Overview The Dream EEG and Mentation (DREAM) database collects and stores metadata about DREAM datasets, and is accessible to the public. DREAM datasets provide polysomnography and associated subjective mentation reports. Some datasets may also contain personally identifiable information about participants, but such information are not stored by the DREAM database. Datasets are contributed to DREAM from many different labs in many different studies and, where possible, made openly accessible in the hope of pushing the fields of sleep, dream, brain-computer interface, and consciousness research forward. If you have data that others in the community might find useful, please consider contributing it to DREAM. Contents The DREAM database consists of a following data tables:

Datasets Data records People

The records in Datasets list all officially accepted DREAM datasets and their summary metadata. Data records lists metadata of each individual datum from these datasets. People provides information on the data contributors, referred to by Key ID in Datasets.

The DREAMS Databases and Assessment algorithm

During the DREAMS project funded by Région Wallonne (Be), we collected a large amount of polysomnographic recordings (PSG) to tune, train and test our automatic detection algorithms.

These recordings were annotated in microevents or in sleep stages by several experts. They were acquired in a sleep laboratory of a belgium hospital using a digital 32-channel polygraph (BrainnetTM System of MEDATEC, Brussels, Belgium). The standard European Data Format (EDF) was used for storing.

In order to facilitate future research and performance comparision, we decided to publish these data on Internet. Therefore, eight DREAMS databases are available according to the annotation carried out (click on the link to open):

• The DREAMS Subjects Database: 20 whole-night PSG recordings coming from healthy subjects, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;

• The DREAMS Patients Database: 27 whole-night PSG recordings coming from patients with various pathologies, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;

• The DREAMS Artifacts Database: 20 excerpts of 15 minutes of PSG recordings annoted in artifacts (cardiac interference, slow ondulations, muscle artifacts, failing electrode, 50/60Hz main interference, saturations, abrupt transitions, EOG interferences and artifacts in EOG) by an expert;

• The DREAMS Sleep Spindles Database: 8 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in sleep spindles; PLEASE NOTICE THAT EXPERT 1's SCORED SPINDLE COUNTS WERE CUT OFF AFTER 1000 SECONDS. THIS MAKES IT DIFFICULT TO USE COUNTS FOR COMPARISON.

• The DREAMS K-complexes Database: 5 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in K-complexes;

• The DREAMS REMs Database: 9 excerpts of 30 minutes of PSG recordings in which rapid eye movements were annotated by an expert;

• The DREAMS PLMs Database: 10 whole-night PSG recordings coming from patients in which one of the two tibialis EMG was annoted in periodic limb movements by an expert;

• The DREAMS Apnea Database: 12 whole-night PSG recordings coming from patients annoted in respiratory events (central, obstructive and mixed apnea and hypopnea) by an expert.

We also developped and tested several automatic procedures to detect micro-events such as sleep spindles, K-complexes, REMS, etc. and provide the source codes for them in the DREAMS Assessment Algorithm package.

(MORE INFORMATION ON EACH DBA CAN BE FOUND in pdf file in this repository)

All our publications on this subject can be found in : https://www.researchgate.net/scientific-contributions/35338616_S_Devuyst

Format is as follows:

dream_id: Incremental identifier of the dream
dreamer: short string identifier of the dreamer (from dreambank)
description: short description of the dreamer (from dreambank)
dream_date: approximate date of when the dream was reported; expressed in free-text, format may vary (from dreambank)
dream_language: language of dream
text_dream: the actual dream report, written by the dreamer
characters_code: Hall-Van de Castle (HVC) code that encode the characters present in the dream
emotions_code: HVC code that encode the emotions present in the dream
aggression_code: HVC code that encode the aggression interactions present in the dream
friendliness_code: HVC code that encode the friendlyinteractions present in the dream
sexuality_code: HVC code that encode the sexual interactions present in the dream
Male: %of male characters
Animal: %of animal characters
Friends: %of characters that are friends to the dre...

Comprehensive dataset containing 10 verified Dreams locations in United States with complete contact information, ratings, reviews, and location data.

Dream EEG and Mentation (DREAM) data set ---Data set information--- Common name: Zhang & Wamsley 2019 Full name: N/A Authors: Jing Zhang, Erin Wamsley Location: Furman University, Greenville SC Year: N/A Set ID: [SET BY DATABASE] Amendment: [SET BY DATABASE] Corresponding author ID: [SET BY DATABASE] Download URL: [SET BY DATABASE] Previous publications: Zhang, J., & Wamsley, E. J. (2019). EEG predictors of dreaming outside of REM sleep. Psychophysiology, 56(7), e13368. Correspondence: Dr. Erin Wamsley: erin.wamsely@furman.edu ---Metadata--- Key ID: [SET BY DATABASE] Date entered: [SET BY DATABASE] Number of samples: [INFERRED BY DATABASE] Number of subjects: [INFERRED BY DATABASE] Proportion REM: [INFERRED BY DATABASE] Proportion N1: [INFERRED BY DATABASE] Proportion N2: [INFERRED BY DATABASE] Proportion experience: [INFERRED BY DATABASE] Proportion no-experience: [INFERRED BY DATABASE] Proportion healthy: [INFERRED BY DATABASE] Provoked awakening: Yes Time of awakening: Mixed Form of response: Free Date approved: [SET BY DATABASE] ---How to decode data files--- Sleep stage codes in the filenames/Case IDs: "SO#" = sleep onset reports collected after

This repository duplicates the entire Sleep and Dream Database (SDDb), a public collection of dream reports. The purpose of this repository is (a) to provide a convenient access point for the dream reports, and (b) to provide a system of SDDb version control so that analysis of these dream reports can be replicated even when the official SDDb undergoes modifications. It contains all SDDb dream reports as of the date of download (see "Dates" section of this repository). No additional processing was applied.

The continuity hypothesis of dreams - a widely studied model of dreaming - suggests that the content of dreams is largely continuous with the waking experiences of the dreamer. Given the unprecedented nature of the experiences during the pandemic of COVID-19, we studied the continuity hypothesis in the context of such a pandemic. To that end, we implemented a state-of-the-art deep-learning algorithm that can accurately extract mentions of virtually any medical condition from text and applied it to two sets of data collected during the COVID-19 pandemic: 2,888 dream reports (dreaming life experiences), and 57M tweets mentioning the pandemic (waking life experiences). We found that the health expressions that were shared by both sets were common COVID-19 symptoms (e.g., coronavirus, anxiety, coughing, and stress), suggesting that dreams reflected people's real-world experiences. On the other hand, we found that the health expressions that distinguished the two sets reflected differences in thought processes: health expressions in waking life reflected a linear and logical thought process and, as such, described realistic symptoms or related disorders (e.g., body aches, nasal pain, SARS, H1N1); by contrast, those in dreaming life reflected a thought process likely based on the activation of the visual and emotional areas of the brain and, as such, described either conditions not necessarily associated with the pandemic's virus (e.g., maggots, deformities, snakebites), or conditions of surreal nature (e.g., teeth suddenly falling out, body crumbling into sand). Our results confirm that, in addition to the sources of health data being researched lately (e.g., psychological conditions inferred from social media posts, physiological readings from commercial wearables), dream reports, if interpreted correctly, represent an understudied yet valuable source of people's health experiences in the real world.

Presented is an implementation of the SALAD algorithm for dream content analysis through word searching. Helper functions for constructing initial seed word dictionaries are provided in "hyponym_dictionary.py" which will also be used to construct the dictionaries from the seed words. "read_csv.py" reads and pre-processes the dream reports into a dictionary that captures the linguistic features of the words and sentences from the dreams. It also contains an implementation of the Improved Lesk Algorithm. The folder Series/ can be populated with data from any dream journal (you can take data from www.dreambank.net). The required data format is a csv file containing one dream in each row. The code "search_lemmas.py" performs the actual word search. The exact steps of SALAD and the parameters that need to be played around with to obtain the best results are described in the paper. The codes are written in Python 3.6 and can run on Python 3.6 and above.

Most psychologists are likely to have at least some clients bring a dream into therapy. In the few studies looking at the use of dreams in therapy, therapists report that they do not feel confident or competent to adequately respond to their clients' introduction of dream material in therapy. The possible consequences of this include a negative impact on the therapeutic alliance and misinterpretation of the therapist's rejection of a dream narrative as a disinterest in the client's inner life. This research project seeks to identify psychologists' and psychology clients' understanding of their experiences of the use of dream material in therapy and their understanding of the role of dreams in contemporary psychological practice. While there have been some surveys about the use of dreams in therapy, relatively little is known about this topic, so a phenomenological, qualitative approach will be used. This research will be broken into two studies. The first study will use semi-structured interviews to interview psychologists and the second study will use semi-structured interviews to interview psychology clients. A hermeneutic phenomenological analysis of the interview transcripts will be completed with the aid of Dedoose software.

Comprehensive dataset containing 12 verified Dreams locations in Argentina with complete contact information, ratings, reviews, and location data.

The research project presented aims at putting the personal perception of Nathalie’s dreams through an objective, quantitative analysis using electroencephalography (EEG), in an attempt to establish a linkage between the two dimensions.

During sleep periods, brain activity is similar to that of an awakened state, yet the thalamus, a phylogenetically ancient structure in the nervous system, isolates us from the environment. But this isolation is not total, and sometimes external stimuli are incorporated into the plot of our dreams. To establish a bridge between the record (EEG) and Nathalie’s dream narrative, we experiment with auditory stimuli as a possible mechanism of interference.

The 101 nights is a longitudinal dataset. At the core of the study is the concordance of two divergent fields of knowledge to record and represent the dream experience. For 101 nights physiological and behavioral data are continuously paired with the dreamer’s inner life, geared towards a dialogue.

A unique dataset for scientific analyses, methodological developments as well artistic projects, including cognitive science and multiple modalities of art. The unprecedented project allows an internal and external perspective on Nathalie’s dreams, containing extensive data for 101 consecutive nights and days.

the project produced four immediate results:

1. 952 GB of brain data was produced by the registry of 256 sensors over the period of 101 nights in continuity, including her body movement (actimetry and infrared camera).
2. the audio logs of the words triggered by the computer system, each night with their exact time.
3. Nathalie’s daily dream diary entries.
4. day-by-day activity.

The overall objective of the i-DREAMS project is to setup a framework for the definition, development, testing and validation of a context-aware safety envelope for driving (‘Safety Tolerance Zone’), within a smart Driver, Vehicle & Environment Assessment and Monitoring System (i-DREAMS). Taking into account driver background factors and real-time risk indicators associated with the driving performance as well as the driver state and driving task complexity indicators, a continuous real-time assessment is made to monitor and determine if a driver is within acceptable boundaries of safe operation. Moreover, safety-oriented interventions were developed to inform or warn the driver real-time in an effective way as well as on an aggregated level after driving through an app- and web-based gamified coaching platform. The conceptual framework, which was tested in a simulator study and three stages of on-road trials in Belgium, Germany, Greece, Portugal and the United Kingdom on a total of 600 participants representing car, bus, and truck drivers, respectively. Specifically, the Safety Tolerance Zone (STZ) is subdivided into three phases, i.e. ‘Normal driving phase’, the ‘Danger phase’, and the ‘Avoidable accident phase’. For the real-time determination of this STZ, the monitoring module in the i-DREAMS platform continuously register and process data for all the variables related to the context and to the vehicle. Regarding the operator, however, continuous data registration and processing are limited to mental state and behavior. Finally, it is worth mentioning that data related to operator competence, personality, socio-demographic background, and health status, are collected via survey questionnaires. More information of the project can be seen from project website: https://idreamsproject.eu/wp/

This dataset contains naturalistic driving data of various trips of participants recruited in i-Dreams project. Various different types of events are recorded for different intensity levels such as headway, speed, acceleration, braking, cornering, fatigue and illegal overtaking. Running headway, speed, distance, wipers use, handheld phone use, high beam use and other data is also recorded. Driver characteristics are also available but not part of this sample data. In the i-Dreams project, raw data for a particular trip was collected via CardioID gateway, Mobileye, wristband or CardioWheel. These trip data are fused using a feature-based data fusion technique, namely geolocation through synchronization and support vector machines. The system provided by CardioID integrates several data streams, generated by the different sensors that make up the inputs of the i-Dreams system. The sample dataset is fused, processed as well as aggregated to produce consistent time series data of trips for a particular time interval such as 30 secs/ 60 secs or 2- minutes intervals. More datasets can be acquired for analysis purposes by following the data acquisition process given in the data description file.

Abstract

The main aim of this study is to evaluate the impact and effectiveness of the scale up of the DREAMS HIV prevention package of biological, behavioural and social interventions in reducing HIV incidence in adolescent girls and young women residing in the uMkhanyakude district of KwaZulu-Natal. To achieve this aim, the changes in different outcomes will be assessed over time in relation to DREAMS roll-out. The primary outcome will be HIV incidence and other key secondary outcomes will include knowledge of own HIV status, sexual debut, HSV-2, number of sexual partners, age-disparity with sexual partners, ever been pregnant, condom use, unmet need for contraception, transactional sex, education (remaining in school) and experiences of violence.

Geographic coverage

Demographic surveillance area of the Africa Health Research Institute; KwaZulu-Natal, uMkhanyakude district.

Analysis unit

Individual

Universe

A planned closed cohorts of 800 AGYW will be followed prospectively at three time points over the two-year study period - baseline, 12 months and 24 months - at points closest aligned with periods before, during and after DREAMS implementation. In ACDIS cohorts of 400 girls aged 13-17 years and 400 young women aged 14-23 years will undergo informed consent, recruited undergo a baseline questionnaire and provide dry blood spots for HSV2 at the same time they provide a sample for the HIV testing in the surveillance and then reviewed annually for the next two years.

Kind of data

Longitudinal survey data

Sampling procedure

This is a closed cohort of AGYW who were enrolled in 2017 (aged 13-22) being followed up in 2018 aged 14-23 who were residents in the demographic surveillance area of the Africa Health Research Institute. A total of 3013 participants were randomly selected to obtain a target sample size of 800 after 2 years of follow-up, allowing for 40% non-contact/loss-to-follow-up. Sampling was stratified by age group and area (week-blocks).

Cleaning operations

All data will be managed using electronic data management tools. The data management system for these will be based on REDCap (research electronic data capture) developed at Vanderbilt University. The REDCap database resides within a single MySQL database server within a secure server cluster at the AHRI. Survey data are synchronised by the REDCap application from the mobile device to a central MySQL server. Access control is managed through Microsoft Active Directory with minimum password complexity and compulsory password change policies.

This repository contains the data generated for the research study titled "Dream Content Discovery from Reddit with an Unsupervised Mixed-Method Approach". The descriptions of the files (& their contents) can be found in files_description.pdf

The corresponding manuscript (preprint) can be found at https://arxiv.org/abs/2307.04167

Dream EEG and Mentation (DREAM) data set ---Data set information--- Common name: REM_Turku Full name: REM_Turku Authors: Pilleriin Sikka, Antti Revonsuo, Valdas Noreika, and Katja Valli Location: Turku, Finland Year: N/A Set ID: [SET BY DATABASE] Amendment: [SET BY DATABASE] Corresponding author ID: [SET BY DATABASE] Download URL: [SET BY DATABASE] Correspondence: Pilleriin Sikka (pilsik@utu.fi), Katja Valli (katval@utu.fi) ---Metadata--- Key ID: [SET BY DATABASE] Date entered: [SET BY DATABASE] Number of samples: [INFERRED BY DATABASE] Number of subjects: [INFERRED BY DATABASE] Proportion REM: [INFERRED BY DATABASE] Proportion N1: [INFERRED BY DATABASE] Proportion N2: [INFERRED BY DATABASE] Proportion experience: [INFERRED BY DATABASE] Proportion no-experience: [INFERRED BY DATABASE] Proportion healthy: [INFERRED BY DATABASE] Provoked awakening: Yes Time of awakening: Night Form of response: Free Date approved: [SET BY DATABASE] ---How to decode data files--- The PSG files are raw (i.e., not preprocessed) EEG data files and include the last 2 minutes of preawakening EEG from each REM episode obtained using a serial awakening paradigm in the sleep laboratory. Data set includes EEG data from 134 awakenings of 18 participants. Files are organized according to the format: /Data/PSG/casexx_syy, where xx refers to Case ID yy refers to Subject ID In the "Records.csv" file, the following information is presented: Filename: in the format /Data/PSG/Sxx_yy Case ID: number of awakening for the subject Subject ID: unique identifier of subject Experience: 2 = dream experience; 1 = without recall ("white dream"); 0 = no dream experience Treatment group: N/A Duration: duration of the EEG data file in seconds EEG sample rate: the sampling rate of the EEG in Hertz Number of EEG channels: the number of EEG signals in this sample Last sleep stage: the scored sleep stage of the final epoch in the sample Has EOG: whether EOG is included in the sample (1 = yes; 0 = no) Has EMG: whether EMG is included in the sample (1 = yes; 0 = no) Has ECG: whether ECG is included in the sample (1 = yes; 0 = no) Proportion artifacts: N/A Time of awakening: time when this sample’s PSG ends Subject age: age of the subject Subject sex: sex of the subject (key: 0 = male, 1 = female; 2 = other) Subject healthy: whether subject is from a relatively healthy population (key: 0 = no; 1 = yes) Has more data: whether this sample has more data in the form of files under the /Data directory other than the /Data/PSG directory (key: 0=no, 1=yes) Remarks: The first number refers to the number of experimental night in the sleep lab for the subject, the second number refer to the number of awakening during that night (e.g., 1_3 refers to Night 1, Awakening 3). Remarks can also include other important information regarding this subject or data file. In the "Ratings.csv" file, the following information is presented: Filename: in the format /Data/PSG/casexx_syy DreamReport_Wordcount: total number of dream-related words minus utterances, fillers, repetitions, corrections, waking commentaries ER = external ratings of emotions expressed in dream reports, conducted by two blind judges using the Finnish version of the modified Differential Emotions Scale (mDES; Fredrickson, 2013); the number refers to the frequency of occurrence of the emotion item in the dream report SR = self-ratings of emotions experienced in the preceding dream using the mDES, conducted by participants themselves upon awakening and after having reported the dream; each item was rated on the scale from 0 = not at all to 4 = extremely much PA = positive emotion/affect item of mDES NA = negative emotion/affect item of mDES The following are the 10 positive and 10 negative emotion/affect items of the mDES scale: PA1 - Amused_Funloving_Giggly PA2 - Awe_Wonder_Amazement PA3 - Grateful_Appreciative_Thankful PA4 - Hopeful_Optimistic_Encouraged PA5 - Inspired_Uplifted_Elevated PA6 - Interested_Alert_Curious PA7 - Joyful_Glad_Happy PA8 - Love_Closeness_Trust PA9 - Proud_Confident_Selfassured PA10 - Serene_Content_Peaceful NA1 - Angry_Irritated_Annoyed NA2 - Ashamed_Humiliated_Disgraced NA3 - Contemptuous_Scornful_DIsdainful NA4 - Disgust_Distaste_Revulsion NA5 - Embarrassed_Selfconscious_Blushing NA6 - Guilty_Repetant_Blameworthy NA7 - Hate_Distrust_Suspicion NA8 - Sad_Downhearted_Unhappy NA9 - Scared_Fearful_Afraid NA10 - Stressed_Nervous_Overwhelmed ER_InferredExpressed = whether the emotion was directly expressed in the dream report or could be inferred from the behaviour of the dream self (key: 1 = expressed, 2 = inferred, 3 = both) Remarks: any remarks regarding this data file --Treatment group codes-- N/A ---Experimental description--- A full description of the materials and methods can be found in the following article: Sikka, P., Revonsuo, A., Noreika, V., & Valli, K. (2019). EEG frontal alpha asymmetry and dream affect: Alpha oscillations over the right frontal cortex during REM sleep and presleep wakefulness predict anger in REM sleep dreams. Journal of Neuroscience, 39(24): 4775-4784. https://doi.org/10.1523/JNEUROSCI.2884-18.2019 Participants: Healthy, not using medication, right-handed, native Finnish speakers, with good sleep quality (score ≤ 5 on the Pittsburgh Sleep Quality Index; Buysse et al., 1989). Experimental design and procedure: For a Figure displaying the experimental procedure, see Sikka et al. (2019, p. 4777). Participants spent 2 nights (separated by a week) in the sleep laboratory. In the evening, participants arrived in the laboratory 2h before their usual bedtime. First, participants were instructed about the procedure of the study and EEG electrodes attached to their scalp. Next, participants' waking state resting EEG was recorded (8 x 1 min; 10:30pm-12:00am) and they rated their current waking affective state using the Finnish version of the modified Differential Emotions Scale (fmDES, Fredickrson, 2013). Participants were then allowed to fall asleep. Sleep stages were monitored and scored visually (Rechtschaffen and Kales, 1968; Iber et al., 2007). Every time REM sleep had lasted continuously for 5 min, and was in a phasic stage, a tone signal was used to awaken the participants. Upon awakening, participants provided an oral dream report: first, they reported the last image they had in mind just before awakening, followed by a detailed report of the whole dream. Next, participants rated their affective experiences in the preceding dream by filling in the fmDES electronically using a mouse and a computer screen above the bed. In case the participants reported ‘‘no dreams’’, researchers asked whether they had not had a dream or they felt like they had had a dream but could not recall any specific content (i.e., ‘white dream’). In these two cases fmDES was not filled in. Participants were then allowed to continue their sleep. This procedure was repeated throughout the night until the final morning awakening (scheduled between 5:30 A.M. to 8:30 A.M.). Upon final awakening, and after having reported and rated the last dream, participants were asked to lie in bed but stay awake. Similar to the evening, waking state resting (morning baseline) EEG was then recorded for 8 min, followed by participants’ ratings of their current waking state affect using the fmDES. --DREAM categorization procedure-- Dream experiences = participants remembered having a dream and were able to report at least some of its content. Without recall ("white dream" = participants felt like they had had a dream but could not recall any specific content. No recall = participants reported not having had any dream experiences. ---Technical details--- N/A --Data acquisition-- EEG was recorded using 24 single Ag/AgCl electrodes (placed according to standard 10/10 system). Additionally, 4 EOG electrodes were used to record eye movements and an EMG electrode (placed on the chin) was used to record muscle activity. All electrodes (except the bipolar EOG and EMG electrodes) were references to the right mastoid. The ground electrode was placed on the forehead. EEG signal was amplified (SynAmps model 5083), notch-filtered at 50 Hz, digitized at 500 Hz, and recorded with Neuroscan equipment and software. All impedances were kept <5 kΩ. --Data preprocessing-- Data has not been preprocessed.

This is a ZIP archive of a dataset package template for the Dream EEG and Mentation (DREAM) database. It embodies the minimal, basic directory structure and files required of a DREAM dataset package.

Version 0.5.0

DREAM database project page: https://bridges.monash.edu/projects/The_Dream_EEG_and_Mentation_DREAM_database/158987

Abstract

The main aim of this study is to evaluate the impact and effectiveness of the scale up of the DREAMS HIV prevention package of biological, behavioural and social interventions in reducing HIV incidence in adolescent girls and young women residing in the uMkhanyakude district of KwaZulu-Natal. To achieve this aim, the changes in different outcomes will be assessed over time. The primary outcome will be HIV incidence and other key secondary outcomes will include knowledge of own HIV status, sexual debut, HSV-2, number of sexual partners, age-disparity with sexual partners, ever been pregnant, condom use, unmet need for contraception, transactional sex, education (remaining in school) and experiences of violence.

Geographic coverage

Demographic surveillance area of the Africa Health Research Institute; KwaZulu-Natal, uMkhanyakude district.

Analysis unit

Individual

Universe

Closed cohorts of 800 AGYW will be followed prospectively at three time points over the two-year study period - baseline, 12 months and 24 months - at points closest aligned with periods before, during and after DREAMS implementation. In ACDIS cohorts of 400 girls aged 13-17 years and 400 young women aged 14-23 years will undergo informed consent, recruited undergo a baseline questionnaire and provide dry blood spots for HSV2 at the same time they provide a sample for the HIV testing in the surveillance and then reviewed annually for the next two years.

Kind of data

Longitudinal survey data

Sampling procedure

Adolescent girls and young women aged 14-23 years who were residents in the demographic surveillance area of the Africa Health Research Institute. A total of 3013 participants were randomly selected to obtain a target sample size of 800 after 2 years of follow-up, allowing for 40% non-contact/loss-to-follow-up. Sampling was stratified by age group and area (week-blocks).

Cleaning operations

All data will be managed using electronic data management tools. The data management system for these will be based on REDCap (research electronic data capture) developed at Vanderbilt University. The REDCap database resides within a single MySQL database server within a secure server cluster at the AHRI. Survey data are synchronised by the REDCap application from the mobile device to a central MySQL server. Access control is managed through Microsoft Active Directory with minimum password complexity and compulsory password change policies.

This dataset mainly provides information on the Dream of the Round Picture Book Database, including the source of the collection, the title of the picture book, author, language, theme, and appropriate reading age, for public reference to the relevant information.

This dataset contains the predicted prices of the asset all degens have dreams over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Dreams Furnishings Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.