dataesr/scientific-paragraphs-categorization huggingface dataset:
Fruit Phenology Fruiting phenology data have been collected at the study site over thirty-three years  (1986 -2018; Tutin and others 2018). The species in this dataset were originally chosen for their importance in ape diet  (Tutin and Fernandez 1993). Fruiting species important to elephants were identified from detailed studies on forest elephant diet at the study site and were found to be highly overlapping with those of ape diets  (White and others 1994). Thus, for thirty-one of the thirty-eight species most important to elephants, we have access to thirtythree years of data. For the remaining seven species, data collection began in 1996 or 2001. Reliable fruiting seasonality can be established with 6 years of data for most species  (Bush and others 2017). All species in this study site reproduce annually, except Psychotria vogeliana, Psidium guineensis and Antidesma vogelianum, which flower and fruit every 6 months  (Bush and others 2017).
d i is the mean density of trees of in species i in that habitat type (forest edge or forest interior) (stems ha -1 ). r i is the mean radius at breast height (cm) of trees in species i. The mean density and mean radius of each species were determined from existing census data for plots in different habitat types across the study area (Table  S1 ). We calculated fruit availability scores using longterm phenology data  rather than phenology recorded exclusively during the study period (May 2016-June 2017; but see Figure  S4  for the comparison between these two datasets). We assume long-term fruit availability patterns will explain elephant density better than those recorded during the study period because we expect elephants to move across the landscape-based largely on memory, as opposed to current smell, sight and sound cues.
Study design and participants Data were drawn from the Three-City Study, a multicentric prospective cohort study carried out in the French cities of Bordeaux, Dijon and Montpellier 32. Communitydwelling persons aged 65 and above were recruited randomly from electoral rolls between 1999 and 2000. Participants were followed up at approximately 2, 4, 7, 10 and 12 years. The study protocol was approved by the ethics committees of the University Hospital of Kremlin-Bicêtre and Sud Mediterranée III (Nîmes). All participants gave written informed consent. At each time-point, participants were administered standardised questionnaires by trained staff with questions on sociodemographic, lifestyle and health characteristics. Patients were asked to report any chronic pathologies from a given list. All drugs used regularly over the past month were recorded and coded according to the Anatomical Therapeutic Chemical Classification (ATC) of the World Health Organisation.
Covariates associated with pre-AD in either sex with p-values < 0. 15 were considered for entry in the multivariate analysis (see Table  S1 ). We further examined the relationship between symptom dimensions and pre-AD stratified by DS and AA use. Finally, we examined individual items categorised as binary variables (score: 0/1+), using Bonferroni correction for multiple testing. Statistical analyses were performed using SAS Enterprise Guide Version 7. 15 (SAS Institute, Inc. Cary, North Carolina).
Sex differences in low versus high DS groups Sex differences in the association between depression and pre-dementia have seldom been examined. Overall, findings are contradictory as to the existence 6,7,12 and direction 10,25 of sex differences in the association between late-life depressive symptoms measured using the CES-D and pre-AD. Conversely, to studies suggesting associations in men but not women  10, 25  , we found in a larger dataset with a greater number of incident AD cases an association in women only. These contradictory findings may be explained by differences in the study populations, for example with respect to age, education, and baseline cognitive functioning. Studies also vary in their methodological approach, focusing on late-life rather than lateonset depression and not always allowing for a diagnosisfree time-lag between assessment and dementia onset  10.
Acknowledgements The  Three-City study  was conducted under a partnership agreement between  Inserm , the  University of Bordeaux Victor Segalen and Sanofi Aventis. The  Fondation pour la Recherche Médicale  funded the preparation and first phase of the study. The  Three-City study  was also supported by the  Caisse Nationale Maladie des Travailleurs Salariées ,  Direction Générale de la Santé ,  Mutuelle Générale de l'Education Nationale (MGEN) ,  Institut de Longévité , the regional governments of Aquitaine, Bourgogne and  Languedoc Roussillon ,  Fondation de France , the  Ministry of Research-Inserm Programme "cohortes and collection of biomedical material ",  Agence Nationale de la Recherche   ANR PNRA 2006  and  LongVie 2007 , and the  "Fondation Plan Alzheimer " ( FCS 2009-2012 ).
Although no adaptation was reported in the Field L using this kind of stimulus set  [100] , the stimuli were broadcast with intervals of at least 300 ms in order to avoid any problem of adaptation between the stimuli. The sequence of stimuli set was determined randomly and then the same sequence was repeated 10 times at each recording site. Spike arrival times were obtained (with a temporal resolution of 0. 1 ms) by thresholding the extra-cellular recordings with a custom-made time-and level-window discriminator  [97]. Single units or small multiunit clusters of 2-4 neurons were recorded in this manner. Since several studies found that analyses resulting from single and multi-units led to similar results  [104, 105] , the data from both types of units were analysed together. The computer that delivered the stimuli also recorded the times of action potentials and displayed on-line rasters of the spike data for the four electrodes simultaneously.
Analyses All videos were recorded with four cameras from different angles. The parrots' choices were directly noted during the experiment. Analyses were conducted in R  [59]  using the package 'lme4'  [60]. We used generalized linear mixed models (GLMM) with a binomial distribution to analyse the frequency of prosocial choices across test conditions. Accordingly, we set the number of prosocial and selfish token choices as the response variable and test conditions (factor: UNI, ALT, YC, ACC, INACC, SFC) as well as reward distribution (factor: equal, unequal), as fixed effects. Individual identity and dyads were entered as random effects. In order to assess potential changes in token choices during the course of a test session, we analysed choices across 10-trial blocks in a separate GLMM including an interaction between blocks (continuous: 1-6) and conditions as fixed effects.
Collected data and definitions The clinical data (including the health record from birth), laboratory data, and skeletal surveys were reviewed by a forensic pediatrician and a pediatric radiologist each with more than 30 years of experience. All living children were examined by the pediatrician author. In accordance with current imaging guidelines for cases of suspected child abuse, each child had a full skeletal survey and an interpretable head CT or MRI  14, 15. All markers for abuse were exhaustively noted, in particular CML and non CML fractures, concurrently identified injuries, previous traumatic injuries, retinal hemorrhages and subdural hematomas (SDH). As forensic medical experts, the authors analyzed all of the written evidence from the police investigations and court hearings. They judged a confession to exist when the perpetrator) admitted a direct causal link between the violence and the child's fracture(s), and described the circumstances and mechanisms of the fracture(s) clearly and consistently over time.
Statistical analyses Potential associations with subdural hematoma (SDH) and with a confession were evaluated with two separate binary logistic regression models. Both univariate and multivariate logistic regression were used. A criterion of p<0. 20 in the univariate analysis was used to select variables for inclusion in the multivariate model. Results were expressed as an odds ratio (OR) and its 95% confidence interval (CI). The adequacy of the model was assessed using the Hosmer-Lemeshow test. In addition, the Fisher exact test was used to analyze potential associations between perpetrator sex and the various circumstances of abuse (diapering, dressing, feeding, etc). All statistical analyses were performed using SAS 9. 4 software. No ethics committee approval was needed, as the data were anonymized and came from a database of forensic experts' findings.
Methods: We performed a systematic review of the studies that assessed food addiction with the YFAS published between January 2014 and July 2017 by searching the electronic databases PsycINFO, MEDLINE, and PsycARTICLES. Results: Sixty publications were included in the analysis. Thirty-three studies examined nonclinical samples and 27 examined clinical samples. All studies used YFAS scoring results to define food addiction. The prevalence of food addiction according to the YFAS varied largely by the studied samples. In general, a higher body mass index and the presence of eating disorders (EDs), especially binge eating disorder (BED), were associated with higher YFAS scores.
Search strategy The electronic databases PsycINFO, MEDLINE, and PsycARTICLES were searched for empirical studies published between January 2014 and July 2017. The following keywords were used: ("yale food addiction scale" OR "YFAS") AND ("food addiction" OR "behavioral addiction" OR "behavioral addiction" OR "eating behavior" OR "eating behavior" OR "obesity" OR "food" OR "eat" OR "feeding behavior" OR "feeding behavior" OR "food preferences" OR "food habits" OR "body mass index" OR "overeating" OR "hyperphagia" OR "substance-related disorders" OR "binge eating" OR "hedonic eating").
Association with Body Mass Index (BMI) Other measures taken simultaneously with the YFAS varied widely. The BMI was most frequently recorded by using self-reported data to calculate the score. The number of YFAS symptoms were positively associated with BMI in a majority  (10)  of the studies that used the original YFAS  [11, 23, 29, 53, 57, 62, 63, 66, [68] [69] [70] 73]  and in 2 studies that used the YFAS 2. 0  [23, 29]. Only three studies did not find this correlation  [30, 47, 56] , although one  [47]  analyzed an obese sample. One study found that the YFAS symptom score was associated with a higher visceral fat level  [46].
Note whether these were applied at the study or individual level i. e. whether eligible participants were included (and ineligible participants excluded) from a study that included a wider population than specified by the review inclusion criteria. The rationale for criteria should be stated. Describe all methods of identifying published and unpublished studies including, as applicable: which bibliographic databases were searched with dates of coverage; details of any hand searching including of conference proceedings; use of study registers and agency or company databases; contact with the original research team and experts in the field; open adverts and surveys. Give the date of last search or elicitation. Present the full electronic search strategy for at least one database, including any limits used, such that it could be repeated.
State the process for determining which studies were eligible for inclusion. Describe how IPD were requested, collected and managed, including any processes for querying and confirming data with investigators. If IPD were not sought from any eligible study, the reason for this should be stated (for each such study). If applicable, describe how any studies for which IPD were not available were dealt with. This should include whether, how and what aggregate data were sought or extracted from study reports and publications (such as extracting data independently in duplicate) and any processes for obtaining and confirming these data with investigators. Describe how the information and variables to be collected were chosen. List and define all study level and participant level data that were sought, including baseline and follow-up information.
If applicable, describe methods of standardising or translating variables within the IPD datasets to ensure common scales or measurements across studies. Describe what aspects of IPD were subject to data checking (such as sequence generation, data consistency and completeness, baseline imbalance) and how this was done. Describe methods used to assess risk of bias in the individual studies and whether this was applied separately for each outcome. If applicable, describe how findings of IPD checking were used to inform the assessment. Report if and how risk of bias assessment was used in any data synthesis. State all treatment comparisons of interests. State all outcomes addressed and define them in detail. State whether they were prespecified for the review and, if applicable, whether they were primary/main or secondary/additional outcomes.
• How studies providing IPD and not providing IPD were analysed together (where applicable). • How missing data within the IPD were dealt with (where applicable). If applicable, describe any methods used to explore variation in effects by study or participant level characteristics (such as estimation of interactions between effect and covariates). State all participant-level characteristics that were analysed as potential effect modifiers, and whether these were pre-specified. Specify any assessment of risk of bias relating to the accumulated body of evidence, including any pertaining to not obtaining IPD for particular studies, outcomes or other variables. For each study, present information on key study and participant characteristics (such as description of interventions, numbers of participants, demographic data, unavailability of outcomes, funding source, and if applicable duration of follow-up).
ACKNOWLEDGEMENTS Louise Penzenstadler ,  Carina Soares , and  Yasser Khazaal  made substantial contributions to the analysis and interpretation of data. Louise Penzenstadler ,  Carina Soares ,  Laurent Karila , and  Yasser Khazaal  were involved in drafting the manuscript or revising it critically for important intellectual content. Louise Penzenstadler ,  Carina Soares ,  Laurent Karila , and  Yasser Khazaal  gave final approval of the version to be published.
In many developed countries, human life expectancy has doubled over the last 180 years from ~40 to ~80 years. Underlying this great advance is a change in how we age, yet our understanding of this change remains limited. Here we present a unique database rich with possibilities to study the human ageing process: the AgeGuess. org database on people's perceived and chronological ages. Perceived age (i. e. how old one looks to others) correlates with biological age, a measure of a person's health condition in comparison to the average of same-aged peers. Determining biological age usually involves elaborate molecular and cellular biomarkers. Using instead perceived age as a biomarker of biological age enables us to collect large amounts of data on biological age through a citizen science project, where people upload pictures of themselves and guess the ages of other people at www.
ageguess. org. It furthermore allows to collect data retrospectively, because people can upload photographs of themselves when they were younger or of their parents and grandparents. We can thus study the temporal variation in the gap between perceived age and chronological age to address questions such as whether we now age slower or delay ageing until older ages. The here presented perceived age data span birth cohorts from the years 1877 to 2014. Since 2012 the database has grown to now contain around 200,000 perceived age guesses. More than 4000 citizen scientists from over 120 countries of origin have uploaded ~5000 facial photographs. We detail how the data are collected, where the data can be downloaded free of charge, and the contained variables. Beyond ageing research, the data present a wealth of possibilities to study how humans guess ages and to use this knowledge for instance in advancing and testing emerging applications of artificial intelligence and deep learning algorithms.
Introduction Global record life expectancy-the highest average life span among the world's populations-has risen steadily by 3 months per year for the last 180 years  [1]. Underlying this exceptional increase is a change in how we age, even though our understanding of this change remains limited. Reaching this understanding is not only a pressing concern for anticipating future changes in life expectancy, a question of high social and economic importance, but also lies at the core of our human self-understanding. Our lives will end with death and most of us have an interest in knowing when this time might come. Here we present a unique open-access database, rich with possibilities for studying ageing: the AgeGuess. org database on people's perceived ages (i. e. how old someone looks to others) and chronological ages.
Here we present a wealth of perceived age data spanning birth cohorts from the years 1877 to 2014. The data were collected by a citizen science project since 2012, where citizens upload facial pictures of individuals with known age and guess ages of other users. The data collection is ongoing. More than 4000 citizen scientists from over 120 countries of origin have uploaded ~5,000 pictures (  Fig 1, Fig 2a). The citizen science project continues to grow steadily (Fig  2b ). Communication by the media and outreach activities on social media (Facebook: www. facebook. com/ageguess. org/, Twitter: @ageguess. org) and in person (e. g. open science days) aim to both recruit more users and to inform the public about the change in how we age. In the following, we introduce how we collect the data via a webpage, and how we recruit further citizen scientists to the project.
The webpage Organisation The webpage at www. ageguess. org is the platform we use to collect the perceived age data. The webpage is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives4. 0 International License. The webpage is hosted in France and has been approved by the Commission nationale de l'informatique et des libertés (CNIL, National Commission on Informatics and Liberty; declaration #1800944v0), France's regulatory body ensuring the application of data privacy law to the collection, storage, and use of personal data. The webpage is built with Drupal (www. drupal. org), a free and open-source content management system (CMS) based on PHP and MySQL. Drupal provides login/logout functionalities and account activation and deletion. While users retain the copyright of their pictures, the AgeGuess project owns the rights to use content on the webpage covered by intellectual property rights, including text, images, graphics, logos, icons, sounds, and software.
Obtaining the AgeGuess data Users with an active AgeGuess account can download the data described in the next section from a repository at www. ageguess. org/download. The AgeGuess project updates the publicly available data every three months. We urge users to read the user agreement and to cite this paper when using the data. We do not publish the photographs open access to respect EU privacy protection law. All data are fully anonymised and any attempt to reveal the identity of users violates AgeGuess regulations.
History and internal organisation In 2012 U. Steiner and D. Misevic started the AgeGuess. org citizen science project. They form the core committee and are responsible for creating and updating protocols for data collection and for the overall infrastructure of the database, as well as for securing funding. They are supported by webpage building and database construction expert, J. Vieillefont, who created and maintains the current version of the webpage and the database. The first fully functional version of AgeGuess. org was coded by Charlotte Le Pesquer. Furthermore, a team of scientific advisors stretching both academic disciplines (e. g. public health) and the industry (e. g. pension providers) helps shape the scientific directions of the project and highlights funding opportunities. Depending on availability of funding, one or more pre-or post-doctoral fellows have worked on data analysis and outreach.
Variables and descriptions The data are stored in a MySQL database but are accessible to the public as five csv files. They respectively contain information on Guess, Photos, Gamers, Quality, and Report, using those names and. cvs extensions. In the following, we describe the variables in each of the csv files. All missing data are encoded as NA. The Guess. csv file stores the information regarding the photographs using the following variables: guess_id, photo_id, uid, ageG, outG, access, access_datetime, repeated_guess. The guess_id, photo_id, uid variables contain the individual identifiers of each guess, the photograph guessed on, and the user who made the guess. The ageG and outG variables describe the guessed age and the deviation in the guess from the real age in years, respectively.
The access and access_datetime variables store the timestamp when the guess was made in Unix time and in date and time UTC+1:00, respectively. Finally, the repeated_guess variable is an indicator variable (TRUE/FALSE) for separating unique from repeated guesses from the same user on the same photograph. Some research questions might use this information, for others the data can be subsetted using repeated_guess. The Photos. csv file stores the information regarding the photographs using the following variables: photo_id, uid, age, relation, gender, ethnicity, birth_country, birth_year, death_age, created, and created_datetime. The photo_id and uid variables represent the individual identifiers of the photograph and the user who uploaded the photograph. The relation variable indicates whether the photograph is of the user or of another person to which the user has a relation (current categories: user, grandchild, half sibling, maternal grandparent, mother/father, paternal cousin, paternal grandparent, paternal uncle/aunt, sibling, son/daughter, unrelated or friend).
Finally, the Report. csv file pertains to information on any other reports made on photographs. The variables are report_id, photo_id, uid, comment, created, and created_datetime. The report_id, photo_id, and uid variables store the individual identifiers of the report, the photograph on which the report was made, and the identifier of the user who made the report, respectively. Report categories are rotation needed, cropping needed, none or more than one person, copyright infringement, offensive content, and combinations thereof. The AgeGuess team regularly edits images after report, for example when cropping is needed, and retains the edited photographs if suitable. Photographs and data associated to the other reports categories are deleted. Finally, after internal checks the system adds reports related to missing photographs and inaccurate data on birth year and age.
Data quality and outlier removal The data set can contain both false and missing data that may have been entered by users either by mistake or intentionally. Therefore, we perform some basic data cleaning steps before publication of the data. From the Guess data we delete all guesses that are more than two times the standard deviation away from the mean age guess on a photograph. We further remove all guesses on photographs with less than 10 guesses on the photograph. Using the information in the Report data (see paragraph above), we delete guesses on photos with inaccurate age or birth year. Since not all inaccurate birth years are flagged by internal system checks, we replace in both the Photos and Gamers data all unrealistic birth years (< 1800 or >2018) with NA. The whole, uncleaned data set can be obtained upon request.