dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The findings of the present work have made the theoretical benefits of open data sharing for brain imaging research tangible through citation analysis of the impact of the FCP/INDI data. Despite common misconceptions, publications using shared data are well-represented in moderate-to-high-impact journals, and were found to benefit young investigators (e. g. , graduate students) as well as more senior investigators. Our findings that the scale (i. e. , sample size) of studies carried out by data contributors increased through the use of data shared by others emphasized the symbiotic relationship that can emerge when investigators with common interests make their data open. Simultaneously, we demonstrated the ability to recruit the broader scientific community to address the task at hand when data are openly shared; given the various forms of expertise required to leverage technical and methodological innovations to achieve ambitions for identifying clinically useful brain-based biomarkers, the recruitment of expertise that extends beyond the teams that generated the initial data sets is essential.
Having demonstrated the impact of data sharing, a remaining challenge is to make sharing a widespread reality. Multiple advances are necessary to accomplish this change. First, there is a need for greater incentivization of both sharing and using open data sets. While funding agencies increasingly espouse mandates to share data and encourage secondary data analysis, voices vilifying openly shared data and its users  28  continue to be expressed in top journals. Second, the mechanisms for recognizing data-sharing contributions remain underspecified in, for example, grant, promotion, or tenure reviews  29. Third, widespread data sharing requires infrastructure. To date, the storage costs of the INDI have been relatively limited, requiring about 10TB to share over 15,000 data sets. However, resource limitations must be considered as data sharing and the size of shared data sets continues to grow along with acceptance of data sharing and of data sharing mandates by funding agencies and journals.
This is especially important given that incentives for sharing may arise over time (e. g. , data sharing supplements for funded grants, data sharing consortia relevant to the investigator's interests). Finally, it is important to note the potential impact recent calls for harmonization can have on the value of shared data. Harmonization of protocols involves the adoption of common means for acquiring data (e. g. , MRI scan sequences, experimental procedures, and phenotyping instruments), as well as the adoption of consensus standards for data quality. For the most part, discussions of harmonization in the imaging community been limited to planning large-scale multisite studies, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the NIH Adolescent Brain Cognitive Development (ABCD) Study.
In the context of INDI, ABIDE provides an excellent example of how standardization can benefit data sharing, as nearly every study in ABIDE included the Autism Diagnostic Observation Schedule (ADOS) due to longstanding efforts in the autism community to standardize phenotyping. This is in contrast to the ADHD-200, where the instrument for assessing ADHD symptoms varied from site to site. While investigators can devise strategies for pooling data across heterogeneous tools, they are inherently suboptimal. In closing, we assert that it is the responsibility of the entire scientific ecosystem, from funding agencies to junior scientists, to accelerate the pace of progress by making data sharing the norm. While the merits and impact of data sharing are clear, it is up to all levels of science to make sharing a priority in order for its true value to be realized.
Methods Identifying and classifying publications using INDI data. We started our bibliometric analysis with a search for publications that used INDI data sets. This was a non-trivial task due to the lack of requirements for author-line recognition of INDI, a policy intended to maximize freedom of use for the data. We identified publications using a full-text search in Google Scholar; the following names and URLs were included as keywords: "fcon_1000. projects. nitrc. org," "Rockland Sample," "1000 Functional Connectomes," "International Neuroimaging Data-Sharing Initiative," "Autism Brain Imaging Data Exchange," "ADHD-200," and "Consortium for Reproducibility and Reliability. " Next, we downloaded all available PDF files for manual review by a team of five research assistants, who classified each as "downloaded and used INDI subject data," "only mentions or references INDI data," "used INDI scripts but not INDI data," or "irrelevant.
Acknowledgements The  Child Mind Institute  provides primary funding for the INDI team, with additional support provided by the  Nathan S. Kline Institute for Psychiatric Research. We would like to thank the many contributors to the 1000 Functional Connectomes Project and  INDI ; it is their vision and contributions that have made these efforts successful. Thanks also to the many members of the INDI team over the years, especially  Maarten Mennes ,  Quiyang Li ,  Dan Lurie , and  David O'Connor. We thank the  Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC)  for hosting support for INDI, as well as  Amazon Web Services  and the  COllaborative Informatics and Neuroimaging Suite (COINS). This work was supported in part by gifts to the  Child Mind Institute  from  Phyllis Green ,  Randolph Cowen ,  Joseph P.
Healey , and the  Stavros Niarchos Foundation. M. P. M. is the  Phyllis Green and Randolph Cowen Scholar  at the  Child Mind Institute. A. D. M. received grant support from the  National Institutes of Health  ( 521MH107045 ); X. -N. Z. received support from the  National Basic Research Program  ( 2015CB351702 ),  National Natural Science Foundation of China  ( 81220108014 ),  Beijing Municipal Science & Technology Commission  ( Z161100002616023  and  Z171100000117012 ), the  National R&D Infrastructure and Facility Development Program of China-"Fundamental Science Data Sharing Platform"  ( DKA2017-12-02-21 ), and  Guangxi Bagui Honor Scholarship Program. A. Kr. received support from the  IDEFI IIFR grant  ( ANR-2012-IDEFI-04 ). Primary funding for the NKI-RS initiatives is provided by grants from the  NIH  ( R01MH094639 ,  R01MH101555 ,  R01-AG047596 , and  U01MH099059 ), as well as support from the  New York State Office of Mental Health and Research Foundation for Mental Hygiene , and  Child Mind Institute  ( 1FDN2012-1 ).
METHODS This study is part of the results of archaeological research that took place on Kampai Island around 2011 to 2016. Collected data of glass beads were gathered from the findings of each archeological site through conscientious excavation all around of Kampai Island. For the analysis, in order to analyze varieties of glass beads, this study characterizes the variety of Kampai Island glass beads based on their typology and frequency, i. e. , drawn glass beads and wound glass beads. A number of these glass beads are analyzed in the laboratory using Inductively Coupled Plasma-Mass Spectrometer (ICP-MS) connected to the laser New Wave UP213 to identify the composition of the ingredients. In determining the origin of glass beads production, this study used a comparative method on some published research.
ACKNOWLEDGEMENT This article is part of an ongoing dissertation by the author as a doctoral candidate in Archaeology, in the  Doctoral Study Programs of Humanities , the  Faculty of Cultural Sciences (Fakultas Ilmu-ilmu Budaya) ,  Universitas Gadjah Mada. The author would like to acknowledge the  BU (Beasiswa Unggulan ) scholarship fund, awarded by the  Republic of Indonesia's Ministry of Education and Culture (Kementerian Pendidikan dan Kebudayaan) , for its financial support. My gratitude goes to  Prof. Dr. Bambang Purwanto , M. A. and  Dr. Widya Nayati ,  M. A. which has provided input in the writing of this article. My grartitude also to  Dr. Daniel Perret  for giving inputs to the initial draft of this article. Thank you to all team members on Kampai Island research 2013, 2014, and 2016:  Andri Restiyadi ,  M.
Method Participants. Twelve seven-month-old infants participated in the experiment (6 female; M age = 225. 1 days, SD = 9. 1). All infants were acquiring English as their first language, were born full-term, and were not at-risk for cognitive or language delay. Seven additional infants were tested, but their data were removed from the analysis as five infants had more than 20 bad channels and two infants did not complete the experiment. This study was approved by the Human Research Ethics Committee at Western Sydney University (approval number 9142). Prior to the study, the primary caregiver of each infant completed an informed consent form, and they were informed that the procedure would be immediately discontinued if they wished so, or if their infant showed any signs of distress. This study followed the approved protocol regarding participant recruitment, data collection, and data management.
Stimuli. The stimuli consisted of recordings of naturally-produced IDS and ADS. These recordings were produced by a female Australian English speaker when she interacted with her seven-month-old infant (IDS) or an experimenter (ADS). During the IDS recording, the speaker and her infant sat alone in an infant laboratory room, and she was instructed to interact naturally with her baby. She was provided with soft toys and pictures to facilitate the interaction. During the ADS recording, the speaker was interviewed by a female experimenter, also a native speaker of Australian English, in the same laboratory room and she was asked to comment about the IDS session. The infant was not present during the ADS recording. A head-mounted microphone (AudioTechnica AT892) connected to Adobe Audition CS6 software via an audio input/output device (MOTU Ultralite MK3) was used during the speech recordings.
EEG Recording. The infants sat on their parent's lap approximately 1 m from an LCD screen. Stimuli were presented through audio speakers at 75 dB SPL. In order to maintain infants' attention, a coloured checkerboard was presented on the screen which changed colour every 30 seconds. While infants listened to the speech stimuli, their continuous EEG was recorded using a 129 channel Hydrocel Geodesic Sensor Net (HCGSN), NetAmps 300 amplifier and NetStation 4. 5. 7 software (EGI Inc) at a sampling rate of 1000 Hz with the reference electrode placed at Cz. The electrode impedances were kept below 50 kΩ. The continuous EEG was saved for offline analysis. EEG Pre-processing. The EEG analysis was performed using EEGLAB  51  , FieldTrip  52  , the mTRF toolbox  38  , and custom scripts in MATLAB2014a  53.
Since the infant EEG recordings are noisy due to infant movements, we applied artifact subspace reconstruction (ASR  54  ) to remove noise. ASR uses a sliding window technique whereby each window of EEG data is decomposed via principal component analysis so it can be compared statistically with data from a calibration dataset. Within each sliding window the ASR algorithm identifies principal subspaces, which significantly deviate from the baseline EEG and then reconstructs these subspaces using a mixing matrix computed from the baseline EEG recording. In this study, we used a sliding window of 500 msec and a threshold of 20 standard deviations to identify corrupted subspaces. The noisy channels that were removed during the ASR procedure were later replaced by averaging the neighbouring channels weighted by distance.
EEG Power Analysis. First EEG data were down-sampled to 250 Hz for computational reasons. The EEG was then divided into one second non-overlapping epochs starting from the onset of the ADS and IDS recordings. EEG epochs with amplitude fluctuations exceeding ±100 µV were removed. The data were then re-referenced to the average of all the electrodes. All participants had at least 100 artifact-free epochs (corresponding to 100 seconds of EEG data; ADS M = 206. 5, SD = 25. 23; IDS M = 203. 52, SD = 31. 55). All artifact-free EEG data were analysed using a discrete Fourier transform (DFT), with a Hanning window of one second width and 50% overlap. Power (µV 2 ) was derived from the DFT output in the 4-8 Hz frequency band for ADS and IDS conditions. EEG power in the theta region was then computed across two scalp locations: frontal left and frontal right.
A lambda of 1 was selected for the current analysis. The resulting regression weights, referred to as temporal response functions (TRFs), were studied in terms of their spatial and temporal dynamics, similar to an ERP analysis. The TRFs obtained from individual participants were averaged to obtain the grand averaged TRF waveform. Five-fold leave-one-out cross-validation was used to assess how well the unseen EEG data could be predicted. This was achieved by using the TRF fit on 4 folds to predict the EEG data of the 5 th fold, and to iterate this procedure for all combinations. If EEG can be predicted with accuracy significantly greater than zero, it can be asserted that the EEG is reflecting the encoding of the envelope of speech  38. Prediction accuracy was measured by calculating Pearson's (r) linear correlation coefficient between the predicted and original EEG responses at each electrode channel.
Acknowledgements We would like to thank the mothers and infants for their participation in this study and  Maria Christou-Ergos  and  Scott O'Loughlin  for their assistance with data collection. This research was funded by the  Australian Research Council  grant  DP110105123 , 'The  Seeds of Literacy ' to  Professor Denis Burnham  at  MARCS Institute for Brain, Behaviour and Development at Western Sydney University , and  Professor Usha Goswami ,  Professor of Cognitive Developmental Neuroscience and Director, Centre for Neuroscience in Education at the University of Cambridge.
Bibliometric study of the RSVP related literature A bibliometric review of the RSVP-based BCIs was conducted. The inclusion criteria for this review were studies that focused on EEG data being recorded while users were performing visual search tasks using an RSVP paradigm. The studies involved various stimulus types presented using the RSVP paradigm where participants had to identify target stimuli. All reported studies where not simply theoretical and had at least one participant. One or more of the keywords BCI, RSVP, EEG or ERP appeared in the title, abstract or keyword list. Only papers published in English were included. The literature was searched, evaluated and categorized up until August 2017. The databases searched were Web of Science, IEEE, Scopus, Google Scholar, and PubMed. The search terms used were: "Rapid serial visual presentation", "RSVP", "Electroencephalography", "EEG", "Brain-Computer Interface", "BCI", "Event Related Potentials", "ERP and "Oddball" Papers were excluded for the following reasons: 1.
Variability in the distribution of accuracy outcome measures is also founded upon whether the dataset is non-parametric e. g. median AUC is reported as opposed to the mean AUC (Matran-Fernandez and Poli, 2014). As a measure of accuracy,  Rosenthal et al. (2014)  conducted a bootstrap analysis, to show the sampled distribution of AUC values for HDCA classifiers where 1000 times over, labels were randomized, classifiers were trained and AUC values calculated through a "leaving one-out cross-validation" technique. Cecotti et al. (2012)  presented a comparison of three class classifiers in a 'one versus all' strategy. The focus of  Cecotti et al. (2012)  was to compare the AUC to the volume under the ROC hyper-surface and the authors found a AUC of 0. 878, which is suggestive of the possibility for discrimination between greater than two types of ERPs using single-trial detection.
Duration of stimuli presentation A key factor of the RSVP paradigm is the rate of presentation, as the focus of this paradigm is presenting data at a rapid rate so that large datasets can be analyzed in short periods. The duration for which stimuli were presented varied from 50 to 500 ms  (Sajda, Gerson and Parra, 2003; Touryan et al. , 2011; B. Cai et al. , 2013). The upper limits for presentation time of stimuli during the RSVPparadigm is ill-defined in the literature; however we found 500 ms per image to be the maximum RSVP duration used across all RSVP studies. The duration of stimuli typically differs between applications. Table  3  shows that the most common duration of stimuli was between 100-199 ms per image. The quickest duration of 50 ms per image was used in a study by  (Sajda, Gerson and Parra, 2003)  where 2 participants were asked to identify scenes containing people in natural scenes.
, 2006; Lotte et al. , 2007). The use of neural networks, in particular deep neural network for RSVP-based BCI framework represents an attractive venture, and have shown promise over standard linear methods  (Manor, Mishali and Geva, 2016; Huang et al. , 2017). A convolution neural network was shown to outperform a two-step linear classifier using the same dataset  (Sadja et al. , 2014; Manor and Geva, 2015). The majority of studies reviewed investigate the effectivity of classifiers in identifying single-trial EEG correlates for target stimuli presented through an RSVP type paradigm. However, the spatial filtering technique as well as the type of classifier used has an impact on proficiency in detecting EEG of single trials  (Bigdely-Shamlo et al. , 2008; Cecotti, Eckstein and Giesbrecht, 2014).
Hybridizing RSVP BCIs with other BCI paradigms has also started to receive more attention  (Kumar and Sahin, 2013). Users of this system navigate using motor imagery movements (left, right, up and down). Search queries are spelt using the Hex-O-Speller and results retrieved from a web search engine may be fed back to the user using RSVP. This study shows the potential benefits of the RSVP paradigm and how it may be used in order to aid physically impaired users. Eye-tracking can be used as an outcome measure to assess and enhance RSVP stimuli and presentation modes. Specifically, using eye tracking researchers can establish where the participant's gaze is focused during erroneous trials and explore correlations between gaze variability and performance. With the RSVP-based BCI paradigm there is much scope to evaluate different data types/imagery.
Although the development of El Nin ˜o coincided with enhanced fire activity in Southeast Asia, fire emissions in the region were reported to be only half of the emissions during the previous El Nin ˜o in 1997/1998, following rainfall return in November 2015  [22]. GFED4. 1s [23]  reports fire emissions 0. 3 PgC yr 21 higher than the previous 5 years in 2015, but lower by 0. 1 PgC yr 21 in 2016 (table  1 ). Here we quantify the response of the terrestrial carbon cycle to El Nin ˜o in 2015/2016 using multiple data-based and modelled datasets. We track the evolution of anomalies in the net land -atmosphere CO 2 flux during the development and decline of the 2015/2016 El Nin ˜o estimated by two atmospheric transport model CO 2 inversions  [24, 25]  and compare them with the net terrestrial CO 2 uptake and its component fluxes (gross primary productivity (GPP), total ecosystem respiration (TER), fire) simulated by  16  LSMs in the latest TRENDY intercomparison project (v6, table  2 )  [1, 42].
Material and methods (a) Atmospheric CO 2 inversion fluxes Here we use three observation-based datasets of net landatmosphere surface fluxes: the Copernicus Atmosphere Monitoring Service (CAMS) atmospheric inversion (henceforth simply 'inversion') version 16r1  [24, 43] , and the Jena CarboScope inversion (update of  [25, 44]  compare with Ro ¨denbeck et al. [45] ) versions s76_v4. 1 and s04_v4. 1 (CarboScope76 and CarboScope04 henceforth). The inversions provide terrestrial (and oceanic) surface CO 2 fluxes, CAMS weekly fluxes at 1. 98latitude Â 3. 758longitude resolution, and CarboScope daily fluxes at 48latitude Â 58longitude resolution. CAMS 16r1 uses 119 atmospheric stations over the different time frames for which they provide data, starting in 1979. CarboScope76 (CarboScope04) uses 10 (59) stations continuously available throughout  1976-2016 (2004-2016).
(b) Land-surface models LSMs simulate the key energy, hydrological and carbon cycle processes in ecosystems, allowing insights on the mechanisms controlling anomalies in land -atmosphere CO 2 fluxes and their drivers. The TRENDY intercomparison project coordinated historical LSM simulations and compiled outputs of CO 2 fluxes among other variables  [42]. We use 16 LSMs from the latest TRENDYv6 simulations  [1]  (table 2), which provide monthly CO 2 fluxes during 1860 -2016. In TRENDYv6 S3 simulations, models are forced by historical data of (i) atmospheric CO 2 concentrations, (ii) climate observations from CRU-NCEP v8  [48, 49]  and (iii) human-induced land-cover changes and management from the HYDE  [50, 51]  and the Land-Use Harmonization LUH2 v2 h  [52]  datasets (extended to 2016 as described in  [1] ).
(c) Satellite-based data We compare anomalies from inversions and LSMs with two remote-sensing datasets that provide proxies for ecosystem activity and a satellite-based GPP product. CABLE Y N [26] CLASS-CTEM Y Y [27] CLM4. 5(BGC) Y Y [28] DLEM Y N [29] ISAM Y N [30] JSBACH Y Y [31] JULES Y N [32] LPJ Y annual [33] LPX-Bern Y Y [34] OCN Y N [35] ORCHIDEE Y N [36] ORCHIDEE- MICT Y Y [37] SDGVM Y annual [38] SURFEX N Y [39] VEGAS N N [40] VISIT Y Y [41] rstb. royalsocietypublishing. org Phil. Trans. R. Soc. B 373: 20170304 LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half of the green needle surface area in needleleaf canopies, which depicts the greenness of vegetation. We used Collection 6 Terra and Aqua MODIS LAI products (MOD15A2H and MYD15A2H)  [54, 55].
The original datasets were available as 8-day composites in 500 m sinusoidal projection. We checked the quality flags (clouds, aerosols, etc. ) to get high-quality LAI as described by Samanta et al. [56]. The original data were re-projected onto a 1/128 Â 1/128 grid by averaging the high-quality LAI. After that, the two LAI datasets were combined to bi-monthly timesteps by taking the mean of LAI values in each 8-day composite, weighted by the number of days that each 8-day composite locates in the specific half-month window. Finally, the annual average LAI and its anomaly relative to the record period (2000-2016) were calculated for each pixel. Anomalies in LAI reflect changes in the canopy leaf density and can therefore track plant stress response to drought. Cheng et al. [57]  used ground-based and remotely sensed land and atmospheric observations, combined with water use efficiency (WUE) model and evapotranspiration data from global land evaporation Amsterdam model (GLEAM), to calculate global annual GPP between 2000 and 2016 at 0.
As L-VOD shows a strong relationship with aboveground biomass stocks, the time-derivative of L-VOD can be directly related to variations in biomass, and thus comparable with the aboveground component of NBP. The three datasets show consistent variability patterns over the 37-year period, but GCB2017 estimates stronger residual sink anomalies in certain years, e. g. 1991 ( positive) or 1997 (negative). Although individual LSMs do not necessarily capture the main variability patterns of NBP reported by inversions, the multi-model ensemble mean (MMEM) is generally close to inversion values both globally and in the tropics. The exception in 1991/1992 is likely to be due to the response of the ecosystems to the variations in net direct and diffuse fraction of short-wave radiation following.