Investigating

## Overview

Investigating is a dataset for object detection tasks - it contains Investigating annotations for 1,233 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

We present the accompanying dataset to the study "A Multimodal Dataset for Investigating Working Memory in Presenceof Music". The experiment is conducted with the aim of investigating the viability of music as an intervention to regulate cognitive arousal and performance states. We recorded the multimodal physiological signals and behavioral data during a working memory task called the n-back task while the background music was playing. We requested the participants to provide the music, and two types of music were employed with the calming and exciting content. The calming music was played during the first session of the experiment, and the exciting music was presented during the second session. Each session includes an equal number of 1-back and 3-back task blocks, where 22 trials are presentedwithin each task block. A total number of 16 task blocks are implemented in each session (8 blocks of 1-back task and 8 blocks of 3-back task). In this experiment,11 participants/subjects originally participated, while we removed participants/subjects with small modalities. The recorded signals are skin conductance (SC), electrocardiogram (ECG), skin surface temperature (SKT), respiration, photoplethysmography (PPG), functional near-infrared spectroscopy (fNIRS), electromyogram (EMG), de-identified facial expression scores, sequence of correct/incorrect responses, and reaction time.

Abstract

The dataset was collected for 332 compute nodes throughout May 19 - 23, 2023. May 19 - 22 characterizes normal compute cluster behavior, while May 23 includes an anomalous event. The dataset includes eight CPU, 11 disk, 47 memory, and 22 Slurm metrics. It represents five distinct hardware configurations and contains over one million records, totaling more than 180GB of raw data.

Background

Motivated by the goal to develop a digital twin of a compute cluster, the dataset was collected using a Prometheus server (1) scraping the Thomas Jefferson National Accelerator Facility (JLab) batch cluster used to run an assortment of physics analysis and simulation jobs, where analysis workloads leverage data generated from the laboratory's electron accelerator, and simulation workloads generate large amounts of flat data that is then carved to verify amplitudes. Metrics were scraped from the cluster throughout May 19 - 23, 2023. Data from May 19 to May 22 primarily reflected normal system behavior, while May 23, 2023, recorded a notable anomaly. This anomaly was severe enough to necessitate intervention by JLab IT Operations staff.

The metrics were collected from CPU, disk, memory, and Slurm. Metrics related to CPU, disk, and memory provide insights into the status of individual compute nodes. Furthermore, Slurm metrics collected from the network have the capability to detect anomalies that may propagate to compute nodes executing the same job.

Usage Notes

While the data from May 19 - 22 characterizes normal compute cluster behavior, and May 23 includes anomalous observations, the dataset cannot be considered labeled data. The set of nodes and the exact start and end time affected nodes demonstrate abnormal effects are unclear. Thus, the dataset could be used to develop unsupervised machine-learning algorithms to detect anomalous events in a batch cluster.

https://doi.org/10.48550/arXiv.2311.16129

This study engaged 409 participants over a period spanning from July 10 to August 8, 2023, ensuring representation across various demographic factors: 221 females, 186 males, 2 non-binary, year of birth between 1951 and 2005, with varied annual incomes and from 15 Spanish regions. The MobileWell400+ dataset, openly accessible, encompasses a wide array of data collected via the participants' mobile phone, including demographic, emotional, social, behavioral, and well-being data. Methodologically, the project presents a promising avenue for uncovering new social, behavioral, and emotional indicators, supplementing existing literature. Notably, artificial intelligence is considered to be instrumental in analysing these data, discerning patterns, and forecasting trends, thereby advancing our comprehension of individual and population well-being. Ethical standards were upheld, with participants providing informed consent.

The following is a non-exhaustive list of collected data:

Data continuously collected through the participants' smartphone sensors: physical activity (resting, walking, driving, cycling, etc.), name of detected WiFi networks, connectivity type (WiFi, mobile, none), ambient light, ambient noise, and status of the device screen (on, off, locked, unlocked).

Data corresponding to an initial survey prompted via the smartphone, with information related to demographic data, effects and COVID vaccination, average hours of physical activity, and answers to a series of questions to measure mental health, many of them taken from internationally recognised psychological and well-being scales (PANAS, PHQ, GAD, BRS and AAQ), social isolation (TILS) and economic inequality perception.

Data corresponding to daily surveys prompted via the smartphone, where variables related to mood (valence, activation, energy and emotional events) and social interaction (quantity and quality) are measured.

Data corresponding to weekly surveys prompted via the smartphone, where information on overall health, hours of physical activity per week, lonileness, and questions related to well-being are asked.

Data corresponding to an final survey prompted via the smartphone, consisting of similar questions to the ones asked in the initial survey, namely psychological and well-being items (PANAS, PHQ, GAD, BRS and AAQ), social isolation (TILS) and economic inequality perception questions.

For a more detailed description of the study please refer to MobileWell400+StudyDescription.pdf.

For a more detailed description of the collected data, variables and data files please refer to MobileWell400+FilesDescription.pdf.

Spreadsheet includes data that were used to evaluate the mixing efficiencies of liquid-to-liquid chemical injection manifolds. Specifically, piping designs were developed to deliver fisheries chemicals (e.g. carbon dioxide) into water to control the movements of invasive bigheaded carps. These data describe mixing time, homogeneity and efficacy of carbon dioxide delivery using various piping designs. Results provide recommendations for piping configurations that could be installed within navigational locks to deliver invasive species control chemicals. There is 1 csv file containing text documents associated with this study

This dataset is about book series. It has 1 row and is filtered where the books is Investigating sexual assault. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Characteristics of 8 studies investigating TLR in the meta-analysis.

This study explored the COVID-19 pandemic as a context for learning about the role of science in a global health crisis. In Spring 2020, when New York City was the epicenter of the pandemic, we worked with a high school teacher to design and implement a unit on human brain and behavior science. The unit guided her 17 students in creating studies that explored personally-relevant questions about the pandemic to contribute to a citizen science platform.Our research questions were:What impact did students’ participation have on their science learning activation, citizen science agency, and science identities?How do students use science inquiry to make sense of their experiences in the pandemic? What are students’ views of the role of science in a public health crisis? What are students’ and their teacher’s experiences engaging in inquiry on a crisis that is currently impacting them?Pre/post surveys, student artifacts, and student and teacher interviews showed increases in students’ fascination with science—a driver of engagement and career preference—and sense of agency as citizen scientists. Students approached science as a tool for addressing their pandemic-related concerns, but were hampered by the challenges of remote schooling. These findings highlight both the opportunities of learning from a global crisis, and the need to consider how that crisis is still impacting learners.The data shared here relate to the quantitative analyses conducted on students' pre and post surveys, which mainly address Research Question 1.

According to many influential theories of memory, successful retrieval depends on the degree to which the processes used when attempting to retrieve information overlap with the processes that were used when learning that information. This can be illustrated using the generation effect. If participants in a memory experiment are asked to read words (eg, TABLE) and generate others from anagrams (eg, HIRAC = chair) they are more likely to remember the words they generated. This advantage is enhanced if participants have to generate the words again when their memory is tested. However, this "reinstatement effect" doesn't occur with all tasks. For example, memory for words that were read during the learning stage is not enhanced if they are read again at the test stage. The aim of this research is to establish the circumstances under which the reinstatement effect occurs. For example, it is possible that the effect only occurs with tasks that require effortful processing, such as generating from anagrams, and not with tasks that are relatively automatic, such as reading. The research will also investigate the duration of the reinstatement effect and how closely the learning and test processes must overlap in order for the effect to occur.

INVAR behaviour is the property of having zero thermal expansion over a given temperature range; something very important for certain engineering applications, and very practical in others. The physical process describing this phenomena involves a perfect balance between varying magnetic interactions in the material, and the interatomic separation as a function of temperature. Pd3Fe was recently shown to enter into such an INVAR state when the system is placed under high pressure - we aim to use this to probe the nature of the phase transition to this novel state, to better understand how INVAR materials work.

This dataset represents a data assessment of select researchers across multiple communities of practice at the University of Florida as part of an IRB 201602303 study to investigate the data management practices, storage, and training needs of researchers. The study was conducted from January 3, 2017 - April 30, 2017. One hundred fifty-nine starts, one hundred fifty-six informed consent, and one hundred thirty-three completes for a 83% completion. However, Question 26 which contained PID was deleted from this raw dataset.

The associated manuscript reports the results of a repeated sampling of a watershed, including surface water and drinking water, that were analyzed with multiple non-targeted analysis (NTA) methods. About 130 chemicals were tentatively identified in the environmental samples, with no concentrations being reported with the non-targeted analysis. The results of this manuscript provide users of NTA with important information on which types of chromatography and ionization methods can be used to detect different classes of environmental contaminants, including PFAS, pesticides, surfactants, and pharmaceuticals. The metadata published here includes a list of all unidentified chemical features detected by each non-target method tested. This dataset is associated with the following publication: Batt, A., L. Brunelle, N. Quinete, E. Stebel, B. Ng, P. Gardinali, A. Chao, A. Huba, S. Glassmeyer, D. Alvarez, D. Kolpin, E. Furlong, and M. Mills. Investigating the chemical space coverage of multiple chromatographic and ionization methods using non-targeted analysis on surface and drinking water collected using passive sampling. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 955: 176922, (2024).

Title

An fMRI dataset for investigating language control and cognitive control in bilinguals

Abstract

The neural correlation between language control and cognitive control in bilinguals remains an area ripe for further exploration. In this work, we present a functional magnetic resonance imaging (fMRI) dataset that simultaneously examines both types of control in 77 healthy, unrelated Chinese-English bilinguals. Each participant completed a language switching task to assess language control and a rule switching task to evaluate cognitive control while undergoing functional MRI scanning. We collected structural imaging data, task-related functional imaging data, and behavioral data from the participants. Additionally, their language proficiency and domain-general cognitive ability were recorded after the scanning. This dataset was released early to facilitate the exploration of the neural relationship between language control and cognitive control, promoting its broader use and benefiting the scientific community. It is well-suited for the study of causality analysis, representational similarity analysis and the prediction model construction.

This dataset reports responses to a survey designed for investigating the relationship that humans have with their smart devices. The dataset has been collected in May-July 2020 and is a sample of over 500 respondents of various different ethnicities and backgrounds. These data have been used for modelling the ways people relate to their devices using the notion of agency. However, the data can be used for complementing any study that intends to investigate a tool-mediated communication from the perspective of the users and via a variety of attitudes and expectations the users invest in their devices and in themselves as users. This article presents the survey items as well as some raw data insights. The data have been collected in English and answers have been anonymized in order to ensure GDPR compliance. They are stored in a .csv file containing the respondents’ answers to the questions. The reference contact for this data at Tilburg University is Francesco Lelli The paper "A Dataset for Studying How Human Relates to their Smart Devices" provide an extensive description of the data as well as the methodology for collecting the samples.

This study engaged 103 participants over a period spanning from November 14 to December 16, 2021, ensuring representation across various demographic factors: 51 females, 52 males, aged 18-70, with varied annual incomes and from 17 Spanish regions. The MobileWell100+ dataset, openly accessible, encompasses a wide array of data collected via the participants' mobile phone, including demographic details, COVID-19-related inquiries, emotional, behavioral, and well-being data. Complementing this, social welfare data from external sources offers contextual insight. Methodologically, the project presents a promising avenue for uncovering new social, behavioral, and emotional indicators, supplementing existing literature. Notably, artificial intelligence is considered to be instrumental in analysing these data, discerning patterns, and forecasting trends, thereby advancing our comprehension of individual and population well-being. Ethical standards were upheld, with participants providing informed consent.

The following is a non-exhaustive list of collected data:

• Data continuously collected through the participants' smartphone sensors: physical activity (resting, walking, driving, cycling, etc.), name of detected WiFi networks, connectivity type (WiFi, mobile, none), ambient light, ambient noise, and status of the device screen (on, off, unlocked).
• Data corresponding to an initial survey prompted via the smartphone, with information related to demographic data, symptoms and COVID vaccination, average hours of physical activity, and answers to a series of questions to measure mental health, many of them taken from internationally recognised psychological and well-being scales (PANAS, PHQ, GAD, BRS and AAQ).
• Data corresponding to daily surveys prompted via the smartphone, where variables related to mood (valence, activation, energy and emotional events) are measured.
• Data corresponding to weekly surveys prompted via the smartphone, where information on work situation, symptoms and COVID vaccination, hours of physical activity per week, questions related to physical and mental health, etc. is requested.

For a more detailed description of the study please refer to MobileWell100+StudyDescription.pdf.

For a more detailed description of the collected data, variables and data files please refer to MobileWell100+FilesDescription.pdf.

The ACIDD (Across the Channel Investigating Diel Dynamics) project, in the Santa Barbara Channel, was initially designed to characterize daily variations in phytoplankton populations, but with the Thomas Fire in the Santa Barbara Hills December 2017, this project evolved into a study to characterize the effects of smoke and ash on the mixed layer in the Santa Barbara Channel.

This dataset is raw data of my research paper: Investigating Artificial Intelligence Adoption Decisions in China’s Apparel Manufacturers with An Extended UTAUT-TOE Framework.

This is a dataset for an eye movement experiment investigating effects of visual blurring on the recognition of Chinese characters during reading. Characters in sentences were presented as normal or with an exterior or interior region visually degraded. As the exterior region was informative about overall character shape, this manipulation allowed us to investigate effects of preserving or degrading information that might support holistic processing. Additionally, to assess effects on the lexical processing of words, each sentence included one of a pair of interchangeable two-character target words that had either a high or low frequency of written usage.The files provide sentence-level and word-level eye movement data. The sentence-level data are informative about the effects of visual blurring on overall sentence reading, while the word-level data are informative about effects of visual blurring on the processing of individual words in sentences, where these words are of either high or low lexical frequency.

Child Protective Investigations (CPI) is authorized to investigate abuse and neglect allegedly committed by a person responsible for a child's care, custody or welfare and to protect abused and neglected children from further harm. This authorization is derived from the U.S. Social Securities Act, Texas Family Code, Human Resources Code, Child Abuse Prevention and Treatment Act, Indian Child Welfare Act and the Adam Walsh Act. CPI conducts either a traditional investigation or Alternative Response (AR). Both require CPI to assess safety and take needed actions to protect a child and assess the risk of future abuse or neglect in the foreseeable future. AR, however, allows for a more flexible, family engaging approach on lower priority cases involving alleged victims who are age 6 or older. AR differs from traditional investigations in that there is no substantiation of allegations, no entry of perpetrators into the Central Registry (a repository for reports of child abuse and neglect), and there a heightened focus on guiding the family to plan for safety in a way that works for them and therefore sustains the safety. Completed investigations only include those cases conducted as a traditional investigation that were not administratively closed or merged into another stage. An investigation can only be administratively closed if all allegations have a disposition of administrative closure. A completed investigation can include more than one alleged victim. Completed investigations do not include any Alternative Response cases. A description of Alternative Response and how it differs from a traditional investigation is in the glossary. FOOTNOTES An investigation represents a report of abuse or neglect and can involve multiple children. The data on completed investigations does not include investigative stages that were administratively closed or merged into another investigation. All completed investigations have a case disposition and a risk finding. Visit dfps.state.tx.us for information on Abuse/Neglect Investigations and all DFPS programs.

A poll conducted in August 2022 found that ** percent of respondents strongly approve of the Justice Department investigating Donald Trump for possibly violating the Presidential Records Act. In contrast, ** percent of respondents strongly disapprove.