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Replication Data for: The Political Consequences of the Mental Load
No full textThe files can be used to replicate tables and figures included in "The Political Consequences of the Mental Load," published in European Sociological Review 2025. Abstract: How do levels of cognitive household labor -- the ``mental load'' involved in anticipating, fulfilling, and monitoring household needs -- affect political interest? The mental load is distinct from the physical tasks of e.g., cooking and cleaning, and thought to be disproportionately undertaken by women. I argue that while low levels of mental load can foster political interest, at high levels the mental (over)load crowds out cognitive space for political issues, especially issues seen to be distant from family life. To test this argument, I field a novel survey on politics and household work to a sample of parents in the United States. I find a large gender gap, with mothers reporting primary responsibility for 71 percent of cognitive household labor, compared to fathers' 45 percent. Low levels of mental load are positively linked to political interest, while high levels of mental load decrease interest in certain issues, including national politics and inflation. I report similar effects for fathers and mothers, but due to the gendered distribution of mental load (mothers carrying more load, on average), negative consequences are more common among mothers. The findings offer new evidence about a gender gap too often hidden, and its consequences for political life.The files include 1 data file and 1 file with the code needed to produce all tables and figures in text, using R v 4.4.2
Dataset for, "Fermi liquid theory of d-wave altermagnets: demon modes and Fano-demon states"
No full textThis dataset contains all raw data required to reproduce the findings of these studies, as presented in Figures 1–3. All data files are in .txt format and are organised into three separate folders, each corresponding to one figure.The methodology for this numerical simulation can be found in the associated paper.Wolfram Mathematica and Python were used to perform the simulation
China's National Climate Policy Database (V1)
No full textChina's National Climate Policy Database (V1) collects all national-level policies addressing climate change issued by the Chinese government between 2016 and 2022. In addition to tracking the policies issued across different sectors, the database also maps policy instruments used and measures the intensity of each policy.Please see the methodology in the associated article; the supporting codebook can be found in the supplementary information section
Dataset for "Thermal comfort in lower economic groups: The applicability of the ASHRAE-55 adaptive standards in informal settlements and refugee camps"
No full textLarge-scale thermal comfort survey in refugee/displacement camps, to establish whether comfort theory needs to be modified for such populations. This was achieved through highly difficult data collection in refugee camps in Jordan, Djibouti and Ethiopia and displacement camps in Peru. The data collected consists of 1982 rows of personal variables and concurrent environmental measurements.Thermal comfort surveys, including spot measurements of environmental parameters, were conducted in Zaatari and Azraq refugee camps in Jordan, Markazi camp in Djibouti, Hitsats camp in Ethiopia and several towns in Peru (Chivay and Yanake in the Andes Mountains, Viru and Trojillio on the coast, and Nuevo Belen in the Amazon). The surveys were either conducted directly by the research team who spoke the local language (Jordan and Peru) or with the assistance of local interpreters (Ethiopia and Djibouti). The thermal comfort scales used were the standard 7-point ASHRAE thermal sensation scale and the 5-point thermal preference scale (see Table 1). The thermal sensation scale records an occupants' Thermal Sensation Vote (TSV) on a scale of (hot to cold), while the thermal preference scale asks the occupants what their preferred sensation is (Thermal Preference Vote, TPV) at that moment, from ''much cooler'' to "much warmer". A numerical approach to voting on the scales is used. The spot measurements of environmental parameters include air velocity, wet bulb temperature, air temperature, relative humidity, and globe temperature, and were collected using hand-held devices at the same time a TSV and TPV were taken. These variables where then used to calculate the operative temperature for each corresponding vote
Dataset on the association between ICC operations and the number of fatalities of organised violence
Full text linkThis dataset is created for a study of the association between International Criminal Court (ICC) investigations and fatalities of organised violence. It links fatalities statistics from the Uppsala Conflict Data Program to data on UN peacekeeping operations, ICC investigations and unilateral operation by great powers (UNSC P5). Specifically, it contains monthly data on fatalities from 1998–2020 in countries in which the ICC was conducting investigations, and the timeline of the aforementioned interventions in those countries. It also contains per-country graphs of the data for the Central African Republic, the Democratic Republic of Congo, Libya, and Palestine.
Definitions and data are further explained and justified in the associated publication, "The International Criminal Court and Organised Violence" by Mattia Cacciatori and Timo Kivimäki.The methodology used to assemble the Uppsala Conflict Data Program Georeferenced Event Dataset (GED) can be found in Sundberg and Melander (2013).
The timeline of ICC, UN and unilateral operations has been explained and justified in the article associated with this dataset. The included do file shows the steps taken to derive this dataset.The UCDP data on battle deaths of organised violence was transformed into monthly data in the way shown in the do file.The data was created with the Stata/BE 17 program; the do file can be opened with any text editor
Dataset for "What is the effect of presenting evidence of the mental vs physical health benefits of quitting smoking on motivation to stop smoking? An online randomised controlled experiment"
Full text linkUsing this dataset we assessed whether mental health labels presenting information of the benefits of quitting smoking for mental health was more effective than blank labels and physical health labels, and if there were any differences between those with mental ill health, or not.
Participants took part in an online randomised experiment, with three conditions: i) mental health labels; ii) physical health labels; iii) blank labels. Participants were randomly allocated to label condition, stratified by mental health status, and viewed four labels for at least 10 seconds each. Motivation to stop smoking was measured using the Motivation to Stop Scale (MTSS) before and after viewing the labels.Full details of the study protocol are available from the OSF Project "Positive Messaging for Tobacco Packaging" (Sawyer et al., 2024)
Dataset for "Redefining Accessibility: Uncovering Cultural, Social, and Economic Barriers to Urban Green Space Accessibility"
No full textThe files are data collected as part of a study on accessibility to green spaces in Bristol. The first file is over 240 participant responses to a survey on urban green space (UGS) use in Bristol. It includes information on the nature of UGS use, the frequency of use along with other details, in addition to non-identifying demographic data.
The second is focus group data, the focus groups were aimed at marginalised groups, especially ethnic minorities. The participants of four focus groups were non-regular users of green space. One of the focus groups was for regular users.
The third file is results of a participatory design workshop, where participants were asked to come up with design and planning solutions to make Netham park in Bristol more accessible for the diverse community it serves.JISC survey system was used for the online survey. The link was distributed through social media and community groups. For the focus groups, researcher notes were taken to document the discussion which was guided by a preset of questions and topics, then analysed using thematic analysis with NVivo. For the participatory workshop, solutions were documented during the event, and later transcribed into a file that was analysed using NVivo.NVivo was used for thematic coding, MS Excel was used for statistic analysis of the survey results
Dataset for Kurhan et al. "The development of a silage based biorefinery to deliver the maximum nutritional benefit for human consumption from UK grasslands"
No full textThis data set contains the underlying data and information presented in the paper, "The development of a silage based biorefinery to deliver the maximum nutritional benefit for human consumption from UK grasslands". Water-soluble protein and vitamins were extracted from silage using a twin-screw extruder at room temperature. The solids from the extruder, which contained further insoluble protein and the carbohydrates from the silage, were depolymerised and used to culture the oleaginous yeast Metschnikowia pulcherrima, producing further mycoprotein and lipid from the system.
Included in the dataset are:
- an analysis of the silage feedstock, specifically the quantities of dry matter/ash, carbon and nitrogen, key essential amino acids, and Vitamin B1, B2, B3 (nicotinamide and nicotinic acid) and B6 (pyridoxine, pyridoxal and pyridoxamine);
- a comparison of amino acid profiles in the silage (before and after extraction) and a comparison to other common protein sources;
- a comparison of the performance of the extruder at different power levels, liquid feeding quantities, temperatures, and screw speeds, with regard to nitrogen content, protein content and yield;
- the fatty acid profile of the M. pulcherrima yeast grown on the resulting hydrolysate produced from the residues of the process.Data was collected according to standard technical methods, fully set out in the materials and methods section of the associated paper.Data was processed using Microsoft Excel.All data is listed with a description and to which part of the associated paper it corresponds: the figure, table or supporting information
Dataset for "Filament Type Recognition for Additive Manufacturing Using a Spectroscopy Sensor and Machine Learning"
No full textThe file contains data collected from three sensors: AS72651, AS72652, and AS72653. Each of these sensor-specific files includes three folders representing different measurement distances: 12mm, 16mm, and 20mm. These folders contain data collected from 12 different filaments. Additionally, there is data recorded without a filament, labeled as "no_object." The following example of organizational structure is consistent across all filament data.
Folder : AS72651 ->12mm->first measurement: abs_carbonfiber1.csv
->second measurement: abs_carbonfiber2.csv
->third measurement: abs_carbonfiber3.csv
->three measurements together: abs_carbonfiber.csv
Each collected dataset is stored in CSV format. The data can be utilized for filament recognition using machine learning models, enabling the identification of different filament types based on sensor measurements.The methodology is described in the associated paper
Dataset for "Effects of a combined energy restriction and vigorous-intensity exercise intervention on the human gut microbiome: A randomised controlled trial"
No full textThis CSV file contains the raw data for all the anthropological, physiological, and biochemical parameters for each human participant, pre and post intervention or control reported in the associated article in the Journal of Physiology.Data were collected using a series of physiological, biological, biochemical, and molecular biology techniques. Details of the methodologies and analysis techniques have been provided in the associated article submitted for publication in Journal of Physiology.
The study protocol was approved by the Carmarthen NHS Research Ethics Committee (REC reference number: 17/WA/0422) and was conducted in accordance with the principles of the Declaration of Helsinki and registered at clinicaltrials.gov (NCT03362554)
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