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50 results for “life events”
Duhumbi life events
<p>Here are a few photo files displaying Duhumbi the ancient Duhumbi burial practice. Other life-events, including a traditional marriage, are slated for recording at a later moment, but described here and in the grammar. The community has hitherto resisted recording of the practice of cutting up the corpse and disposing it in the river. The Duhumbi people traditionally consider three main life events: birth, marriage and death. There are no special coming-of-age, adulthood ceremonies, engagements and the like. </p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
A Twitter dataset (with labels) for Life-Event detection
<p>The file contains an anonymized version of the dataset collected in the framework of the “Tsundoku” project financed by the Autonomous Province of Trento according to the province law 13 of December 1999, n. 6 (and subsequent modifications), art. 5. - “Aids for promoting research and development”, financing approved with APIAE manager’s provision n. 691. The purpose of this project is training a Deep Learning (DL) model capable of detecting the occurrence of a so-called Life Event - a wedding and/or the birth of a child - in a person’s life on the basis of the contents she shared on social media (Twitter, in this case). </p> <p>More precisely, the dataset consists of the most recent tweets - up to approximately 3200 for each account - of 8 Italian and 27 English-speaking users (all of them randomly picked), totalling 74722 tweets, 20302 written in Italian and 54420 in English.</p> <p>For each user (labelled as ‘user_<em>x</em>’ with <em>x</em> an integer number between 1 and 35), the file includes the language her tweets are written in as well as the list of said tweets. For every one of the latter, the information made available includes the text of the tweet (appropriately modified as explained below), its length, the number of hashtags and of user mentions it featured, the number of retweets and of likes it received, two Boolean flags (“True” or “False”) assessing whether it was a quote or a reply and a label (‘birth’, ‘wedding’ and ‘not Life-Event-related’, depending on whether the tweet refers to a birth/wedding experienced by the user or not). With respect to the label, it is worth stressing that a tweet was labelled as ‘birth’/‘wedding’ only if the event “actively” involved the user (thus, a tweet reading “Today I get married” is labelled as ‘wedding’, while the label ‘not Life-Event-related’ is associated with ‘Today my sister gets married’) and if the tweet is <strong>by itself</strong> unambiguous (in other words, a tweet reading ‘My wife is pregnant’ is labelled as ‘birth’, while ‘Josephine is pregnant’ does not, since the tweet alone does not allow to determine who Josephine exactly is - this could perhaps be inferred from other tweets but this kind of contextualization is very hard to be carried out and was out of the scope of this project.).</p> <p>In order to comply with GDPR, each one of the texts included in the file was obtained from the original text after taking the following anonymization steps:</p> <p> </p> <ul> <li> <p>every web link got replaced by the ‘WEBLINK’ string;</p> </li> <li> <p>every mention to another Twitter user (for instance, “@joedoe”) got replaced by the “OTHERUSER” string;</p> </li> <li> <p>every hashtag got replaced by the “HASHTAG” string;</p> </li> <li> <p>every name and surname detected by the spaCy library (see <a href="https://spacy.io/">https://spacy.io</a> for more info) got replaced by the “NAME/SURNAME” string;</p> </li> <li> <p>10% of the available words got randomly picked and erased. </p> </li> </ul> <p> </p> <p> </p>
An event-based precipitation dataset with life cycle evolution using resilient algorithms
<p>The dataset covers eastern Asia at a temporal range of April to June 2016-2020. We identified initial rain clusters (RCs) from the Global Precipitation Measurement 2ADPR dataset and Mesoscale Convective Systems (MCSs) from the Himawari-8 Advanced Himawari Image gridded product. Based on the contours of the initial RCs and MCSs, we then carried out a series of resilient processes, including filtration, segmentation, and consolidation, to obtain the final RCs. The final RCs had a one-to-one correspondence with the relevant MCS. We extracted the RC area, central location, average radar reflectivity profile, average droplet size distribution profile and other precipitation information from the final RCs and retrieved the life cycle evolution of the MCS area, location, and cloud-top brightness temperature from the corresponding MCSs and tracking algorithms. This dataset facilitates studies of the life cycle evolution of precipitation and provides a good foundation for convection parameterizations in precipitation simulations.</p>
Life events on Twitter
<p>This dataset contains tweets about two types of life event: weddings and births of a child.<br> It is build very unbalanced with the purpose to simulate a social network timeline, in this case a Twitter account. In fact, on someone's timeline is hard to find contents about user's personal wedding or child births.</p> <p>This dataset contains 5836 tweets, taken from 44 different accounts: 161 about wedding, 135 about a birth of a child.</p> <p>It has two colums:</p> <ul> <li>TweetID, which contains the ID of the tweet</li> <li>Label, which contains the classification. Possible values: <ul> <li>WEDDING if the post is about a wedding</li> <li>BIRTH if the post is about a birth of a child</li> <li>NONE if the post is neither about a wedding or a birth of a child</li> </ul> </li> </ul>
Data from paper: "Life cycle of bamboo in the southwestern Amazon and its relation to fire events".
<p>This is the dataset from the paper "Life cycle of bamboo in the southwestern Amazon and its relation to fire events".</p> <p> </p> <p>It contains:</p> <p>- The processed MODIS (MAIAC) time series for the southwest Amazon, already processed and ready to use for the modeling (files such as bamboo_ts_annual_2000-2017_band[...]). MODIS (MAIAC) composites for south america is not provided here because of its huge file size (contact ricds@hotmail.com).</p> <p>- Final bamboo die-off data from 2001-2017 using the simple bilinear model (combination from band 2 and 5, p-value < 0.001). The data used for Figure S3. File: "Theoric_death_merged_band2_band5.tif"</p> <p>- Bamboo die-off detected from 2001-2017 using the simple bilinear model (files Theoric_death_year_band2 and Theoric_death_year_band5, and Theoric_pvalue_band2 and Theoric_pvalue_band5). To obtain the same map as in the paper, must apply the p < 0.001 over the death year map.</p> <p>- Bamboo spatial distribution obtained by the die-off detection and live detection. In this map, values equal to 0, 1 and 2 correspond to non-bamboo, live bamboo and dead bamboo forests.</p> <p>- Bamboo die-off predictions from 2000-2028 using the empirical curves (files Empirical_death_year_band2_curves2 and Empirical_death_year_band5_curves2, and Empirical_p_value_band5_curves2 and Empirical_p_value_band5_curves2). To obtain the same map as in the paper, must apply the p < 0.001 over the death year map.</p> <p> </p> <p>More information contact Ricardo Dalagnol (ricds@hotmail.com).</p>
Figure 1 in Impact of warm weather events on prolongation of the life cycle of Stomaphis Walker (Hemiptera, Aphididae, Lachninae)
Figure 1. Apterous female of Stomaphis sp. collected on 05.01.2014 (a); oviparous female of S. graffii collected on 01.03.2014 (b) (arrow indicates large subgenital plate, typical of oviparous females).
Linked collectors and determiners for: First observations on the life cycle and mass eclosion events in a mantis fly (Family Mantispidae) in the subfamily Drepanicinae.
Natural history specimen data linked to collectors and determiners held within, "First observations on the life cycle and mass eclosion events in a mantis fly (Family Mantispidae) in the subfamily Drepanicinae". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/07029e9e-48af-4335-a3cd-3130a372a562">https://bionomia.net/dataset/07029e9e-48af-4335-a3cd-3130a372a562</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/07029e9e-48af-4335-a3cd-3130a372a562">https://gbif.org/dataset/07029e9e-48af-4335-a3cd-3130a372a562</a>. Formatted as a Frictionless Data package.
Data from: Revised age estimates for Northern Resident killer whales (Orcinus orca) based on observed life-history events and demographic discounting
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Real-Life Indoor Sound Event Dataset (ReaLISED) for Sound Event Classification (SEC)
<p>The Real-Life Indoor Sound Event Dataset (ReaLISED) offers the scientific community the possibility of testing Sound Event Classification (SEC) algorithms with new real indoor audio event recordings. The full set is made up of 2479 sound recordings of 18 events. The 18 event classes are the following: beater, cooking, cupboard/wardrobe, dishwasher, door, drawer, furniture movement, microwave, object falling, smoke extractor, speech, switch, television, vacuum cleaner, walking, washing machine, water tap, and window. There are 2479 clips of isolated sounds, which result in 3624.51 seconds. The number of events in each class is between 104 for the "Window" class and 190 for the “Speech” class, with a mean value of 138 events and a standard deviation of 25.</p> <p>Four Olympus LS-100 recorders were used. The sampling frequency was set to 44.1 kHz and 24 bits per sample. The stereo mode was used, and a medium sensitivity of the microphone was set. The distance between the recorder and the sound source was set to approximately 30-40 cm.</p> <p>Apart from the labels related to the class of event, extra information for each recording is provided in order to be exploited if necessary in the future, with other research purposes. This extra information completes the description of the sound source.</p> <p>The dataset is introduced to the scientific community by providing all the .flac files which composed it. The name of the files is built with 5 pieces of information, separated with underscores (“_”), with the format “abc_123_45_67_8.flac′′:</p> <ul> <li> <p>“abc”: the first three letters indicate the source that produces the sound. This segment can take 18 different values: ‘bea’ (beater), ‘coo’ (cooking), ‘cup’ (cupboard/wardrobe), ‘dis’ (dishwasher), ‘doo’ (door), ‘dra’ (drawer), ‘fur’ (furniture movement), ‘mic’ (microwave), ‘obj’ (object falling), ‘smo’ (smoke extractor), ‘spe’ (speech), ‘swi’ (switch), ‘tel’ (television), ‘vac’ (vacuum cleaner), ‘wal’ (walking), ‘was’ (washing machine), ‘wat’ (water tap), win’ (window).</p> </li> <li> <p>“123”: this set of digits identifies the event among the number of events produced by the source identified with “abc”. This segment can take all the values between ‘001’ and ‘190’, which is the maximum number of events of a particular class we can find in the dataset (speech).</p> </li> <li> <p>“45”: this set of digits identifies the action that produce the sound. This segment can take 11 different values: ‘01’ (close), ‘02’ (open), ‘03’ (throw), ‘04’ (turn on), ‘05’ (turn off), ‘06’ (move), ‘07’ (plug), ‘08’ (unplug), ‘09’ (raise), ‘10’ (lower), and ‘00’ (there is no information about the action).</p> </li> <li> <p>“67”: this set of digits identifies the material the sound source is made of. This segment can take 14 different values: ’01’ (wood), ’02’ (glass), ’03’ (metal), ’04’ (plastic), ’05’ (ceramic), ’06’ (synthetic), ’07’ (cardboard), ’08’ (marble), ’09’ (floating platform), ’10' (platelet), ’11’ (wicker), ’12’ (carpet), ’13’ (medium-density fibreboard MDF), and ’00’ (there is no information about the material).</p> </li> <li> <p>“8”: the last digit gives approximate information about the intensity of the recorded sound. It can take 4 different values: ’1’ (low intensity), ’2’ (medium intensity), ’3’ (high intensity), ’0’ (there ir no information about the intensity).</p> <p>For clarity, some examples of audio file names with this code are shown hereunder:</p> </li> <li> <p>“doo_040_02_00_3.flac” is the name of the 40th file in the Door class, described as “opening a door of unknown material with high intensity”.</p> </li> <li> <p>“fur_058_06_01_2.flac” is the name of the 58th file in the furniture movement class, described as “moving a wooden furniture with medium intensity”.</p> </li> <li> <p>“vac_001_00_00_0.flac” is the name of the 1st audio file in the vacuum cleaner class, described as “using the vacuum cleaner, without information about the action, neither the material or the intensity”.</p> </li> </ul>
Taking up and terminating leisure-time physical activity over the life course: The role of life events in the familial and occupational life domains
<p>Dataset used for the analyses in the following publication:</p> <p>Lenze, L., Klostermann, C., Lamprecht, M. & Nagel, S. (2021). Taking up and terminating leisure-time physical activity over the life course: The role of life events in the familial and occupational life domains. <em>International Journal of Environmental Research and Public Health</em>, <em>18</em>, 9809. https://doi.org/10.3390/ijerph18189809</p>
Reproductive phenology is a repeatable, heritable trait linked to the timing of other life history events in a migratory marine predator
<p>Population-level shifts in reproductive phenology in response to environmental change are common, but whether individual-level responses are modified by demographic and genetic factors remains less well understood. We used mixed models to quantify how reproductive timing varied across 1,755 female southern elephant seals (<em>Mirounga</em> <em>leonina</em>) breeding at Marion Island in the Southern Ocean (1989–2019) and to identify the factors that correlate with phenological shifts within- and between individuals. We found strong support for covariation in the timing of breeding arrival dates and the timing of the preceding moult. Breeding arrival dates were more repeatable at the individual-level, as compared to the population-level, even after accounting for individual traits (wean date as a pup, age and breeding experience) associated with phenological variability. Mother-daughter similarities in breeding phenology were also evident, indicating that additive genetic effects may contribute to between-individual variation in breeding phenology. Over 30 years, elephant seal phenology did not change towards earlier or later dates, and we found no correlation between annual fluctuations in phenology and indices of environmental variation. Our results show how maternal genetic (or non-genetic) effects, individual traits and linkages between cyclical life-history events can drive within- and between-individual variation in reproductive phenology.</p>
QBSAfe: A Novel Approach to Diabetes Management Focused on Quality of Life, Burden of Treatment, Social Integration and Avoidance of Future Events
ClinicalTrials.gov study NCT04514523. IPD Sharing: NO. Countries: 1. Publications: 1.
Reproductive phenology is a repeatable, heritable trait linked to the timing of other life history events in a migratory marine predator
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Can time heal anything? Exposure to traumatic events, autobiographical memory, and the quality of life among older adults. The role of time perspective.
<p><span><span> </span><strong><span>Objectives</span></strong>. This study aimed to investigate the relationship between autobiographical memory, exposure to traumatic events, and quality of life, and also whether temporal perspective represents a significant mediator of these relations, in a group of elderly people. <strong><span>Method</span></strong>. The study was conducted using a sample of 362 participants (<em>M</em>age = 68.35, <em>SD</em> = 6.67; 65.5% women and 34.5% men). They completed the questionnaires for measuring quality of life, exposure to traumatic events, autobiographical memory, and time perspective. <strong><span>Results</span></strong>. The results indicated that direct exposure to major traumatic life events negatively predicted quality of life, while autobiographical memory was not a predictor for quality of life. Time perspective (i.e., negative past, negative future) partially mediated the relationship between exposure to traumatic life events and quality of life. At the same time, the temporal perspective mediated the relationship between autobiographical memory and quality of life. <strong><span>Discussion</span></strong><span>. How people manage their lives after exposure to traumatic events should be of particular interest to society as a whole and it is necessary to take into account various factors related to quality of life, especially in the case of elderly people. To improve quality of life, interventions can address the physical and psychological dimensions, as well as social interactions and the quality of the environment in which people live.</span></span></p>
TUT Sound Events 2018 - Ambisonic, Reverberant and Real-life Impulse Response Dataset
<p><strong>Tampere University of Technology (TUT) Sound Events 2018 - Ambisonic, Reverberant and Real-life Impulse Response Dataset</strong></p> <p>This dataset consists of real-life first order Ambisonic (FOA) format recordings with stationary point sources each associated with a spatial coordinate. The dataset was generated by collecting impulse responses (IR) from a real environment using the Eigenmike spherical microphone array. The measurement was done by slowly moving a Genelec G Two loudspeaker continuously playing<br> a maximum length sequence around the array in circular trajectory in one elevation at a time. The playback volume was set to be 30 dB greater than the ambient sound level. The recording was done in a corridor inside the university with classrooms around it during work hours.The IRs were collected at elevations −40 to 40 with 10-degree increments at 1 m from the Eigenmike and at elevations −20 to 20 with 10-degree increments at 2 m. </p> <p>The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), spatial location in azimuth and elevation angles (in degrees), and distance from the microphone (in meters).</p> <p>The isolated sound events were taken from the <a href="https://serv.cusp.nyu.edu/projects/urbansounddataset/urbansound8k.html">urbansound8k dataset</a>. This dataset consists of 10 sound event classes such as air_conditioner, car_horn, children_playing, dog_bark, drilling, enginge_idling, gun_shot, jackhammer, siren, and street_music. We do not consider the air_conditioner and children_playing sound events. Further, we only include the sound event examples marked as foreground in the dataset. We used the splits 1, 8 and 9 provided in the urbansound8k as the three CV splits. These splits were chosen as they had a good number of examples for all the chosen sound event classes after selecting only the foreground examples. During the sound scene synthesis, we randomly chose a sound event example and associated it with a random distance among the collected ones, azimuth and elevation angle. The sound event example was then convolved with the respective IR for the given distance, azimuth and elevation to spatially position it.</p> <p>The metadata.zip folder consists of the license and the metadata for the complete dataset. The rest of the nine zip files consists dataset for given split and overlap. For example, the wav_ov3_split1_30db.zip file consists of training and testing recordings for the case of maximum three temporally overlapping sound events (ov3) for the first cross-validation split (split1). Within each audio folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Sound event localization and detection of overlapping sources using convolutional recurrent neural network</a>' work.</p> <p><strong>Data collector (s): </strong>Fagerlund, Eemi; Koskimies, Aino</p> <p> </p>
Dataset from: "Global research trends of BRUE (brief resolved unexplained event) or formerly ALTE (apparent life-threatening event): A comprehensive visualization and bibliometric analysis from 1988 to 2024"
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Lifelong traumatic events, social support, and health-related quality of life among older adults
<p><strong>Purpose</strong>. The present study aimed to investigate the relation between lifelong exposure to traumatic life events and health-related quality of life in a sample of older people. We also assessed the moderating role of social support in the relation between traumatic life events exposure and quality of life. <strong>Method</strong>. A sample of 172 participants (<em>M</em>age = 68.81, <em>SD</em> = 7.15; 68.6% female and 31.4% male) was involved in this study. The participants completed scales measuring lifelong exposure to traumatic events, social support, and health-related quality of life. <strong>Results</strong>. The results showed that lifelong exposure to traumatic events was negatively related to physical functioning, emotional well-being, social functioning, and general health perception. It was also positively related to body pain, role limitations due to physical health problems and role limitations due to personal or emotional problems. Moreover, social support moderated the relation between traumatic life events exposure and dimensions of health-related quality of life. High social support from family contributed to reduced role limitations following trauma exposure, whereas more social support from friends led to poorer emotional well-being. <strong>Discussion</strong>. Geriatric services could identify and implement adequate measured to provide social support and to improve different dimensions of quality of life among older adults.</p>
Relationship of Genes and Life Events to Blood Pressure
ClinicalTrials.gov study NCT00327431. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Psychotherapy on Quality of Life and Recurrence of Events in Patients With Recurrent Vasovagal Syncope: A Randomized Pilot Study
ClinicalTrials.gov study NCT04252729. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Extreme climate events and individual heterogeneity shape life-history traits and population dynamics
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OpenNeuro
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