This dataset contains all data and analysis scripts pertaining to the research conducted for the frontiers paper: "Using a conversational agent for thought recording as a cognitive therapy task: feasibility, content, and feedback." Following a literature review that we conducted in 2017 and 2018 on the technological state of the art of e-mental health for depression, we saw an opportunity to use technology in a more dialogical way than was being done to date. We therefore developed a conversational agent to support people in regularly recording their thoughts. This thought recording is a common technique in cognitive therapy. The cognitive approach to psychotherapy aims to change patients' maladaptive schemas, that is, overly negative views on themselves, the world, or the future. To obtain awareness of these views, they record their thought processes in situations that caused pathogenic emotional responses. We recruited 308 participants through Prolific, a crowd-sourcing platform for research participants. The participants interacted with our chatbot in two sessions, one practice session of two thought records based on scenarios and one actual session in which we asked to complete at least one personal thought record but as many additional ones as they wanted. We assessed the feasibility of completing the task with the agent, the content of the personal thought records, and whether the agent providing feedback on the content of the thought record (using natural language processing) had a positive e ect on the number of voluntarily completed thought records and participant's engagement in self-reection. We here deliver: a natural language dataset: the thoughts delineated by participants in the scenario-based and open thought records the coding of all personal thought records on their content by two independent coders: all thought records of the second session were labeled with respect to their content on the DIAMONDS and on three additional categories (COVID, Achievement/Competence, and Comprehensibility) analyses to test the hypotheses related to whether the feedback of the agent can increasemotivation to complete thought records additional materials (scenarios, qualtrics surveys, data management plan) that could assist in the replication of the study.
This dataset is published as part of the master thesis: "An Empirical Assessment on the Limits of Binary Code Summarisation with Transformer-based Models". It includes both the training/evaluation data as well as trained models. For more information, please refer to the data.md file or to the master thesis.
Attributing the start of peat growth to an absolute timescale requires dating the bottom of peat deposits overlying mineral sediment, often called the basal peat. Peat initiation is reflected in the stratigraphy as a gradual transition from mineral sediment to increasingly organic material, up to where it is called peat. So far, varying criteria have been used to define basal peat, resulting in divergent approaches to date peat initiation. The lack of a universally applicable and quantitative definition, combined with multiple concerns that have been raised previously regarding the radiocarbon dating of peat, may result in apparent ages that are either too old or too young for the timing of peat initiation. Here, we aim to formulate updated recommendations for dating peat initiation. We provide a conceptual framework that supports the use of the organic matter (OM) gradient for a quantitative and reproducible definition of the mineral-to-peat transition (i.e., the stratigraphical range reflecting the timespan of the peat initiation process) and the layer defined as basal peat (i.e., the stratigraphical layer that is defined as the bottom of a peat deposit). Selection of dating samples is often challenging due to poor preservation of plant macrofossils in basal peat, and the representativity of humic and humin dates for the age of basal peat is uncertain. We therefore analyse the mineral-to-peat transition based on three highly detailed sequences of radiocarbon dates, including dates of plant macrofossils and the humic and humin fractions obtained from bulk samples. Our case study peatland in the Netherlands currently harbours a bog vegetation, but biostratigraphical analyses show that during peat initiation the vegetation was mesotrophic. Results show that plant macrofossils provide the most accurate age in the mineral-to-peat transition and are therefore recommendable to use for 14C dating basal peat. If these are unattainable, the humic fraction provides the best alternative and is interpreted as a terminus-ante-quem for peat initiation. The potential large age difference between dates of plant macrofossils and humic or humin dates (up to ~1700 years between macrofossil and humic ages, and with even larger differences for humins) suggests that studies reusing existing bulk dates of basal peat should take great care in data interpretation. The potentially long timespan of the peat initiation process (with medians of ~1000, ~1300 and ~1500 years within our case study peatland) demonstrates that choices regarding sampling size and resolution need to be well substantiated. We summarise our findings as a set of recommendations for dating basal peats, and advocate the widespread use of OM determination to obtain a low-cost, quantitative and reproducible definition of basal peat that eases intercomparison of studies.
A continuous high-resolution humidity history in arid Central Asia over the past millennium based on the ~1.8-year high-resolution multiproxy records from Lake Dalongchi in the central Tianshan Mountains.
This dataset contains all data and analysis scripts pertaining to the research conducted for the PLOSOne paper: “Natural language processing for cognitive therapy: extracting schemas from thought records.” The cognitive approach to psychotherapy aims to change patients' maladaptive schemas, that is, overly negative views on themselves, the world, or the future. To obtain awareness of these views, they record their thought processes in situations that caused pathogenic emotional responses. To date, the schemas underlying such thought records have been largely manually identified. Using recent advances in natural language processing, we take this one step further by automatically extracting schemas from thought records. We used the Amazon Mechanical Turk crowd sourcing platform to collect a set of 1600 thought records. In total, these thought records contain 5747 thoughts of various depth levels, with the automatic thought constituting the most shallow level and the core belief the deepest level. We here deliver:1. a natural language dataset: the thoughts delineated by participants in the scenario-based and open thought records2. reliability analyses: all thoughts were labeled with respect to the degree to which they reflect a set of 9 possible schemas by the first author. An independent second coder also labeled a sample of the thoughts.3. analyses to determine whether automatic identification of thoughts is possible.4. additional materials (scenarios, instruction videos, qualtrics survey, osf preregistration form) that could assist in the replication of the study.
This is the supporting dataset for “Chamberlain, E.L., Shen, Z., Kim, W., Törnqvist, T.E., McKinley, S., & Anderson, S. Does load-induced shallow subsidence inhibit delta growth? In prep for Journal of Geophysical Research - Earth Surface”. Estimated submission date: March, 2021.The dataset includes stratigraphic information for boreholes of ten cross sections that were hand drilled from 2013-2015 CE in the bayhead region of the Lafourche subdelta, Mississippi Delta, USA. The study area spans ~6000 km2 and the cross sections are located near distributary channels. Data herein describe the location, surface elevation, and depth of each borehole as well as the depth relative to the surface of the mouth-bar to overbank (M-O) boundary and OSL ages for the mouth-bar deposits.We used these data to estimate centennial- to millennial-timescale cumulative subsidence and subsidence rates of a buried stratigraphic horizon, the M-O boundary, as detailed in the associated publication. Subsidence calculation methods are presented in the dataset, and subsidence data obtained from peat-top elevations at a relatively inland site (Paincourtville, LA, USA) are presented for comparison.
Materials In Paintings (MIP): An interdisciplinary dataset for perception, art history, and computer vision.Download the README.txt first to help you decide what you want/need to download!In this dataset, we capture the painterly depictions of materials to enable the study of depiction and perception of materials through the artists' eye. We annotated a dataset of 19k paintings with 200k+ bounding boxes from which polygon segments were automatically extracted. Each bounding box was assigned a coarse label (e.g., fabric) and a fine-grained label (e.g., velvety, silky).Note that the data can be browed and explored on https://materialsinpaintings.tudelft.nl. If you only want to download a few paintings, using that website might be faster.
The database illustrates the variety of functions and enlarges the solution space to preserve heritage. It shows different aspects to categorize on. That way, matches in possible functions can be found. The data is found by doing qualitative and quantitative research (case studies and literature research)