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6,002 results for “sleep”
Fig. 3 in Sleeping site selection in two Asian viverrids: effects of predation risk, resource access and habitat characteristics
Fig. 3. Use of sleeping sites within different forest types. Percentage of different forest types used (denoted as U) for sleeping sites versus forest types available (A) for two binturongs (Arctictis binturong) and three masked palm civets (Paguma larvata). Forest types are: semi-evergreen forest (SEF), mixed deciduous forest (MDF), and dry dipterocarp forest (DDF). Numbers in parenthesis after individual animals represent the number of sleeping sites used in the analysis, excluding reused sites.
Fig. 2 in Sleeping site selection in two Asian viverrids: effects of predation risk, resource access and habitat characteristics
Fig. 2. Use of vertical strata for sleeping sites. Percentage use of different vertical strata of sleeping sites by five radio-collared viverrids (two binturongs Arctictis binturong and three masked palm civets Paguma larvata). Strata are: Above canopy, Canopy, and Sub-canopy. Numbers in parenthesis represent number of sleeping sites where animals were directly observed, excluding reused sites.
Data from: Ecological and social pressures interfere with homeostatic sleep regulation in the wild
<p>Sleep is fundamental to the health and fitness of all animals. The physiological importance of sleep is underscored by the central role of homeostasis in determining sleep investment – following periods of sleep deprivation, individuals experience longer and more intense sleep bouts. Yet, most sleep research has been conducted in highly controlled settings, removed from evolutionarily-relevant contexts that may hinder the maintenance of sleep homeostasis. Using tri-axial accelerometry and GPS to track the sleep patterns of a group of wild baboons (<i>Papio anubis</i>), we found that ecological and social pressures indeed interfere with homeostatic sleep regulation. Baboons sacrificed time spent sleeping when in less familiar locations and when sleeping in proximity to more group-mates, regardless of how long they had slept the prior night or how much they had physically exerted themselves the preceding day. Further, they did not appear to compensate for lost sleep via more intense sleep bouts. We found that the collective dynamics characteristic of social animal groups persist into the sleep period, as baboons exhibited synchronized patterns of waking throughout the night, particularly with nearby group-mates. Thus, for animals whose fitness depends critically on avoiding predation and developing social relationships, maintaining sleep homeostasis may be only secondary to remaining vigilant when sleeping in risky habitats and interacting with group-mates during the night. Our results highlight the importance of studying sleep in ecologically relevant contexts, where the adaptive function of sleep patterns directly reflects the complex trade-offs that have guided its evolution.</p>
Deep Representation Learning of Physical Activity and Sleep Patterns During Pregnancy Identifies post-hoc Inferences Associated with Prematurity
<p><strong>Running title</strong>: series2signal gestational age "clock" for pregnancy monitoring</p> <p><strong>Summary</strong>: </p> <p>Preterm birth (PTB) is the leading cause of infant mortality globally. While research has focused on the development of predictive models for PTB, cost-effective interventions have remained understudied. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations. However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy that decays over time. In this study, we use physical activity data collected using a wearable device comprising over 181, 944 hours of data across N = 1, 083 patients. Using a new state-of-the art deep learning time-series classification architecture, we first develop a ”clock” of healthy dynamics in physical activity patterns during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also developed a novel interpretability algorithm that integrates unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and static clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy based on measures of physical activity and sleep.</p> <p>Importantly, we found that deviations from this normal ”clock” of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e − 67) and when our model overestimates GA, there are 1.44 times (P = 2.82e − 39) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043), indicating that our model assigns a more advanced GA when an individual’s daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e − 21). Combining prediction with interpretability allows us to robustly signal when activity behaviors increase or decrease the likelihood of preterm birth and advocates for the future development of clinical decision support through passive monitoring and suggestions around exercise habits and sleep patterns, which are easily implemented in low- and middle-income countries (LMICs). Beyond this particular application, the presented pipeline can be used to analyze high-fidelity time-series data in other translational studies utilizing wearable devices.</p> <p> </p> <p><strong>Data description (brief)</strong>: the raw wearables data is available as .mtn files with the GA encoded in the filename after the underscore. The processed data with sleep annotations can be loaded using the pickle module for serialized objects in python. See https://github.com/nealgravindra/wearables for examples.</p>
APNIWAVE: A Dataset Collected Using a Radar-Based Sleep-Apnea Screening Device for Use at Home
<p>The "APNIWAVE: A Dataset Collected Using a Radar-Based Sleep-Apnea Screening Device for Use at Home" dataset was created by collecting data from 11 patients with Obstructive Sleep Apnea and Hypopnea Syndrome (OSAHS) using an UWB sensor, which was the X4M200 UWB radar sensor by Novelda and a Raspberry Pi 3 device, for the purposes of APNIWAVE project (EIT Health RIS Scheme, Project ID: Project ID 2021-RIS_Innovation-066). The dataset comprises 1,011 selected samples of 10 sec each (a total of about 3 hours), from three main events that are the most frequent during sleep:</p> <ul> <li><em>Normal breathing</em> – periodic movement of the patient’s torso as a result of the inhale and exhale. (label in "<em>Labels Three Events.csv</em>" file equal to 0)</li> <li><em>Apnea event</em> – based on the Apnea type, e.g. central Apnea or hypopnea, no torso movement or a periodic torso movement but weaker than normal breathing, respectively. (label in "<em>Labels Three Events.csv</em>" file equal to 1)</li> <li><em>Other event</em> – events that may occur during data collection, such as change of posture, leaving the bedroom etc. (label in "<em>Labels Three Events.csv</em>" file equal to 2)</li> </ul> <p>Data from five distances are included for each 10 sec sample. More specifically, the central distance is the distance of the user from the radar; on top of that we added two more discrete distance steps (as designated by the radar range resolution) in front of and another two behind the user, thus totaling five distances from the radar. These five distances were selected from a set of 165 distance steps (from 0.5m to 9 m) considering a distance step of about 0.05144 m and a sampling rate of 17 Hz. As a consequence, the size of each sample (of 10 sec duration) is 5 x 170, which leads to a total size of the uploaded "<em>Multiple Distances Three Events.csv</em>" file of 5 x 171870.</p>
Multi-stage sleep classification using photoplethysmographic sensor
<p>The conventional approach to monitoring sleep stages requires placing multiple sensors on the patients, which is inconvenient for long-term monitoring and requires expert support. We propose a single sensor Photoplethysmographic (PPG) based automated multi-stage sleep classification. This experimental study recorded the PPG during the entire night's sleep of ten patients. Data analysis was performed to obtain 82 features from the recordings, which were then classified against the sleep stages. The classification results using SVM with the polynomial kernel gave the overall accuracy of 84.66%, 79.62%, and 72.23% for two, three, and four-stage sleep classification. These results show that using only PPG; it is possible to conduct sleep stage monitoring. These findings open the opportunities for PPG-based wearable solutions for home-based automated sleep monitoring.</p>
Dataset used for upcoming paper: The effects of mild disturbances on sleep behaviour in laying hens
<p>2 Excel .csv files for statistical analysis using R-Studio. The first is a constants file for temporal coding, the second is the raw data.</p>
101-nights -sleep and dream study night 002
<p>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. </p> <p>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. </p> <p>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.</p> <p>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.</p> <p>the project produced four immediate results:</p> <p>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).<br> 2. the logs of the words triggered by the computer system, each night with their exact time.<br> 3. Nathalie’s daily dream diary entries.<br> 4. day-by-day activity.</p>
101-nights -sleep and dream study night 016
<p>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. </p> <p>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. </p> <p>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.</p> <p>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.</p> <p>the project produced four immediate results:</p> <p>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).<br> 2. the audio logs of the words triggered by the computer system, each night with their exact time.<br> 3. Nathalie’s daily dream diary entries.<br> 4. day-by-day activity.</p>
101-nights -sleep and dream study night 015
<p>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. </p> <p>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. </p> <p>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.</p> <p>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.</p> <p>the project produced four immediate results:</p> <p>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).<br> 2. the audio logs of the words triggered by the computer system, each night with their exact time.<br> 3. Nathalie’s daily dream diary entries.<br> 4. day-by-day activity.</p>
101-nights -sleep and dream study night 014
<p>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. </p> <p>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. </p> <p>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.</p> <p>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.</p> <p>the project produced four immediate results:</p> <p>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).<br> 2. the audio logs of the words triggered by the computer system, each night with their exact time.<br> 3. Nathalie’s daily dream diary entries.<br> 4. day-by-day activity.</p>
101-nights -sleep and dream study night 013
<p>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. </p> <p>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. </p> <p>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.</p> <p>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.</p> <p>the project produced four immediate results:</p> <p>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).<br> 2. the audio logs of the words triggered by the computer system, each night with their exact time.<br> 3. Nathalie’s daily dream diary entries.<br> 4. day-by-day activity.</p>
101-nights -sleep and dream study night 012
<p>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. </p> <p>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. </p> <p>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.</p> <p>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.</p> <p>the project produced four immediate results:</p> <p>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).<br> 2. the audio logs of the words triggered by the computer system, each night with their exact time.<br> 3. Nathalie’s daily dream diary entries.<br> 4. day-by-day activity.</p>
101-nights -sleep and dream study night 011
<p>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. </p> <p>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. </p> <p>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.</p> <p>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.</p> <p>the project produced four immediate results:</p> <p>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).<br> 2. the audio logs of the words triggered by the computer system, each night with their exact time.<br> 3. Nathalie’s daily dream diary entries.<br> 4. day-by-day activity.</p>
101-nights -sleep and dream study night 009
<p>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. </p> <p>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. </p> <p>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.</p> <p>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.</p> <p>the project produced four immediate results:</p> <p>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).<br> 2. the audio logs of the words triggered by the computer system, each night with their exact time.<br> 3. Nathalie’s daily dream diary entries.<br> 4. day-by-day activity.</p>
101-nights -sleep and dream study night 010
<p>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. </p> <p>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. </p> <p>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.</p> <p>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.</p> <p>the project produced four immediate results:</p> <p>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).<br> 2. the audio logs of the words triggered by the computer system, each night with their exact time.<br> 3. Nathalie’s daily dream diary entries.<br> 4. day-by-day activity.</p>
101-nights -sleep and dream study night 006
<p>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. </p> <p>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. </p> <p>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.</p> <p>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.</p> <p>the project produced four immediate results:</p> <p>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).<br> 2. the logs of the words triggered by the computer system, each night with their exact time.<br> 3. Nathalie’s daily dream diary entries.<br> 4. day-by-day activity.</p>
101-nights -sleep and dream study night 023
<p>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. </p> <p>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. </p> <p>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.</p> <p>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.</p> <p>the project produced four immediate results:</p> <p>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).<br> 2. the audio logs of the words triggered by the computer system, each night with their exact time.<br> 3. Nathalie’s daily dream diary entries.<br> 4. day-by-day activity.</p>
101-nights -sleep and dream study night 022
<p>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. </p> <p>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. </p> <p>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.</p> <p>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.</p> <p>the project produced four immediate results:</p> <p>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).<br> 2. the audio logs of the words triggered by the computer system, each night with their exact time.<br> 3. Nathalie’s daily dream diary entries.<br> 4. day-by-day activity.</p>
101-nights -sleep and dream study night 021
<p>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. </p> <p>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. </p> <p>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.</p> <p>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.</p> <p>the project produced four immediate results:</p> <p>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).<br> 2. the audio logs of the words triggered by the computer system, each night with their exact time.<br> 3. Nathalie’s daily dream diary entries.<br> 4. day-by-day activity.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.