This is the online appendix to the working paper "Labor market implications of Taiwan’s accession to the WTO: A dynamic quantitative analysis", available at https://ink.library.smu.edu.sg/soe_research/2613/

This record includes the replication data and code supporting results of a published book chapter Discoverability beyond the library: Search engine optimization (case study) [link to be added]. The case study compares the discoverability of two hosted institutional repository solutions, Digital Commons and Figshare using a randomized controlled experiment. Two randomly selected groups of journal articles were deposited and made open access in institutional repositories hosted on Digital Commons and Figshare respectively. Download count data were collected over 7 months to measure and compare the open access discoverability and search engine visibility of the two platforms. GENERAL INFORMATION This readme file was generated on 2022-07-04 by Dong Danping Author Contact Name: Dong Danping ORCID: 0000-0002-2229-6709 Institution: Singapore Management University Email: danpingzzz@gmail.com Name: Aaron Tay ORCID: 0000-0003-0159-013X Institution: Singapore Management University Email: aarontay@smu.edu.sg Date of data collection: 2021-04-01 to 2021-10-01 SHARING/ACCESS INFORMATION Licenses/restrictions placed on the data: CC-BY-4.0 License Links to publications that cite or use the data: [to be updated] Links to other publicly accessible locations of the data: https://github.com/dpdong19/IR-compare https://doi.org/10.25440/smu.19121768 Recommended citation for this dataset: Dong, D., & Tay, A. (2022). Data and code to compare the discoverability of Digital Commons and Figshare. https://doi.org/10.25440/smu.19121768 DATA & FILE OVERVIEW File List /analysis/2021-11_DataAnalysis_DCvsFig.ipynb This is the Jupyter Notebook containing notes and scripts of statistical analysis for the case study. /analysis/DCvsFigshare-downloads-combined-v1.csv This file contains clean data for analysis containing download stats from Apr to Oct 2021 for both InK(Digital Commons) and RDR(Figshare).

Relationship between files: The Jupyter Notebook and data file should be placed in the same folder for the code to run. Data Dictionary DCvsFigshare-downloads-combined-v1.csv Number of variables: 15 Number of cases/rows: 92 Variable List Identifier: unique ID for each record. Also the URL to access the record. (Note: Figshare records have been unpublished after the study thus no longer accessible) IR: Name of the IR. InK is on Digital Commons and RDR is on Figshare. Title: title of the deposited journal article Column D-J: monthly download count excluding bots downloads from April to October 2021. Total: sum of column D-J, total download count during the study period AugToOct: sum of column H-J from Aug to Oct 2021 GS_avail: whether the record can be found in Google Scholar. uniq_PDF: whether the record provides the only PDF in Google Scholar primary: whether the record is displayed as the primary record in Google Scholar.

Missing data codes: blank METHODOLOGICAL INFORMATION Methods are described in 2021-11_DataAnalysis_DCvsFig.ipynb and published book chapter [link to be added]

This dataset comprises a set of information cascades generated by Singapore Twitter users. Here a cascade is defined as a set of tweets about the same topic. This dataset was collected via the Twitter REST and streaming APIs in the following way. Starting from popular seed users (i.e., users having many followers), we crawled their follow, retweet, and user mention links. We then added those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. With this, we have a total of 184,794 Twitter user accounts. Then tweets are crawled from these users from 1 April to 31 August 2012. In all, we got 32,479,134 tweets. To identify cascades, we extracted all the URL links and hashtags from the above tweets. And these URL links and hashtags are considered as the identities of cascades. In other words, all the tweets which contain the same URL link (or the same hashtag) represent a cascade. Mathematically, a cascade is represented as a set of user-timestamp pairs. Figure 1 provides an example, i.e. cascade C = {< u1, t1 >, < u2, t2 >, < u1, t3 >, < u3, t4 >, < u4, t5 >}. For evaluation, the dataset was split into two parts: four months data for training and the last one month data for testing. Table 1summarizes the basic (count) statistics of the dataset. Each line in each file represents a cascade. The first term in each line is a hashtag or URL, the second term is a list of user-timestamp pairs. Due to privacy concerns, all user identities are anonymized.

Interview notes and transcripts from fieldwork conducted in China in 2015, 2016, and 2018.

These files are related to the published paper "Why do farmers' cooperatives fail in a market economy? Rediscovering Chayanov with the Chinese experience".

This dataset comprises a set of Twitter accounts in Singapore that are used for social bot profiling research conducted by the Living Analytics Research Centre (LARC) at Singapore Management University (SMU). Here a bot is defined as a Twitter account that generates contents and/or interacts with other users automatically (at least according to human judgment). In this research, Twitter bots have been categorized into three major types:

Broadcast bot. This bot aims at disseminating information to general audience by providing, e.g., benign links to news, blogs or sites. Such bot is often managed by an organization or a group of people (e.g., bloggers). Consumption bot. The main purpose of this bot is to aggregate contents from various sources and/or provide update services (e.g., horoscope reading, weather update) for personal consumption or use. Spam bot. This type of bots posts malicious contents (e.g., to trick people by hijacking certain account or redirecting them to malicious sites), or promotes harmless but invalid/irrelevant contents aggressively.

This categorization is general enough to cater for new, emerging types of bot (e.g., chatbots can be viewed as a special type of broadcast bots). The dataset was collected from 1 January to 30 April 2014 via the Twitter REST and streaming APIs. Starting from popular seed users (i.e., users having many followers), their follow, retweet, and user mention links were crawled. The data collection proceeds by adding those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. Using this procedure, a total of 159,724 accounts have been collected. To identify bots, the first step is to check active accounts who tweeted at least 15 times within the month of April 2014. These accounts were then manually checked and labelled, of which 589 bots were found. As many more human users are expected in the Twitter population, the remaining accounts were randomly sampled and manually checked. With this, 1,024 human accounts were identified. In total, this results in 1,613 labelled accounts. Related Publication: R. J. Oentaryo, A. Murdopo, P. K. Prasetyo, and E.-P. Lim. (2016). On profiling bots in social media. Proceedings of the International Conference on Social Informatics (SocInfo’16), 92-109. Bellevue, WA. https://doi.org/10.1007/978-3-319-47880-7_6

This record contains the underlying research data for the publication "Extended Comprehensive Study of Association Measures for Fault Localization" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/1818Spectrum-based fault localization is a promising approach to automatically locate root causes of failures quickly. Two well-known spectrum-based fault localization techniques, Tarantula and Ochiai, measure how likely a program element is a root cause of failures based on profiles of correct and failed program executions. These techniques are conceptually similar to association measures that have been proposed in statistics, data mining, and have been utilized to quantify the relationship strength between two variables of interest (e.g., the use of a medicine and the cure rate of a disease). In this paper, we view fault localization as a measurement of the relationship strength between the execution of program elements and program failures. We investigate the effectiveness of 40 association measures from the literature on locating bugs. Our empirical evaluations involve single-bug and multiple-bug programs. We find there is no best single measure for all cases. Klosgen and Ochiai outperform other measures for localizing single-bug programs. Although localizing multiple-bug programs, Added Value could localize the bugs with on average smallest percentage of inspected code, whereas a number of other measures have similar performance. The accuracies of the measures in localizing multi-bug programs are lower than single-bug programs, which provokes future research.

This record contains the underlying research data for the publication "Worker selection, hiring, and vacancies" and the full-text is available from: https://ink.library.smu.edu.sg/soe_research/1990The ratio of hirings to vacancies in the U.S. has the following establishment level properties: (i) it steeply rises with employment growth rate; (ii) falls with establishment size; and (iii) rises with worker turnover rate. The standard Diamond-Mortensen Pissarides (DMP) matching model is not compatible with these observations. This paper augments selection of workers prior to hiring into a random matching model with multi-worker firms. In the calibrated model, worker selection accounts for about 30% of the variation in the hiring-vacancy ratio observed in the data. Compared to the standard model, the worker selection model has both qualitative and quantitative policy implications. A hiring subsidy reduces the unemployment rate substantially in the worker selection model, whereas the reduction in the unemployment rate is very small in the standard model. The two models also differ regarding the impact of the hiring subsidy across firms. The worker selection model implies that firms that have initially high worker turnover rates experience proportionally higher worker turnover rates after the subsidy. In contrast, the standard model predicts that the worker turnover rate increases proportionally more at firms with initially lower worker turnover rates.

The replication data for "The Intergenerational Mortality Tradeoff of COVID-19 Lockdown Policies"

The data that support the findings of this study are available from the corresponding author upon reasonable request and approval of the HR consultancy firm the data were obtained from. The Mplus code for the CFA and multilevel analyses is available at: https://osf.io/6f47s/ This study draws from brand positioning research to introduce the notions of points-of-relevance and points-of-difference to employer image research. Similar to prior research, this means that we start by investigating the relevant image attributes (points-of-relevance) that potential applicants use for judging organizations' attractiveness as an employer. However, we go beyond past research by examining whether the same points-of-relevance are used within and across industries. Next, we further extend current research by identifying which of the relevant image attributes also serve as points-of-difference for distinguishing between organizations and industries. The sample consisted of 24 organizations from 6 industries (total N = 7171). As a first key result, across industries and organizations, individuals attached similar importance to the same instrumental (job content, working conditions, and compensation) and symbolic (innovativeness, gentleness, and competence) image attributes in judging organizational attractiveness. Second, organizations and industries varied significantly on both instrumental and symbolic image attributes, with job content and innovativeness emerging as the strongest points-of-difference. Third, most image attributes showed greater variation between industries than between organizations, pointing at the importance of studying employer image at the industry level. Implications for recruitment research, employer branding, and best employer competitions are discussed.

This is the online supplement to the working paper 'A panel clustering approach to analyzing bubble behavior', available at https://ink.library.smu.edu.sg/soe_research/2591/This online supplement has six sections. Section A collects together technical lemmas that are used for membership estimation in the first stage. Section B collects the lemmas needed for post-clustering panel estimation and the bubble detection methods, specifically the post-clustering panel t- and J-tests. Section C collects results and proofs for selecting the number of groups. Section D extends the two-stage algorithm and the corresponding post-clustering statistics to the mixed-root panel autoregressive model with purely stationary, unit, and purely explosive roots. Section E overviews experimental designs and reports simulation findings. Section F contains tables.

Related Publication: Marcinkowska U.M., Kozlov M.V., Cai H., Contreras-Garduño J., Dixson B.J., Oana G.A., Kaminski G., Li N.P., Lyons M.T., Onyishi I.E., Prasai K., Pazhoohi F., Prokop P., Rosales Cardozo S.L., Sydney N., Yong J.C., Rantala M.J. (2014). Cross-cultural variation in men’s preference for sexual dimorphism in women’s faces. Biology Letters 10(4): 20130850. Available at: https://doi.org/10.1098/rsbl.2013.0850 Available in InK: http://ink.library.smu.edu.sg/soss_research/1615/

Research on meaning has begun to assess the specific facets of meaning in life. Few studies have examined the extent to which these facets distinguish meaning at the level of individual events. In the present study, participants from Singapore and the U.S. wrote about meaningful and meaningless events and rated the extent to which they experienced purpose, coherence, positive and negative implications for self and others, positive affect, and negative affect. In both samples, meaningful and meaningless events differed most in their levels of positive affect, purpose, and positive implications for the self. When entered as predictors of overall event meaningfulness, purpose and positive affect independently predicted meaning. Measures of coherence did not predict the meaningfulness of event with one exception. The extent to which an event offered a new understanding predicted meaning above and beyond purpose and PA. Implications for meaning assessment and theories of meaning are discussed.

These files are used to replicate all analyses in 'Media in a Time of Crisis' published in Journalism Studies (2021).

This e-companion contains four sets of supporting materials for the main paper. EC.1 provides algorithmic treatments to handle key market implementation issues. EC.2 examines effects of active market intermediation on market performance and the dealer’s wealth under the controlled market experiment. EC.3 studies market liquidity and heterogeneous market participation in a randomized market environment. EC.4 includes proofs of Lemmas and Corollaries.

See the readme file inside for replication steps

This is the accompanying data for the journal article Values assessment for personnel selection: Comparing job applicants to non-applicants, forthcoming in the journal European Journal of Work and Organizational Psychology. It includes the data as well as the scripts for analysis in R.Data Description for Exported Data The following file provides some details for the data supplied on the OSF.DatasetsThere are three main data files- rcases: rcases.rdataThis stands for raw cases. This file is contained in the R repository. It includes several cases that were excluded from the final analysis. It does not include derived variables such as scale scores.- ccases: ccases.csv ccases.rdataThis stands for cleaned cases. As described in the R script and in the method, a few cases were removed because of concerns about data quality. These cases are excluded from ccases. Ccases also includes derived variables such as scale scores.- data/meta.rdata: This file includes meta data used for scoring the values measure. The general principle is that one row is one item. It indicates which broad and narrow values the item belongs to, whether it should be reversed and so on. - crep.rdataData from repeated measures sample.Variables in rcases/ccases1. DemographicsFor privacy purposes, the raw data shared on the repository has been slightly modified. Age was rounded to the nearest 10 (i.e., 20, 30, 40). All other demographic data besides age and gender is excluded.

Wearables or infrastructure sensors have been widely proposed for automated tracking and analysis of individual-level exercise activities. This dataset is collected as part of building a pervasive, low-cost digital personal trainer system, that supports fine-grained tracking of an individual’s free-weights exercises via a combination of (a) sensors on personal wireless ear-worn devices (‘earables’) and (b) inexpensive IoT sensors attached to exercise equipment (e.g., dumbbells). The dataset is comprised of sensor signals acquired from two 6-axis IMUs and contains a total of 324 samples for 3 different free-weight exercises performed by 27 individuals.

Fieldwork conducted in August 2023 in Shandong Province, China, investigating forms of agricultural production in several sectors.

Fieldwork sites: 1. Rongcheng City, Weihai 2. Qixia, Yantai 3. Changyi, Weifang 4. Shouguang, Weifang

This is the supplementary data for the article "Estimating stranded coal assets in China's power sector" published in Utilities Policy. China has suffered overcapacity in coal power since 2016. With growing electricity demand and an economic crisis due to the Covid-19 pandemic, China faces a dilemma between easing restrictive policies for short-term growth in coal-fired power production and keeping restrictions in place for long-term sustainability. In this paper, we measure the risks faced by China's coal power units to become stranded in the next decade and estimate the associated economic costs for different shareholders. By implementing restrictive policies on coal power expansion, China can avoid 90% of stranded coal assets by 2025.