dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction There is a growing demand for studies dealing with natural disasters in the research field of emotion and social cognition. This inquiry, however, requires validated stimuli of natural hazards. Thus, we present the Natural Disasters Picture System (NDPS), a database of pictures of natural hazards and their impact on the environment and humans. Beyond the academic investigation, the objective of this work is to get a better understanding of natural hazard perception, with an emphasis on volcanic threats, in order to improve communication strategies during crises. In this article, we present the creation and implementation of the NDPS as well as the procedure and results of its validation among a population of Western European individuals. We also provide a distinctive emotional classification of the pictures and their contents.
Studies in the field of the affective sciences use different methods and materials to induce emotions, such as imagination, movie clips or static pictures. If emotions are to be genuinely elicited, the material has to be standardized and validated  [1]. However, the affective sciences are not the only field that requires standardized material. Specific subject areas, such as the study of stereotypes based on perceptual cues (e. g. , skin color, clothes) or risk behaviors in response to crises, also need such material in order to reduce biases inherent to stimulus characteristics. However, natural hazards are barely represented in the available databases. Let us consider one of the most widely used picture databases, the International Affective Picture System (IAPS)  [2] , which offers a large selection of pictures validated on the basis of appraisal dimensions.
This database has been extensively used in the field of cognitive and affective science, such as in investigations of the psychological  [3, 4] , psychopathological  [5] , and neurological  [6]  underpinnings of fear conditioning. The applications range from investigations of the fear system to depression, anxiety, psychophysiology, and emotion regulation, among many other fields of research, resulting in more than 3. 700 citations. The variety of pictures ranges from positive (e. g. , babies, landscapes, cute animals) to extremely negative pictures (e. g. , mutilations, assaults, accidents), from low (e. g. , lamps, spoons, bowls) to highly arousing pictures (e. g. , aimed guns, mutilations, erotic couples), and from pictures inducing submissive (e. g. , aimed guns, burn victims, attacks) to dominant perceptions (e.
g. , babies, flowers, cute animals). Despite this large selection of 956 pictures, natural hazards are barely represented in the IAPS (only 8 pictures: 5 of tornadoes, 1 of lightning, 1 of a volcano, 1 of a lava flow). This issue of the limited number of picture categories has already been pointed out by several studies (e. g. ,  [7] [8] [9] ). Consequently, three other validated image databases were recently published: the Emotional Picture System (377 pictures)  [10] , the Geneva Affective Picture Database (730 pictures)  [7] , and the Nencki Affective Picture System (1356 pictures)  [9]. However, none of these databases included pictures of natural hazards. Considering the great diversity of natural hazards (e. g. , earthquakes, tsunamis, tornadoes and on to volcanic eruptions), the affective and cognitive sciences require a comprehensive database, with a specific section devoted to volcanic hazards due to the variety of the related events (e.
g. , fumaroles, geysers, lahars, lava flows, pyroclastic flows, tephra falls). In order to provide a database of pictures related to natural hazards, we first implemented the NDPS by selecting relevant pictures of several natural hazards (e. g. , volcanic eruptions, earthquakes, hurricanes). To validate the NDPS, we then adopted a methodology somewhat similar to that used for the validation of the IAPS  [2]. Specifically, we assessed the emotional aspects of the pictures using the Self-Assessment Manikin (SAM) scale  [11] , which is based on three dimensions taken from the appraisal theories  [12, 13] : arousal, valence and dominance. To these dimensions, we also added the assessment of certainty, given its significant weight in emotion discrimination, as has been mentioned by most authors working in the field of appraisal theories  [14] [15] [16] [17].
We believe studies in the affective and psychological sciences would benefit from a systematic evaluation of certainty, which is now considered to be a key element in judgments, decision-making and communication theories  [19] [20] [21] [22] [23]. Researchers in fear-arousing communications  [24, 25] , such as those related to natural disasters, require a validated fear-eliciting picture system. In this article, we present the NDPS, a picture database exclusively dedicated to natural hazards and their impact on the environment and humans. Note that previous studies showed that cultural differences can lead to some various perceptions of natural hazards  [26, 27]. However, the current validation, as a first step of a broader validation, is based on a sample of participants from Western Europe with no prior experience and/or exposure to natural hazards.
To evaluate each picture in the database, we used four dimensions: arousal, valence, dominance and certainty. We performed a two-stage validation operation involving a total of 120 participants, during which we divided the entire database into two sets of pictures. Finally, to provide a distinctive emotional classification of the pictures and their contents, we used a Hierarchical Ascendant Classification. Overall, this classification suggests four clusters depicting distinct patterns of emotions, subdivided into nine classes of picture types. The NDPS database is freely available together with the associated online material.
Creation of the database Description of the picture set. The database contains 154 pictures of various natural hazards (e. g. , earthquakes, hurricanes, tornadoes), including an extended set of pictures of volcanic hazards (e. g. , volcanic explosions, lava flows, ash and lapilli falls, and pyroclastic flows), aftermaths and victims. The readers should note that due to a copyright misattribution at the time of the searches, picture 19 had to be removed from our database upon validation. We used two sets of pictures, a first set of 72 pictures and a second set of 83 pictures. The characteristics of each picture are detailed in S1 Appendix. They include the picture identification number, its main category based on the nature of the hazard (e. g. , earthquakes, lava flows), and three subcategories referred to as content (e.
g. , event, aftermath), kind of view (e. g. , closerange, long-range), and specific features (e. g. , aerial views). Most pictures were taken from different web sources such as Flickr, Wikimedia Commons, USGS (United States Geological Survey), USDA (United States Department of Agriculture). The others were taken by various volcanologists (one is a co-author of the present article) or photographers. All pictures were either in the public domain or taken from Creative Commons, or permission was obtained from the author(s) for use as part of a database in a research context. The database and on-line supporting material are freely available at: http://ndps. lapsco. fr. Image processing. The photo quality varied depending on the source. We used Rawtherapee 4. 0. 11. 8. to enhance the overall quality of the pictures.
All of them were inspected and, if necessary, corrected in terms of focus quality, exposure (highlights, shadows and contrast), white-balance, resolution, and cropping. For example, to obtain a more natural effect, highlight/shadow corrections make it possible to compensate for over/underexposure. Most pictures were already high resolution (over 1200 x 800 pixels), except for pictures 50, 53, 54 and 65, which were only available in a lower resolution (800 x 500 pixels). The minimum resolution was finally set at around 1200 x 800 pixels for all pictures, thus giving rise to a homogenous and standardized database.
Validation of the database Participants and ethics statement. The participants consisted of 129 undergraduate students in Psychology from Clermont Auvergne University, formerly Blaise Pascal University (Clermont-Ferrand, France). We recruited 63 and 66 participants for the first and second sets of pictures, respectively. Participants who could not perform the task properly (n = 9) were excluded from the analysis for the following reasons: (i) did not pay attention to the change of order of the dimensions (n = 5), (ii) gave the same answer throughout the task (n = 1), (iii) could not understand how to use the dominance and certainty scales (n = 2), and (iv) suffered from Stargardt disease (n = 1) (Stargardt disease is a visual affliction defined as a macular degeneration which progressively reduces vision to the point of blindness, while leaving peripheral vision unaffected).
Statistical analysis. We calculated means and standard deviations for each dimension on each of the 154 pictures. We then performed Pearson correlations between each dimension for the overall sample. To examine the emotional patterns induced by the pictures, we conducted a Hierarchical Ascendant Classification (HAC) on the four dimensions, based on the distance between groups, using a solution ranging from one cluster up to the maximum number of clusters. To interpret the emotional pattern for each class, we calculated the means (±SD) of each dimension for each class. Statistical analyses were conducted using IBM SPSS 22 (IBM Corp. , USA).
42), and high certainty (M = 6. 15, SD = 0. 39), and therefore corresponded to a more abstract category consisting of a pleasant aesthetic emotion or elation  [13, 29]. The cluster Neutral (B) was associated with moderate valence (M = 5. 46, SD = 0. 32), arousal (M = 4. 68, SD = 0. 61), dominance (M = 4. 60, SD = 0. 54) and certainty (M = 4. 96, SD = 0. 31), and therefore corresponded to an attitude of neutrality or to a minimum level of interest. Overall, 89. 6% of the pictures in the database were associated with negative emotions: 29. 9% sadness (cluster Desolation A) and 60. 4% fear (cluster Frightening C). Only 10. 4% corresponded to positive emotions and neutral/interest, with 5. 1% corresponding to an aesthetic emotion (cluster Pleasant/aesthetic D), and 4. 9% to neutrality/interest (cluster Neutral B).
Discussion Here, we have presented the Natural Disasters Picture System (NDPS), a picture database dedicated to natural hazards and their impacts on the environment and humans. Regarding the implementation of the NDPS, we captured a wide variety of natural events such as snow avalanches, earthquakes, fires, floods, hurricanes, landslides, sea storms, tornadoes, tsunamis, and typhoons. Due to the great diversity of events arising from volcanic hazards, we also created a specific section relating to volcanic activity, such as acid lakes, fumaroles, geysers, lahars, lava flows, pyroclastic flows, tephra falls, and other volcanic phenomena. The results from the NDPS validation demonstrated that most of the pictures were of negative valence. Finally, the emotional classification analysis revealed that most of them induced emotions of fear or sadness.
We have reported all the validation data for each picture and identified clusters. The emotional aspects of this database mainly involved negative emotions, a finding consistent with our expectations concerning stimuli related to natural hazards. 90% of the pictures gave rise to negative emotions, which corresponded to fear and sadness. The classification revealed two very negative classes of pictures: the Highly shocking (V) and the Victims classes (VI). The remaining 10% of the pictures were found to elicit neutral and positive aesthetic emotions. In our opinion, these stimuli could be very useful for researchers, as protocols often require control conditions involving pictures of natural hazards that elicit positive emotions or convey neutrality rather than feelings of negative arousal.
The classification revealed by the NDPS therefore exhibited a high level of consistency within clusters and classes. For the sake of clarity, we consider here some potential uses of the NDPS in experimental and applied psychology (evacuation of exposed population), cognitive neuroscience (investigation of the fear system during uncertain or natural disasters, etc. ), and even applied volcanology. Complementarily, the IAPS [2] had a tremendous and extensive impact in psychology, cognitive neurosciences, and-more broadly-affective sciences, as evidenced by the number of citations (more than 3500 for the 2008 reference). However, a weakness of IAPS is that this database is constituted of general stimuli that cannot be applied to natural disasters. The NDPS can be used separately or in combination with the IAPS in studies requiring natural hazard stimuli (e.
Conclusion The NDPS provides a new tool for studying natural events and disasters in the field of affective and cognitive sciences, including a wide variety of natural events with a specific section related to volcanic activity. Its dimensional validation with four dimensions (SAM + certainty) means that this new database could be used for a range of research works, especially because the certainty dimension is of key importance in social cognition. The hierarchical classification also provides researchers with a coherent and distinctive emotional classification of the pictures and their features. In addition, the use of the NDPS is not restricted to scientific research applications as it could also find concrete applications in a range of communication purpose, such as in prevention or information campaigns.
Acknowledgments This work was supported by the  Clermont-Ferrand Center for Volcano Research (ClerVolc Excellence Laboratory , http://clervolc. univ-bpclermont. fr/)financed by the  French Government Laboratory of Excellence initiative  (n˚ANR- 10-LABX-0006 ), the  Region Auvergne , and the  European Regional Development Fund. This is Laboratory of Excellence ClerVolc contribution number  294. All the authors declare that they have no competing interests to report. The authors thank  Daniel Andrade ,  Alain Gourgaud  and  Jean Claude Tanguy  for their valuable technical support during the picture selection and for their permission to use these as part of the database. They are also grateful to the participants who took part in the study.
This work was supported by the  Clermont-Ferrand Center for Volcano Research (ClerVolc Excellence Laboratory , http://clervolc. univ-bpclermont. fr/)financed by the  French Government Laboratory of Excellence initiative  (n  ˚ANR-10-LABX-0006 ), the  Region Auvergne , and the  European Regional Development Fund. This is Laboratory of Excellence ClerVolc contribution number  294. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Background and Summary We publish high-dimensional and multi-scale datasets that contain recordings from motor cortex with a 10-by-10 Utah electrode array during controlled reach-to-grasp movements for two monkeys (L and N). In particular, we provide the activities of a large number of simultaneously recorded single neurons (93 and 156, for L and N respectively) along with the continuous neuronal "raw" signals (sampled at 30 kHz, and broadly band-pass filtered to 0. 3 Hz-7. 5 kHz). To study the local field potential (LFP) 1-3 , a downsampled and filtered version of the latter is provided for monkey N and can be computed from the data of monkey L. These high-dimensional parallel datasets provide the opportunity for neuroscientists and computational neuroscientists to study interactions in the cortical network during different epochs of a well described behavior.
If these features are ignored and not considered by the method, there is a considerable danger of generating false positive outcomes and potentially wrong interpretations of the results 5,6. The data we publish allow non-experimentalists to get insights in features of massively parallel cortical data from awake behaving animals. Our data also provide the possibility to test and validate spike sorting methods (see ref. 7). Besides the raw data we publish the corresponding spike data resulting from an offline spike sorting using the Plexon Offline Sorter. Other sorting methods can be applied to the same (raw) data, and differences in the results can be compared and analyzed. Complex datasets, such as those provided here (high-dimensional, multi-scale during complex behavior), are a challenge for performing reproducible analysis.
Besides the often rather variable nature of the circumstances under which such data were recorded, the data additionally experience a number of often interactively performed preprocessing steps before they can be used in actual data analyses. Without a detailed knowledge about all these steps, the actual data analysis may be biased or strongly affected. In most cases, openly available electrophysiological data are not sufficiently annotated and documented in this respect. For this reason, we provide here a comprehensive description of how and under which circumstances the datasets were recorded as well as a detailed description of preprocessing steps that need to be considered before performing analyses on the data. We additionally publish a machine-readable format of these metadata including our parameters and results of the described preprocessing steps (see ref.
In summary, we publish two datasets (one for each monkey, both having performed the same behavioral task) containing the raw neuronal data, offline sorted single and multi unit activity, the behavioral data, and metadata containing all information about each dataset including our parameters and results of the described preprocessing steps (Data Citation 1). The collected information are partly taken from 8,10-12. These are the first published datasets of massively parallel recordings from monkeys while they perform a complex instructed delayed reach-to-grasp task.
She habituated rapidly to the training chair and could start the task training within a month from chair training onset. Generally, monkey L was described as being eager to work, quick and efficient during the task, but rather nervous. The surgery for the Utah Array implant took place on September 15, 2010 (see Sec. 'Surgery Array Location'). About 2 weeks after the surgery, the recordings started. At that time she had a body weight of 5 kg. Neuronal data were recorded during task performance until May 6, 2011. After this date, the quality of the recordings deteriorated suddenly and did not recover. On June 23, 2011, during a short surgery, the array wire bundle was cut, the connector was removed, but the array left in place. Monkey L is still alive. File names from monkey L are identified by the letter "l".
Monkey N Monkey N was born on May 15, 2008. He was initially trained to perform the task with the right hand between April 2012 and April 2013. After a first and disappointing recording period from the left hemisphere which ended in September 2013, he was retrained to perform the task with the left hand and implanted with another Utah Array in the right hemisphere on May 22, 2014 (see Sec. 'Surgery and Array Location'). In general, he was calmer than monkey L, but overall less motivated. He learned the task at a much slower pace, performed on average less trials per recording and was often less attentive than monkey L. When the recording period started he had a body weight of 7 kg. Neuronal data were recorded until January, 2015. The quality of the recordings decayed gradually throughout the recording period.
Surgery and Array Location At the end of the training period, each monkey was chronically implanted with a Utah array (Blackrock Microsystems, Salt Lake City, UT, USA) in the motor cortex contralateral to the working hand. The array consisted of one 10-by-10 electrode grid with 96 active iridium oxide electrodes. Each electrode was 1. 5 mm long with an inter-electrode distance of 400 μm. The electrodes had on average an impedance of 50 kΩ measured at 1 kHz (impedance values were provided by Blackrock Microsystems for each electrode). The impedance of each electrode is indicated in the respective metadata file for each dataset. The electrodes were connected through a wire bundle of 4 cm length to a high-density CerePort Connector (Blackrock Microsystems). In parallel, two reference/ground wires were connected to the connector pedestal.
The neural recording platform (top) was composed of the implanted Utah array with its corresponding connector (CerePort), a headstage (Samtec or Patient cable), and the Cerebus data acquisition (DAQ) system (i. e. the Front-End Amplifier, Neural Signal Processor (NSP), and the Cerebus control software, Central Suite, installed on the setup computer 1). The experimental apparatus (bottom left) consisted of the physical devices which the monkeys had to interact with (i. e. , the visual cue panel (square with 5 LEDs), the target object, the table switch, and the reward system). The behavioral control system (bottom right) was built from hard-and software of National Instruments (NI, National Instruments Corporation, Austin, Texas, USA). It was composed of a NI connector block which was linked via a NI 6023E A-D converter card to setup computer 2 on which the NI system design software, LabView, was running.
To record the behavioral data the behavioral control system was interlinked with the neuronal recording platform via the NSP and the NI connector block. All three parts are separately described in more detail in the main text, as well as Figures  2  and 4. Setup differences between the two monkeys are indicated in yellow and red for monkeys L and N, respectively. illuminating the corresponding corner LEDs of the chosen trial type (GO-ON, e. g. , top LEDs for HF-ON). This second cue also served as a GO signal for the monkey to initiate the movement which was registered by the activation of the table switch (SR-ON) when the monkey released it after a variable reaction time (RT). The execution of the movement was composed of reaching, grasping, pulling and holding the object in the position window for 500 ms.
In case one of these scenarios was registered by LabView the trial was aborted. For monkey L, the LabView VI provided additionally a negative feedback when aborting a trial by flickering all LEDs three times. As displayed in Fig. 2c , the behavioral control system was connected to the NSP of the Cerebus DAQ system to store the trial event sequence and the monkey's behavior of each trial in a recording along with the neural data registered by the neural recording platform. For this, the analog signals of the sensors of the target object were copied from the NI connector block to the analog input port of the Cerebus System NSP via DC coupled BNC cables and connectors. In the NSP they were digitized with a 16-bit resolution at 0. 15 mV/bit and a sampling rate of 1 kHz and saved in the ns2 file under the channels ids listed in Table  1  (see Sec.
Fig. 4 ). platform consisted of the multi-electrode Utah array, a headstage, and a Cerebus data acquisition (DAQ) system. The latter is composed of a Front-End Amplifier, a real-time Neural Signal Processor (NSP) and the control software, Central Suite (version 4. 15. 0 and 6. 03. 01 for L and N, respectively), running on Windows XP for L, and Windows 7 for N on the setup computer 1 (see Fig. 4 ). The Cerebus DAQ system was also connected to the behavioral control system via the NI connector block to save the analog behavioral data and digital trial event signals that were described in the previous section in parallel with the neural signals. All data were transmitted from the NSP via an ethernet cable to be saved first locally on the setup computer 1. After a recording day, all recordings were transferred to a data server.