ODISSEI (Open Data Infrastructure for Social Science and Economic Innovations)
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Code and data for 'Age-specific transmission dynamics of SARS-CoV-2 during the first two years of the pandemic'
No full textThis is the main code for the paper "Age-specific transmission dynamics of SARS-CoV-2 during the first two years of the pandemic" by Otilia Boldea, Amir Alipoor, Sen Pei, Jeffrey Shaman, and Ganna Rozhnova and its Supplement.
For questions related to the code, please email Otilia Boldea via the button "Contact Owner" top right or https://sites.google.com/site/otiliaboldea/home
The code is organized as follows:
Main Code/ Code with synthetic train data
synthetic_mobility.m is the code to generate the synthetic mobility Msynth.mat; the true data, used to generate the results in the paper (output.m) is property of the national railway company National Spoorwegen, and is confidential
all the other data is publicly available or posted by us with permission from RIVM (Royal Institute for Public Health and Environment).
The folder Data contains all this data. The code for construction of the data along with plots and Tables can be found in Supplementary Materials Code/Code for S figures and Tables.
The description of how this data is constructed in available in the Supplementary Materials of the above paper
To run the code with synthetic mobility data:
run inference.m, which uses the following functions:
SEIRHS.m - runs the stochastic version of the epidemiology model and an RK4 integration
transf.m - calculates the time-varying transition function of contacts multiplied by the susceptibility parameter
waning - calculates the waning of protection parameters in the second compartment set
initialize - initializes the parameters and the unobserved states
checkbound_ini - checks initial bounds on unobserved states (for example, S cannot be negative or larger than population)
checkbound - checks bounds on unobserved states - after updating (for example, S cannot be negative or larger than population)
Inflation.m - performs variance inflation of posterior updates to prevent the filter from collapsing due to lack of variance
liki.m - calculates the marginal posterior (quasi-)likelihood of the model based on the ensemble adjustment Kalman filter
the dataset movNSperc.mat (generated for Figures S11 and S19) contains percentage change in National train mobility compared to pre-pandemic levels and is used by the function synthetic_mobility.m (note that instead, Google mobility data could be used, but priors on parameters theta would likely need adjustment)
the dataset CBSprov.mat contains in column 4 the number of people living in province i and working in j, and is downloaded from CBS (Central Bureau Statistics)
https://www.cbs.nl/nl-nl/cijfers/detail/83628NED#shortTableDescription
use year 2018, first published in 2020.
Main Code/ Output files
contains output.m, main output in the paper
output_synth.m, output with synthetic train mobility data
Main Code/ Code for Main Text Figures
contains code for reproducing Figures in the main text
Supplementary Materials Code
contains two folders.
The folder "Code for S Figures and Tables" contains code for constructing all the data used in the main code, as well as code for reproducing
the Tables and Figures in the Supplementary Materials of the paper, labeled "S"
The folder "Code for A Figures and Tables" contains code for reproducing the figures and tables in the Supplementary Materials labeled "A"
Main Text Figures
contains all the figures in the main text of the paper
Supplementary Figures
contains all the figures in the Supplement
Universe: Covering the entire Dutch population
Country / Nation: the Netherlands
A Novel Smartphone-Based Intervention Aimed at Increasing Future Orientation via the Future Self: a Pilot Randomized Controlled Trial of a Prototype Application
No full textDataset of an RCT examining the effectiveness of a smartphone-based intervention on future orientation compared to a goal-setting control condition among first-year university students
Network dynamics and its impact on innovation outcomes: R&D consortia in the Dutch water sector
No full textStatutory annual evaluation reports of Technology Foundation STW (Now: NWO Domain Applied and Engineering Sciences). STW provides funding for university-industry R&D projects in the Netherlands. The publicly (online) accessible evaluation reports (appearing 5 and 10 years after each project start) provide information on the Project Leader (University professor), Project Members (industrial firms or service providers), the Project start year, Project end year, Project goal, Outcomes, etc. From these evaluation reports, Alexander Smit and Remco Mannak derived a database with all STW funded projects that started between 1981 and 2004. Development of the database started in 2008 and took multiple years. This specific project focusses on the Dutch water sector. The data is enriched with qualitative data from the annual reports and with publications that were linked to the projects. 51 interviews have been conducted with Project Leaders and Members on their collaboration in STW projects. The respondents are sampled from the STW-data
Katherine Wisener - Projectdata for study 2
No full textTitleIncentives for clinical teachers: On why their complex influences should lead us to proceed with caution.
SummaryWhen medical education programs have difficulties recruiting or retaining clinical teachers, they often introduce incentives to help improve motivation. Previous research, however, has shown incentives can unfortunately have unintended consequences. When and why that is the case in the context of incentivizing clinical teachers remains unclear. The purposes of this study, therefore, were to understand what values and motivations influence teaching decisions; to delve deeper into how teaching incentives have been perceived; and, to provide recommendations to those seeking to better support clinician teachers. An interpretive description methodology was used to improve understanding of the development and delivery of teaching incentives. A purposeful sampling strategy identified a heterogenous sample of clinical faculty teaching in undergraduate and postgraduate contexts. The datafile, Study 2_Incentives and Motivations.nvpx, is an NVivo file containing interview transcripts with 16 faculty participants.<BR
Replication Data for Chapter 4 - Automation and Employment over the Technology Life Cycle: Evidence from European Regions
No full textThis dataset contains the scripts and packages needed to reproduce Chapter 4 in R.
- summeR: contains the functions and packages needed for R
- RealAutWaves: contains the raw data and the scripts to reproduce the analysis of Chapter 4
- a ReadMe detailing the analysis
Note: Almost all the raw data used in the chapter is included. However, the data for robots comes from the IFR and we do not have permission to publish it. Therefore, this dataset has been excluded.
Description of the folder RealAutWaves:
The folder _data\_raw contains all the raw data (except the robots data from IFR as it cannot be made public)
The folder _function contains the codes of functions that are later called in the main script of the analysis
The folder _script contains:
* a folder named "_clean" contains all the scripts to clean the raw data (this data will then be used in script 01-main, see below)
* init: this script is needed to set-up R (it will be used in the main scripts
On Being Unpredictable and Winning
No full textIn theory, it can be strategically advantageous for competitors to make themselves unpredictable to their opponents, for example by variably mixing hostility and friendliness. Empirically, it remains open whether and how competitors make themselves unpredictable, why they do so, and how this conditions conflict dynamics and outcomes.
We examine these questions in interactive attacker-defender contests, in which attackers invest to capture resources held and defended by their opponent.
Study 1, a re-analysis of nine (un)published experiments (total N=650), reveals significant cross-trial variability especially in pro-active attacks and less in re-active defense.
Study 2 (N=200) shows that greater variability makes both attacker’s and defender’s next move more difficult to predict, especially when variability is due to occasional rather than (in)frequent extreme investments in conflict.
Studies 3 (N=27) and 4 (N=106) show that pre-contest testosterone, a hormone associated with risk-taking and status competition, drives variability during attack which, in turn, increases sympathetic arousal in defenders and defender variability (Study 4). Rather than being motivated by wealth maximization, being unpredictable in conflict and competition emerges in function of the attacker’s desire to win ‘no matter what’ and comes with significant welfare cost to both victor and victim
Borg’s Rating of Perceived Exertion (RPE)
No full textThe Borg’s Rating of Perceived Exertion (RPE) measures the subjective perception of exercise intensity. The RPE comprises a 15-point scale ranging from 6 to 20, with marks at 7 (“very, very light”), 9 (“very light”), 11 (“fairly light”), 13 (“somewhat hard”), 15 (“hard”), 17 (“very hard”) and 19 (“very, very hard”)
Delay Discounting - Marshmallow task
No full textThe Delay of Gratification task aims to measure self-regulation by presenting the child with the choice between a small reward in the short-term and a larger reward in the long-term. The classic implementation of the Delay of Gratification task is the Marshmallow experiment, in which a child is presented with one marshmallow right now and is instructed that if they manage to wait for the researcher to return before eating it, they will receive multiple marshmallows later on. Multiple implementations of the Delay of Gratification task exist, with different reward types and suitable for different populations
Finger tapping task
No full textThe Finger Tapping Task is a computerized task that assesses motor control, speed and lateralized coordination. During the task, children have to tap switching between their right, left, or both index fingers as quickly as possible for 10 seconds. A total of five trials are measured, starting with a visual and an auditory queue. The total number of finger taps per trial and the inter-tap interval is recorded