dataesr/scientific-paragraphs-categorization huggingface dataset:
Recce walks: Besides transects, we also recorded all evidence of mammals and snares from recce walks and during work on other research projects, e. g. focal follows of red-tail monkeys (Cercopithecus ascanius) or yellow baboons (Papio cynocephalus), or days spent searching for chimpanzees or snares specifically. Additionally, once monthly, we conducted a 3-day extended patrol to a peripheral area to the core study site. These patrols were designed to expand the geographical scope of our project and offer comparative data from areas less frequently visited by researchers. Each patrol destination (n=6, 8km MISHAMO Figure  2 ) was visited twice annually. Similar to transect methods, we recorded number, age, and type of evidence, in addition to vegetation type and topography. In addition to mammal and snare sightings, we recorded "effort" points every 30 minutes, where a GPS coordinate, vegetation and topography information were recorded.
Line transects We used DISTANCE 6  (Thomas et al. 2010)  to analyze line transect data according to standard line transect analyses in which the drop in the number of sightings with increasing distance is modeled to obtain a probability estimate of sighting an object  (Thomas et al. 2002). Estimating densities from line transect survey can be done from several types of observations, e. g. direct encounters, dung samples, ape nests  (Spehar & Marshall 2010; Tagg & Willie 2013). We considered only direct observations of individuals in our analyses, except in two cases. For chimpanzees, we analyzed encounter data of both individuals and nest sightings. For bushpigs (Potamochoerus larvatus), because we encountered them only rarely, we used dung encounters to calculate an overall density. Previous studies have demonstrated the reliability of using dung counts to estimate overall species richness, especially at scales >25km 2  (Cromsigt et al.
Habitat and mammal characterization We defined the beginning of the wet season as 15 September, and the dry season as 15 April, based on average annual  (2009) (2010) (2011) (2012) (2013) (2014)  onset and end of rains. To investigate whether there was more riverine forest further from the researcher station (which may explain poaching effort), we conducted a vegetation classification of the entire area (combined core and peripheral = 400 km 2 ), where each of the above-described cells was scored as either 0 (no forest present in the cell) or 1 (forest present). These data were then included into our model as forest presence or absence. To examine whether (animal) encounter rates differed with animal-size or taxa level, we sub-divided animals into small (<~50kg, e. g. duikers, klipspringer, pig), medium (50-100kg, e.
Acknowledgements We are grateful to the Tanzanian Wildlife Research Institute (TAWIRI), the Commission for Science and Technology (COSTECH), and to Mpanda District government for permission to conduct research in Tanzania. Many thanks to Leslie Knapp and the Division of Biological Anthropology for hosting AL during her internship at Cambridge and to the Erasmus Programme for funding this internship to AL. CJ and Moritz Rahlfs led many of the safaris that resulted in data described here, and none of the work could be achieved without the indefatigable efforts of Shedrack and Joffrey Lucas, Busoti and Mlela Juma, Msigwa Rashid, Mashaka Alimas, and Parag Kadam. Funding was provided by the Carnegie Trust for Universities of Scotland, Harold Hyam Wingate Foundation, L. S. B. Leakey Foundation, International Primatological Society, National Science Foundation, the Ruggles-Gates Fund for Biological Anthropology, University of California, San Diego, and the Wenner-Gren Foundation.
The different editions were completed by almost 1000  (in 1985 and 1992)  to more than 10000 participants (in 2011). Internet accounted for 23% of the questionnaires analysed in 2004 and 90% in 2011. The topics concerned the socio-demographic profile of the respondents, their social lives, their sex lives (practices and prevention methods) and their health. All questionnaires were completed anonymously. The French data protection committee (Commission nationale de l'informatique et des libertés) approved the surveys and data collection and storage.
Variables for analysis We compared the data from surveys from which common indicators could be created, restricting analysis to participants aged 18-59 (N = 38821) due to the relatively sparse participation of respondents aged >59. Year of the 18 th birthday (= year of birth + 18) was used as the variable defining the cohort/generation, as reference to the starting point of sexual life: 18 was the median age of first sexual intercourse with a man (see Results section) in our samples. This variable allowed us to study MSM who had begun sexual activity (i. e. , turned 18) from 1944 to 2011 (1944 corresponding to the oldest MSM included, i. e. , those aged 59 in 1985) (Table  1 ). Other socio-demographic variables were education level, marital status, self-identification and size of the city of residence.
We also took into account the source of the questionnaire (press or internet). The sexual debut was described by the sex of the first sexual partner and the age at the first intercourse with a man, from data in surveys from 1995 to 2011. First intercourse is used here   to translation for premier rapport sexuel in French. No definition was provided in the surveys; it refers to first sexual experience. Individuals interviewed with this question and then asked about their interpretation of the questions generally understood the term as indicating penetrative sexual relations  [37]. Questions used for the analysis of sexual debut were: "How old were you when you had your first sexual intercourse with a man?" and "How old were you when you had your first sexual intercourse with a woman?". When reported age was the same for both, the sex of the very first partner could not be determined and was considered as missing.
Identification of studies An extensive and systematic search was conducted of 12 literature databases to identify peer-reviewed, primary studies that reported long-term outcomes of individuals with ADHD. The databases were Academic Search Premier, CINAHL, Cochrane CRCT (including EMBASE), Criminal Justice Abstracts, ERIC, MEDLINE, Military & Government collection, NHS Economic Evaluation database, PsycARTICLES, PsycINFO, SocINDEX, and Teacher Reference Center. All 12 databases were searched in two search waves conducted in May 2010 and March 2011. A third search of 9 databases was conducted in March 2012. Three databases were not utilized in the third search (Academic Search Premier, NHS Economic Evaluation database, and PsycAR-TICLES) because no unique useful citations had been derived from those databases in the first searches.
Data extraction Reported data from each study was manually extracted from the full text of the study to a database, including: (1) study location, (2) study sample size, (3) study length (e. g. , years to follow-up), (4) participant sex and age range, (5) diagnostic criteria or symptom assessment, (6) study type (longitudinal, cross-sectional, prospective, retrospective, randomized),  (7)  outcome results,  (8)  comparator type, (9) treatment type,  (10)  treatment initiation age, and (11) treatment duration.
Effect of participant age at initiation of treatment was analyzed by dividing studies into three groups according to mid-range age at initiation of treatment (<8 years old, 8-12 years old, and 13 years old) and determining the proportion of studies demonstrating benefit or no benefit for each initiation age category. These age category divisions were chosen to correspond with the minimum age of adolescence used in this analysis for other age categorizations (13 years) and to provide a sample of the youngest age-of-initiation studies available in this dataset (<8 years old). The influence of several aspects of study design and participant characteristics was also examined. Outcomes reported in studies of specific study designs (e. g. , randomized treatment studies) were examined. The effect of total study length (i.
RCTs are often considered the gold standard of study designs testing treatment efficacy. The 7 retrospective studies utilized several sources of historical data. Four studies utilized self-report questionnaires or clinical interview of the patients; three studies accessed medical records or health claims databases; and one accessed criminal records. While most included studies were observational open-label studies, in the 5 RCT studies, individuals were randomized to receive non-pharmacological treatment or no treatment: four (57%) of the 7 total outcomes were reported to improve with treatment. The results from studies grouped by other aspects of study design (all treatment modalities considered) are as follows: 66% of outcomes in the prospective studies improved with treatment, 43% of outcomes improved with treatment in the retrospective studies, and one of three outcomes improved with treatment in the cross-sectional studies.
Discussion This comprehensive analysis examined 51 studies that reported 111 treatment outcomes and provided an extensive dataset for addressing questions related to the effects of specific aspects of ADHD treatment on long-term outcomes. Improvement with treatment (outcomes reported to be statistically significantly better than either pre-treatment or an untreated ADHD group) was found for all treatment modalities (pharmacological, non-pharmacological, and combination treatment) for the majority of outcomes reported. In this analysis, the comparison of treatment modalities demonstrated that the highest proportion of long-term outcomes was reported to improve with combination treatment. The results of effect size analysis were also consistent with benefit being associated with all treatment modalities, with the largest effect sizes being associated with combination treatment.
In our assessment of the influence of study design, we found that prospective studies and RCTs showed a greater proportion of improved outcomes with treatment, compared with retrospective and crosssectional studies. The limitations inherent to retrospective and cross-sectional studies may influence the reported outcomes. For example, self-reported historical data may be less accurate than data regarding recent or present status. Furthermore, historical data from medical and criminal records, although accurate, may not be complete (e. g. , only crimes that resulted in police encounter would be in official records). Another important limitation is that "treatment benefit" was defined as any significant improvement compared with a group of untreated individuals with ADHD or from pre-treatment baseline to follow-up, even without separate within-study untreated comparator groups.
There are many comorbidities associated with ADHD, including oppositional defiant disorder, conduct disorder, anxiety disorders (i. e. , generalized anxiety disorder, obsessive-compulsive disorder, and posttraumatic stress disorder), and depression. The presence of comorbidities may influence the effect of treatment. Many of the studies in this dataset, however, either excluded individuals with particular comorbidities (24 of 51 studies) or controlled for comorbidities through methods such as regression analysis or comparison of outcomes of individuals with ADHD with/without the comorbidity (17 of 51 studies). Outcomes adjusted for comorbidities by some method were used in the present analysis whenever possible. The influence of comorbidites on the long-term outcomes associated with ADHD is a complex issue, especially in the consideration of the effects of treatment on these long-term outcomes.
Statistical analysis Analysis was by intention to treat. Outcome data from patients randomized in the coached group were analyzed even if they dropped out of the study or refused phone calls. The sample characteristics were evaluated using summary statistics with values given as mean (standard deviation) or proportions as appropriate. Outcomes between groups were compared with independent samples t-tests and the χ 2 test for the difference in proportions. The number of calls for the coached group were indicated and used for subgroups analysis. Based on the primary analysis, it was estimated that 352 patients would be required to complete the study to detect a difference of 15 % between two randomized groups, with a 2-tailed significant level of 0. 05, and power 0. 80. The recruitment target was 380 patients to account for loss to follow up.
Characteristics of patients Three hundred and seventy-nine patients fulfilled the inclusion criteria and were randomized. All patients had an education level superior to elementary school. Analyzed data are presented on 377 patients (cf. Fig. 2 ). Patients' characteristics of the 2 groups are presented in Table  1. Mean age, sex ratio, AHI, ESS were not statistically different among the two randomized groups. A majority of the patients were men, the mean AHI was 42/h. The most common clinical features were a history of snoring (82 %), excessive daytime sleepiness (69 %) and witnessed apneas (30 %). Most patients had associated co morbidity, such as AHT (45 %) or diabetes (20 %).
SRH SFLE Fig. 1. Empirical network of contacts between individuals, aggregated over the two weeks of study. Each node represents an individual and the color-code corresponds to the different departments. Each link between two nodes indicates that the corresponding individuals have been in contact at least once during the data collection. Nodes are laid out using the Force Atlas algorithm (see Gephi software, http://www. gephi. org), which allows communities to be apparent in the visualization of the network. between two nodes if the corresponding individuals have been in contact at least once during the study. Each link carries a weight calculated as the total duration of the contacts between the two individuals. The resulting distributions of node degrees (the degree of an individual gives the total number of distinct other individuals with whom (s)he has been in contact during the study) and link weights are shown in Fig.
16 , is significantly different from the empirical one. This indicates that the empirical contact matrix structure is not explained by random encounters of individuals with different presence timelines. 7. 2 Effect of data representation on epidemic spreading. The collected contact data consist in a temporal network at a very high temporal resolution. These data can be aggregated and represented in different ways, both along the temporal and the organizational dimensions, in order e. g. to build models for the spread of epidemics in the population. As discussed e. g. in  (Smieszek et al. , 2009b; Read et al. , 2008; Eames et al. , 2009; Stehlé et al. , 2011c; Machens et al. , 2013b; Blower & Go, 2011b) , the level of detail of the data representation that is taken into account in the model can influence the outcome of the simulations.
PARTICIPANTS The total sample consisted of 532 Hungarian participants (380 female), aged between 16 and 69 years (M = 24. 88, SD = 6. 94). Respondents were recruited by posting an online survey link on Facebook. Among them, we chose a subsample (N = 292 (230 female) ranging between 16 and 69 years-old (M = 24. 92, SD = 6. 62) who, based on the online survey, were currently in a relationship. In order to assess relationship status, respondents of the online survey answered the following question: "Are you currently dating with a romantic partner and/or living in a relationship?" by choosing the following possibilities: "No" (N = 181, 34. 7%), "Rather no" (N = 23, 4. 3%), "Rather yes" (N = 31, 5. 8%), "Yes" (N = 292, 55. 1%). We only analyzed the data of the individuals who answered "Yes" to this question.
DATA GATHERING AND MEASURES The dataset was collected by a Facebook application based on Kosinski and Stillwell's myPersonality application  (Kosinski and Rust, 2011; Kosinski and Stillwell, 2012). The Concerto platform was provided by the Psychometric Center of University of Cambridge, which allowed us to acquire Facebook user information such as relationship status. Furthermore, participants got a personalized feedback on the scales that they completed, in exchange for their time and effort. Before starting the questionnaire, participants received detailed information about the study and a list of personal data gathered from their Facebook profiles. Subsequently, participants read and approved the informed consent. The scales used were translated, and back-translated by Náfrádi and Orosz (in preparation) following  Beaton et al.
Stimuli Previous research indicated that the natural running cadence for recreational runners lies somewhere between 130 and 200 steps per minute (SPM)  [45]. On that account, a music database consisting of songs in the tempo range of 130-200 beats per minute (BPM) was created. A group of 19 students from Ghent University, all recreational runners, were asked to provide a list of at least ten songs they believed to be motivational to run to. From that specific list of music, the database for the experiment was created. In total, 117 songs with a clear beat and correct tempo range were pre-selected (see Table  1 ). In the course of the selection process, it was verified that the tempo of each song remained stable throughout the entire track. Using Audacity software (http://audacity. sourceforge.
Apparatus Participants were equipped with two iPods (fourth generation), one attached at each ankle. Using the Sensor Monitor Pro application on the iPods, data from accelerometers and gyroscopes was streamed wirelessly at 100 Hz to the main processing computer. A Wi-Fi hotspot (TP-Link N750) with special 3-dB gain antennas for longer range was used for maintaining a stable connection between the computer and sensors. Some minimal jitter and lag in the data stream were neutralized using a 500-ms buffer before processing. Incoming sensor data was processed by a customized version of D-Jogger  [47] , a music alignment framework that selects and tempo-adapts music to runners' gait frequencies using kinematic sensor input (Additional file 1). Music tempi were manipulated using a phase vocoder, which time stretches music without pitch modification.
D-Jogger was adapted to match the experimental protocol (detect running cadence, playback tempo-matched music to this reference, increase or decrease music tempo). The system logged all data and calculations in real time. Finally, the resulting auditory stimuli were sent back to the participant using a Sennheiser HDR130 audio transmitter (with a range of up to 100 m). The participant perceived the music through Sennheiser HD60 headphones connected to the transmitter (attached to the upper arm). The delay due to the wireless audio transmission was negligible.
Experimental Procedure and Set-up The experiment took place in the Flanders Sports Arena of Ghent, Belgium. In order to select motivational music adapted to each runner's personal taste, participants performed the Brunel Music Rating Inventory 2 (BMRI-2) test  [48]  at the start of the experiment. In this test, they were asked to rate all items of the music database by answering six questions about the motivational aspects of each song. Each item referred to an action, a time, a context, and a target (e. g. "The rhythm of this song would motivate me during a running exercise")  [49]. Participants responded on a seven-point Likert scale anchored by 1 ("strongly disagree") and 7 ("strongly agree"). Afterwards, participants filled out a questionnaire on personal background, music education, and sports training.
Data Analysis Cadence Adaptation Running cadence was calculated using the iPods' acceleration data. In order to check the degree of cadence increase/decrease, running cadence (SPM) recorded during the laps with tempo-changed music (tempo-changed laps or TCL) was compared to the cadence captured during the lap with tempo-matched music (tempo-matched lap or TML) and will be further referred to as cadence adaptation. As the tempo was gradually shifting during that period, the first 5 s of the laps with tempo-changed music was discarded. The final 20 s of those laps was also ignored as participants possibly altered their running behaviour due to the anticipated ending of the final lap (e. g. slowing down or speeding up).
, 2007; Hutt, 2007)  while involving finite transmission delay interactions, they allow to study a large class of pattern forming systems, cf. Hutt (2007). In recent years, several software tools have been developed to simulate neural network dynamics. Examples for simulators for networks of spiking neurons are BRIAN  (Stimberg et al. , 2014)  and  Neuron (Carnevale and Hines, 2006). The Virtual Brain  (Sanz Leon et al. , 2013)  allows to simulate networks of neural mass models to reproduce global brain activity. The simulation platform DANA  (Rougier and Fix, 2012)  simulates a hierarchy of coupled Dynamic Neural Fields which are decentralized, i. e. , are updated numerically in time asynchronously  (Rougier and Hutt, 2011). These latter software tools are powerful, general and highly adaptive to the framework of their neural network types.
The mean neuron potential V ∈ R at location x ∈ is evolved by the external stimulus I(x, t) ∈ R and the integral of the synaptic connectivity kernel K : R 2  → R and population firing rate S ∈ R which depend on the distance between spatial locations x and y with a finite axonal transmission speed c. Equation (1) represents the core of most NFM in the sense that most NFMs consider extensions of this equation. Motivation for the work arises from a need for a visualization tool that is useful to the largest number of NFM researchers, allows for the tailoring of code and has fast while visually appealing output. The simulator can operate on all major operating systems. Output of data in three dimensions is provided by PyOpenGL which brings the speed and graphical detail of low-level OpenGL to the agile Python language.
MATERIALS AND METHODS The cross-platform simulator is written in Python (version 2. 7) and uses the NumPy library in consideration of its speed being close to the computational rate of the platformdependent C language  (Langtangen, 2006). The simulator can be downloaded 1  in a package along with documentation 2 describing its installation, running, features, and examples and the code is registered in ModelDB 3. The following Section 2. 1 describes the comprehensive access to field parameters, the subsequent section details the techniques applied to accelerate the simulation and Section 2. 3 discusses the 3D visualization.
For instance, matrix I can be defined in the interface with as many lines of Python code as necessary given the definition ends with an assignment (i. e. , I =. ). Assigning the first parameter in the values. py file, named showData, a value of 3 displays I in the simulator, which can be useful when refining its values. Time-varying spatiotemporal input is available in the interface by uncommenting and modifying the body of a function named updateI in any manner while maintaining that I is returned. Neural field investigations are thereby efficiently implemented with free choice over all the variables accessible through the interface while retaining the full performance.
Implementation on GPUs The third approach parallelizes the output calculations on the graphics processing unit (GPU). The displayed matrix is put onto the running system's GPU for hardware acceleration of the visualization. Vertex buffer objects improve visualization throughput by uploading vertices to video device memory where vertex and fragment shaders transform and write neural field data in parallel to the framebuffer for display. The simulator also avoids the CPU to GPU information transfer bottleneck by its storage of data on the video device memory. This is accomplished with the OpenGL Shading Language that is used through PyOpenGL to achieve a better visual description of information than is provided in other tools. A background on PyOpenGL and its comparison to other visualization libraries can be found in  Rossant and Harris (2013).