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 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.

An implementation for the paper DeepFacade: A Deep Learning Approach to Facade Parsing with Symmetric Loss.

Look into main.py for how to run the code.

This code was tested in pytorch 0.4.1. For any updates, visit the GitHub repository at https://github.com/liuhantang/DeepFacade

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 "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.

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.

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

See the readme file inside for replication steps

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/

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.

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.

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.

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.

Replication Data for "JUE Insight: Migration, Transportation Infrastructure, and the Spatial Transmission of COVID-19 in China"

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.

A growing literature posits that colonial Christian missions brought schooling to the colonies, improving human capital in ways that persist to this day. But in some places they did much more. This paper argues that colonial Catholic missions in the Philippines functioned as state-builders, establishing law and order and building fiscal and infrastructural capacities in territories they controlled. The mission-as-state was the result of a bargain between the Catholic missions and the Spanish colonial government: missionaries converted the population and engaged in state-building, whereas the colonial government reaped the benefits of state expansion while staying in the capital. Exposure to these Catholic missions-as-state then led to long-run improvements in state capacity and development. I find that municipalities that had a Catholic mission have higher levels of state capacity and development today. A variety of mechanisms---religious competition, education, urbanization, and structural transformation---explain these results.

This record contains the data and code for the published journal article "Global value chains and the CPTPP".

TABLE 2: PARTICIPATION OF CPTPP MEMBERS IN GVC (2005 - 2015)

Run "GVCparticipation.do" For CPTPP countries: the code directly extracts the Foreign Content for VS index (Hummels, Ishii and Yi, 2001), and the GVC index (Borin and Mancini, 2017). For World: the code extracts gross export (GEXP), traditional trade component (DAVAX), and the foreign contents (FC) between each country and their importers. Then sum up the components and calculate the GVC share using equation (2).

TABLE 3: GVC INTENSITY OF CPTPP COMPARED TO OTHER TRADE BLOCS (2015)

Use the decomposition for individual country from the GVC_participation.do to calculate the member's GVC share using equation (2). Sum the GVC share across all the members of the trade bloc.

TABLE 4: KEY DOWNSTREAM TRADE PARTNERS OF CPTPP MEMBERS (2015)

Run "Table4_Downstream_partners.do" The decomposition for Domestic contents (DC) and Traditional trade (TT) of each country with respect to each trade block is saved in the corresponding sheet of "downstream_partners.xlsm" Calculate the percentage trade of each country with regards to its downstream partners using equation (3).

TABLE 5: KEY UPSTREAM TRADE PARTNERS OF CPTPP MEMBERS (2015)

Run "Table5_Upstream_partners.do" The decomposition for Foreign contents (FC) of each country with respect to each trade block is saved in the corresponding sheet of "upstream_partners.xlsm" Calculate the percentage trade of each country with regards to its upstream partners using equation (4).

TABLE 7: PARTICIPATION IN GVC AT SECTORAL LEVEL

Run "Table7_GVCsectors.do"

TABLE 8: KEY DOWNSTREAM PARTNER IN SELECTED SECTORS

Run "Table 8_Downstream_partners_sectors.do" The decomposition for Domestic contents (DC) and Traditional trade (TT) of each country at the sectoral level is saved in "DC_by_sect_manuf" (for manufacturing sectors) and "DC_by_sect_svc" (for service sectors) Calculate the percentage trade of each country to its downstream partners at sectoral level using equation (3).

TABLE 9: KEY UPSTREAM PARTNER IN SELECTED SECTORS

Run "Table 9_Upstream_partners_sectors.do" The decomposition for Foreign contents (FC) of each country at the sectoral level is saved in the corresponding sheet Calculate the percentage trade of each country to its downstream partners using equation (4).

TABLE A.4: Key Downstream Partners by Alternative Formula

To obtain the decomposition for Foreign contents (FC), importer content, and the difference between domestic contents and the traditional trade (DC - TT), run "Table A4_Downstream_partners_apd.do" Calculate the percentage trade of each country with regards to its downstream partners using equation (8)

This record contains the data and codes for the paper "SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing" published in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). RequirementPython 3.7Pytorch 1.9.1Network ArchitectureTrainPlace the training and test image pairs in the data folder.Run data/makedataset.py to generate the NH-Haze20-21-23.h5 file.Run train.py to start training.TestPlace the pre-training weight in the checkpoint folder.Place test hazy images in the input folder.Modify the weight name in the test.py.parser.add_argument("--model_name", type=str, default='Gmodel_40', help='model name')Run test.pyThe results is saved in output folder.Pre-training Weight DownloadThe weight40 Gmodel_40.tar for the NTIRE2023 val/test datasets, i.e., the weight used in the NTIRE2023 challenge.The weight105 Gmodel_105.tar for the NTIRE2020/2021/2023 datasets.The weight120 Gmodel_120.tar for the NTIRE2020/2021/2023 datasets (Add the 15 tested images as the training dataset).