the Assessor's website as values are mailed and certified. However, note that there may be small discrepancies between the Assessor's site and this data set, as each pulls from a slightly different system. If you find a discrepancy, please email the Data Department using the contact link below. This dataset contains data for the current tax year, which may not yet be complete or final. Assessed values for any given year are subject to change until review and certification of values by the Cook County Board of Review, though there are a few rare circumstances where values may change for the current or past years after that. Rowcount for a given year is final once the Assessor has certified the assessment roll all townships. Current property class codes, their levels of assessment, and descriptions can be found on the Assessor's website. Note that class codes details can change across time.For more information on the sourcing of attached data and the preparation of this dataset, see the Assessor's Standard Operating Procedures for Open Data on GitHub. Read about the Assessor's 2025 Open Data Refresh.

This repository contains the code and data for reproducibility of the paper 'Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms'. The following files are included: - TA.zip and DEM.zip: The code used for the TA and DEM algorithms respectively. - experiment_runner: Python file which was used to run the black-box optimization algorithms on the discrepancy problems from IOHexperimenter (requires package 'ioh', version 0.3.6 or higher). This generates data in IOH-format, which is included in 'raw_data.zip' - process_stardicr.R: R script which uses IOHanalyzer to extract the performance from the raw data into csv files for visualization. The resulting csvs are included in 'csv_with_pos' for the final results including the corresponding coordinates and 'csv_perf.zip', which contains the convergence information. - Found_Values: The discrepancy values found by TA and DEM, separated by sampler. - A csv file of the relative performance of each of the optimizers compared to the values found by TA is included in 'final_precision_table.csv' - Plot_StarDiscr: the python notebook used to generate all figures, except figure 3 which was created using the IOHanalyzer GUI (iohanalyzer.liacs.nl). The full dataset is available on the website under the source 'star_discrepancy' - Figures: some additional figures which were not included in the paper because of space constraints + higher quality versions of some of the landscape plots.

Tran Viet Khoa, Do Hai Son, Dinh Thai Hoang, Nguyen Linh Trung, Tran Thi Thuy Quynh, Diep N. Nguyen, Nguyen Viet Ha, and Eryk Dutkiewicz, "Collaborative Learning for Cyberattack Detection in Blockchain Networks," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 7, pp. 3920-3933, Apr. 2024.

Land, building, and total assessed values, pre and post-appeal with the Cook County Assessor’s office, for all Cook County parcels, from 1999 to present. The Assessor's Office uses these values for reporting, evaluating assessment performance over time, and research. When working with Parcel Identification Numbers (PINs) make sure to zero-pad them to 14 digits. Some datasets may lose leading zeros for PINs when downloaded. This data is parcel-level. Each row contains the assessed values for a single PIN for a single year pre and post-appeal. Important notes:This dataset includes appeal cases that are currently open. Data for these cases is not final and is subject to change. Each row includes two stages: 1) mailed, these are the initial assessed values (AVs) estimated by the Assessor's Office and mailed to taxpayers. The columns mailed_bldg, mailed_land, and mailed_tot are the AVs for the building, land, and total, at the mailed stage. 2) certified, these are values after the Assessor's Office closes appeals. The columns certified_bldg, certified_land, and certified_tot are the AVs for the building, land, and total, after the Assessor has completed appeal decisions. Values in this dataset are not final assessed values for a given year as they are still subject to change if a taxpayer appeals to the Cook County Board of Review. At present, this dataset does not contain appeal decisions from this final third stage. However, the final stage AVs and appeal decisions can be downloaded from the Board of Review Appeal Decision History dataset. Due to the current transition from the county's legacy system to a modern system of record, appeal data is sparse prior to 2021. Values and change/no change decisions are available, but the reason, agent, and type fields will only be complete once the new system has been successfully batch updated with complete historical data. The values in this data are assessed values, NOT market values. Assessed values must be adjusted by their level of assessment to arrive at market value. Note that levels of assessment have changed throughout the time period covered by this data set. This data set will be updated monthly regardless of the Assessor's mailing and certification schedule. There may be small discrepancies between the Assessor's site and this data set, as each pulls from a slightly different system. If you find a discrepancy, please email the Data Department using the contact link below. This dataset contains data for the current tax year, which may not yet be complete or final. Assessed values for any given year are subject to change until review and certification of values by the Cook County Board of Review, though there are a few rare circumstances where values may change for the current or past years after that. Rowcount for a given year is final once the Assessor has certified the assessment roll all townships. Current property class codes, their levels of assessment, and descriptions can be found on the Assessor's website. Note that class codes details can change across time. For more information on the preparation of this dataset, see the Assessor's Standard Operating Procedures for Open Data on GitHub. <a href="https://datacatalog.cookcountyil.gov/stor

Dataset Card for "MedQuAD"

This dataset is the converted version of MedQuAD. Some notes about the data:

Multiple values in the umls_cui, umls_semantic_types, synonyms columns are separated by | character. Answers for [GARD, MPlusHerbsSupplements, ADAM, MPlusDrugs] sources (31,034 records) are removed from the original dataset to respect the MedlinePlus copyright. UMLS (umls): Unified Medical Language System CUI (cui): Concept Unique Identifier

  Question type discrepancies… See the full description on the dataset page: https://huggingface.co/datasets/lavita/MedQuAD.

2574 (2494) materials used for training regressors that predict shear and bulk modulus. The xlsx file provided consists of the original data used to train models described in reference 1 below. The json.gz file includes structural and composition based data from the Materials Project as well as mpid values. Several entries have been marked suspect in this file as they could not be properly cross referenced on the Materials Project database. An additional goup of materials have been marked suspect due to large discrepancies in shear and bulk modulus from the source file and current MP values.Data is available as Monty Encoder encoded JSON and as a XLSX file. Recommended access method is with the matminer Python package using the datasets module.Note on citations: If you found this dataset useful and would like to cite it in your work, please be sure to cite its original sources below rather than or in addition to this page.Dataset discussed in:Machine Learning Directed Search for Ultraincompressible, Superhard Materials Aria Mansouri Tehrani, Anton O. Oliynyk, Marcus Parry, Zeshan Rizvi, Samantha Couper, Feng Lin, Lowell Miyagi, Taylor D. Sparks, and Jakoah Brgoch Journal of the American Chemical Society

                2018
                140
                (31),
                9844-9853



          DOI: 10.1021/jacs.8b02717

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This metadata document describes the data file 'model_recalibration_results.csv' for the recalibration of the Sinking Pond hydrologic model. The hydrologic model was recalibrated using the R script ‘annotated_R_script_for_model_recalibration.R’ and the input dataset ‘Daily_values_for_climate_stage_and_model_terms.csv’ in this data release to produce recalibrated values for the four calibrated parameters in the model: KBR, KPR, KBG, and PG (see the ‘Entity and Attribute’ section of this metadata document; for additional information see table 9 in Wolfe and others, 2004). The data file ‘model_recalibration_results.csv’ contains the results from the recalibration process. Each row in the dataset represents a different model, where each model is defined by a unique combination of values for KBR, KPR, KBG, and PG. All possible combinations of KBR (15 parameter values), KPR (13 parameter values), KBG (20 parameter values), and PG (21 parameter values) were run as separate models, to yield a total of 81,900 models (i.e., 81,900 rows in this dataset). Each model produced a time series of modeled pond stage which was compared to observed pond stage on a daily time step across 22 water years to calculate four model performance statistics: (1) root mean-squared error (RMSE), (2) Nash-Sutcliffe Efficiency (NSE), (3) the 25th percentile of NSE values across water years (NSE_p25), and (4) hydroperiod discrepancy (HPD); see the ‘Entity and Attribute’ section of this metadata document for additional information about model performance metrics.

Published in Turner, K. G., Ostevik, K. L., Grassa, C. J., & Rieseberg, L. H. 2020. Genomic analyses of phenotypic differences between native and invasive populations of diffuse knapweed (Centaurea diffusa). Frontiers in Ecology and Evolution (in press), 8. doi: 10.3389/fevo.2020.577635

Draft reference genome assembly

Seed was collected from an individual in the native range of C. diffusa (TR001-1; Table S1). Seed from this collection was grown in a greenhouse at the University of British Columbia in 2009. Young leaf tissue was sampled from one progeny (TR001-1L) and stored at -80° C to be used for draft reference genome assembly. DNA was extracted from frozen tissue using a modified Qiagen DNeasy column-less protocol (Qiagen, Valencia, CA, USA). Concentration and quality were verified by Nanodrop, Qubit 2.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA), and gel electrophoresis. A whole genome shotgun library for this individual was produced and sequenced using Illumina...

Hong Kong Social Contact Dynamics

Understanding Age, Gender and Network Dynamics

By [source]

About this dataset

This dataset provides an in-depth look at the dynamics of social interaction, particularly in Hong Kong. It contains comprehensive information regarding individuals, households and interactions between individuals such as their ages, frequency and duration of contact, and genders. This data can be utilized to evaluate various social and economic trends, behaviors, as well as dynamics observed at different levels. For example, this data set is an ideal tool to recognize population-level trends such as age and gender diversification of contacts or investigate the structure of social networks in addition to the implications of contact patterns on health and economic outcomes. Additionally, it offers valuable insights into dissimilar groups of people including their permanent residence activities related to work or leisure by enabling one to understand their interactions along with contact dynamics within their respective populations. Ultimately this dataset is key for attaining a comprehensive understanding of social contact dynamics which are fundamental for grasping why these interactions are crucial in Hong Kong's society today

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How to use the dataset

This dataset provides detailed information about the social contact dynamics in Hong Kong. With this dataset, it is possible to gain a comprehensive understanding of the patterns of various forms of social contact - from permanent residence and work contacts to leisure contacts. This guide will provide an overview and guidelines on how to use this dataset for analysis.

Exploring Trends and Dynamics:

To begin exploring the trends and dynamics of social contact in Hong Kong, start by looking at demographic factors such as age, gender, ethnicity, and educational attainment associated with different types of contacts (permanent residence/work/leisure). Consider the frequency and duration of contacts within these segments to identify any potential differences between them. Additionally, look at how these factors interact with each other – observe which segments have higher levels of interaction with each other or if there are any differences between different population groups based on their demographic characteristics. This can be done through visualizations such as line graphs or bar charts which can illustrate trends across timeframes or population demographics more clearly than raw numbers would alone.

Investigating Social Networks:

The data collected through this dataset also allows for investigation into social networks – understanding who connects with who in both real-life interactions as well as through digital channels (if applicable). Focus on analyzing individual or family networks rather than larger groups in order to get a clearer picture without having too much complexity added into the analysis time. Analyze commonalities among individuals within a network even after controlling for certain factors that could affect interaction such as age or gender – utilize clustering techniques for this step if appropriate– then focus on comparing networks between individuals/families overall using graph theory methods such as length distributions (the average number of relationships one has) , degrees (the number of links connected from one individual or family unit), centrality measures(identifying individuals who serve an important role bridging two different parts fo he network) etc., These methods will help provide insights into varying structures between large groups rather than focusing only on small-scale personal connections among friends / colleagues / relatives which may not always offer accurate portrayals due to their naturally limited scope

Modeling Health Implications:

Finally, consider modeling health implications stemming from these observed patterns– particularly implications that may not be captured by simpler measures like count per contact hour (which does not differentiate based on intensity). Take into account aspects like viral transmission risk by analyzing secondary effects generated from contact events captured in the data – things like physical proximity when multiple people meet up together over multiple days

Research Ideas

• Analyzing the age, gender and contact dynamics of different areas within Hong Kong to understand the local population trends and behavior.
• Investigating the structure of social networks to study how patterns of contact vary among socio economic backgro...

This table contains 9 series, with data for years 1961 - 2012 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada);  Category flows (9 items: Gross saving and capital transfers; Discrepancy, income side (income and expenditure accounts); Non-financial investment; Discrepancy, expenditure side (income and expenditure accounts); ...).

Cultural Nuances Dataset V1: Understanding Cross-Cultural Differences with Chain of Thought Reasoning

Description: Dive into the intricate world of cultural differences with the "Cultural Nuances Dataset V1." This open-source resource (MIT licensed) presents a carefully curated collection of question-and-answer pairs designed to train AI models in understanding the subtle yet significant variations in language, behavior, decision-making, and social norms across diverse cultures.… See the full description on the dataset page: https://huggingface.co/datasets/moremilk/CoT-Reasoning_Cultural_Nuances.