dataesr/scientific-paragraphs-categorization huggingface dataset:
5 12+log[O/H] 9. 0; 2) magnitude: -21. 3 M absolute -13. 5 mag; 3) surface brightness: 18 µ V 30 mag arcsec - 2  ; and 4) galactic environments: 1/3 isolated objects, 2/3 in groups, with no strongly interacting systems. The group property of the galaxies was extracted from NED 2 As the data is analyzed, the selection can be adjusted to properly sample the different environments. For example, if we gather enough high metallicity and dense environment regions, we will then concentrate on the small irregular galaxies with a low-metallicity. We will prioritize the completion of the science goals and that depends mostly on the sample of H II regions and less on the completion of one particular target. Among SIGNALS'galaxy sample, 7 targets are very close, resulting in a spatial resolution below 3 pc in the best observing conditions.
A mosaic of several fields with small overlaps is used for these objects to cover most of the starformation activity. Figure  1  shows an assemblage of four fields in M33 from the SIGNALS pilot project obtained in 2017. Reaching such a high spatial resolution is essential for some objectives of the project, i. e. to resolve gas ionization structures and the ionizing star content of the nebulae (e. g. faint end of the H II region luminosity function, calibration of the models with resolved structure of ionized gas, etc. ). The spatial resolution of the sample ranges from 2 to 40 pc. It should complement the MUSE survey over the nor- thern sky (PHANGS/MUSE). Out of 19 galaxies, SIGNALS has 6 are in common with PHANGS/MUSE. We also compiled existing data from HST+LEGUS (28 galaxies), GA-LEX (UV, 39 galaxies), Spitzer (IR, 43 galaxies), BIMA and ALMA (CO, 23 galaxies), VLA (HI, 42 galaxies), as well as those obtained with narrow-band filters.
Wavelength Calibration using Sky Lines As mentioned in Section 2. 2, the wavelength calibration provided by the data reduction pipeline ORBS is performed by measuring the position of the laser line on a He-Ne datacube through the FOV, and applying the measured centroid map to scale the science cube spectral axis (Martin & Drissen 2017). Some velocity residual errors remain from this technique since the science observations are made at different on-sky positions than those of the calibration laser (zenith). As described in  Martin et al. (2016)  and  Martin et al. (2018) , the sky lines observed in the SN3 filter can be used to correct residual errors. A simultaneous fit of all the bright sky lines available in the bandpass is made using the ORCS analysis tool. The resulting velocity map is interpolated over the FOV and a correction map is then applied to any subsequent velocity map extracted from the emission line with ORCS.
Notably, the flux of faint areas covered by fibers (for fiber-fed systems) becomes negligible resulting in a total flux per resolution element dominated by the brightest zones. Areas not covered by fibers do not contribute to the resulting map. Our analysis of the science verification data allowed us to identify 4285 H II region candidates in NGC 628 using a tailored procedure developed for the SITELLE data at a spatial resolution of 35 pc. Details are published in Rousseau-Nepton et al. (2018). There are two advantages of the IFTS that our procedure uses: the full spatial coverage and the spectral information. By combining the flux of three emission lines (Hα, Hβ, and [O III]λ5007), we increased the detection threshold and improved the detection of the low metallicity regions with much fainter recombination lines.
This code provides flexibility in defining the regions and their DIG background. Our method proceeds through multiple steps: 1)the identification of the emission peaks; 2) the determination of the zone of influence around each peak; and 3) the definition of the outer limit of a region and its DIG background. Figure  7  shows the border of a few H II regions derived from this procedure. For SIGNALS, it will be adapted to different spatial resolutions to make sure that the regions are uniformly defined. SITELLE's dataset for NGC 628 was used to recover the luminosity function for different samples of H II regions as shown in  Rousseau-Nepton et al. (2018). These luminosity functions, established from 2000 -4000 regions, are well sampled and provide interesting insights on the active star formation processes within that galaxy.
9). For any region with a surface brightness greater than 3. 4 × 10 -16 erg s -1 cm -2 arcsec -2 and a radius of 2 , a SNR of 3 would be reached for any line with a flux greater than 3% of Hα (or log[line/Hα] ≥ -1. 5). According to photoionization models  (Vale Asari et al. 2016) , this corresponds to virtually all the regions for [N II]λ6583, [S II]λλ6716,6731, and [O II]λ3727. Figure  8  shows the expected ratios of the strong lines over Hα for a set of models from the Bayesian Oxygen and Nitrogen abundance Determinations database (BOND, Vale  Asari et al. 2016)  including models for ages ranging from 1 to 6 Myr, optical depth of 1. 0, log[N/O] ranging from 0 to -2. 0, 12+log[O/H] ranging from 6. 5 and 9. 5, and ionization parameters ranging from -2. 5 to -4 (value observed in the CALIFA sample,  Morisset et al.
The model has been used to investigate the conditions under which the different sources of feedback dominate and the amount of radiation that escapes through the shell. It was also used to derive the minimum star formation efficiency for a large parameter space of clouds and clusters  (Rahner et al. 2017). The same approach was used to explain the two stellar populations in NGC 2070 in the LMC, which hosts the younger cluster R136 in its center  (Rahner et al. 2018). Because this method is computationally very efficient, it will allow us to consider a large range of parameters. We are currently compiling a comprehensive database where we vary the star formation efficiency, the cloud mass and density profile, and the metallicity of both the gas and the stars. Each model is currently being post-processed using CLOUDY to make detailed predictions for the time evolution of the line and continuum emission associated with the system.
2019 ) and photoionization models have been developed to trace back the spectral energy distribution of the source of ionization. Several sources of ionization might be responsible for the DIG : ionizing photons escaping H II regions that travel long distances  (Ferguson et al. 1996; Haffner et al. 2009; Zurita et al. 2000; Oey et al. 2007; Barnes et al. 2014; Howard et al. 2016 Howard et al. , 2017)) , a weak AGN  (Ho 2008; Davies et al. 2014) , a generation of post-AGB stars  (Binette et al. 1994; Flores-Fajardo et al. 2011) , and fast shocks in the ISM  (Allen et al. 2008; Hoffmann et al. 2012). Some stu-   2018 ), using circular regions only, along with simulations CLOUDY using post-processing of evolutionary tracks from the WARP-FIELD code  (Rahner et al. 2019; Pellegrini et al. 2019).
DATA PRODUCTS Figure  14  summarizes the diverse processing steps presented in this paper, from the observational phase to the extraction of the physical parameters. The process differs between nearby (i. e. spatial resolution 15 pc) and more distant targets. The ORCS software can simultaneously or individually fit lines in each datacube while returning maps for each line including the amplitude, FWHM, continuum height, flux, velocity, velocity dispersion, and their corresponding uncertainties. The H II regions identified with our procedure will be included in a catalog that will contain the raw emission line measurements from the datacubes obtained after data processing and the stellar continuum subtraction. The median profile of each line will also be fitted as in  Rousseau-Nepton et al.
(2018)  ; the fitting parameters will be provided in the catalog. Extinction corrected values will also be provided as explained in Section 4. 4. The absolute position (right ascension and declination) of the center of each region will be provided as well as the name of the host galaxies. Additional data products extracted from complementary data will be added to the catalog when available (e. g. the mean µ V around the regions, and the total HI and CO emission evaluated over the region radius). Data products will be distributed to the collaborators and other communities from the SIGNALS Website. The HDF5 files containing the reduced datacubes are available without any proprietary period from the program 18BP41, 19AP41,. P41, etc, via the Canadian Astronomy Data Centre Website 3. Data release of additional products following the analysis will be made available later from the SIGNALS Website.
LEGACY VALUE SIGNALS fills the gap between other galaxy surveys focusing on the local Universe, from the upcoming SDSS-V LVM survey to current surveys such as MAD, PHANGS, CALIFA, SAMI, MANGA, etc. It will provide new, key insights into star formation and feedback mechanisms driving the evolution of the ISM in star-forming galaxies. By using multiple nebular lines, physical properties like abundances, ionization structures, and dynamics will be obtained as well as information on the impact of local environments on the star formation process. The rich SIGNALS dataset will be valuable for investigating other complementary astrophysical topics. Some possibilities include: 1) planetary nebula abundance distributions and luminosity functions  (Kreckel et al. 2017; Martin et al. 2018) ; 2) supernova remnant ionization conditions, occurrence, and feedback contribution  (Moumen et al.
We present a publicly available dataset of 227 healthy participants comprising a young (N = 153, 25. 1 ± 3. 1 years, range 20-35 years, 45 female) and an elderly group (N = 74, 67. 6 ± 4. 7 years, range 59-77 years, 37 female) acquired cross-sectionally in Leipzig, Germany, between 2013 and 2015 to study mind-bodyemotion interactions. During a two-day assessment, participants completed MRI at 3 Tesla (resting-state fMRI, quantitative T1 (MP2RAGE), T2-weighted, FLAIR, SWI/QSM, DWI) and a 62-channel EEG experiment at rest. During task-free resting-state fMRI, cardiovascular measures (blood pressure, heart rate, pulse, respiration) were continuously acquired. Anthropometrics, blood samples, and urine drug tests were obtained. Psychiatric symptoms were identified with Standardized Clinical Interview for DSM IV (SCID-I), Hamilton Depression Scale, and Borderline Symptoms List.
For example, psychological stress has negative influences on somatic and mental health  10  and emotional episodes like depressive symptoms have been discussed as a risk factor for stroke  11  , coronary heart disease  12  or diabetes  13. Informed by these recent studies, we investigate psychological factors that play a key role in the pathogenesis, development, and treatment of somatic diseases in a multi-modular approach. This "mindbody-emotion" approach emphasizes the bi-directionality of brain-body interactions as they underlie mental phenomena and the importance of psychological factors for somatic health and disease. In the "Leipzig Study for Mind-Body-Emotion Interactions" (LEMON), we acquired a large dataset of physiological, psychological, and neuroimaging measures in younger and older healthy adults.
The LEMON dataset provides the following advantages: 1. Subjects underwent an extensive medical and psychological selection procedure: Past and current somatic or mental illnesses as well as current medication status are well-controlled and documented. Careful adherence to selection and "health" criteria is especially important when investigating healthy aging. 2. Psychometric tools to assess cognitive and socio-emotional characteristics are tailored to relate them to bodily and brain measures. The LEMON dataset thereby enables basic research on the healthy interaction between brain, mind, and bodyas it is assumed to be altered in somatic and mental illness. 3. LEMON complements data of brain structure and brain function with extensive bodily measures. Measures of peripheral physiology serve a double function of being utilized for removing artifacts from the Magnetic Resonance Imaging (MRI) data (as e.
In summary, the LEMON dataset is particularly suited to comprehensively relate cognitive and emotional traits or states to physiological characteristics of brain and body. While focusing on fundamental mind-body-emotion interactions in healthy younger and older adults, our data and results may inform clinical research. Here, we present the study's objectives, design, and methods together with available data types, their quality and quantities. The dataset presented here was acquired as one of two complementary data acquisition protocols on a partially overlapping cohort of participants which constitute the MPI-Leipzig Mind-Brain-Body database. All MRI data of both projects were acquired on the same scanner. Taken in conjunction with the data acquired in the complementary project by Mendes et al.
Physiological data MRI. Magnetic resonance imaging (MRI) was performed on a 3 Tesla scanner (MAGNETOM Verio, Siemens Healthcare GmbH, Erlangen, Germany) equipped with a 32-channel head coil. Over the course of MRI data acquisition, the scanner remained stable and did not undergo any major maintenance or updates which would systematically affect the quality of data provided here. This is also true in relation to the complementary protocol by Mendes et al. , ensuring comparability between the studies. The imaging protocol lasted approximately 70 min and included the following scans in fixed order: 1) gradient echo fieldmap scan for distortion correction in rs-fMRI  74, 75  , 2) a pair of spin echo images with reversed phase encoding direction for distortion correction in rs-fMRI  76, 77  , 3) rs-fMRI scan, 4) a second pair of spin echo images with reversed phase encoding direction, 5) quantitative and weighted T1 Magnetization-Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE)  78  image, 6) T2-weighted image, 7) Fluid-attenuated inversion recovery (FLAIR) scan, 8) diffusion-weighted imaging (DWI) scan, 9) a pair of spin echo images with reversed phase encoding for distortion correction in DWI, 10) T2*/ susceptibility-weighted imaging (SWI) scan.
The data were acquired with a very large coverage using simultaneous multi-slice acquisition to include the brain and the cerebellum. Diffusion data were acquired parallel to the AC-PC line and the volume (149. 6 mm height) covered the entire brain including the cerebellum in all participants. The fMRI data were angulated by -15°(backwards) with respect to the AC-PC line. The slice block (147 mm) also covered the entire brain including the full cerebellum. The figures in Supplementary Figure  S1  show cross-subject coverage of the fMRI (left) and DWI (right) data normalized to the MNI brain. During rs-fMRI, electrocardiography (ECG), pulse, beat-to-beat blood pressure and respiration were recorded simultaneously (see section Continuous peripheral physiological recordings during rs-fMRI). Before imaging started, participants filled out the first MDBF questionnaire.
Once imaging was completed, participants were asked to fill out the New York Cognition Questionnaire (NYC-Q, for details on the questionnaires see section Emotion and Personality Test Battery). Resting-state fMRI (rs-fMRI): A T2 * -weighted gradient echo echo planar imaging (EPI) multiband BOLD rs-fMRI scan  [79] [80] [81]  was acquired to enable functional connectivity analyses. Participants were instructed to remain awake and lie still with their eyes open while looking at a low-contrast fixation cross. Data regarding sleep/wake for the rs-fMRI as such does not exist, but it is assumed that the participants were awake throughout the duration of the scan because they were requested to do so. The sequence parameters were specified as follows: TR = 1400 ms and the total number of volumes = 657 (for more details see Table  7 ).
The total acquisition time for rs-fMRI was 15 min 30 s. To enable correction for geometric distortions in EPI images from rs-fMRI, a gradient echo fieldmap scan and two pairs of spin echo EPI images with reversed phase encoding direction were acquired. Resting-state fMRI Data Preprocessing: The preprocessing of the rs-fMRI data was implemented in Nipype and the details of it can be found in the complementary project by Mendes et al. 21     84  , (v) denoising (Nipype rapidart and aCompCor)  85  , (vi) band-pass filtering between 0. 01-0. 1 Hz (FSL), mean-centering, as well as variance normalization of the denoised time series (Nitime)  86  , (vii) spatial normalization to MNI152 2 mm standard space via transformation parameters derived during structural preprocessing (ANTs SyN)  87. Structural MRI T1 and T2: The MP2RAGE  78  sequence was acquired for assessment of brain structure with a voxel resolution of 1 mm (isotropic).
Resulting T1-weighted images and quantitative T1 maps can be used for analyses of gray and white matter (e. g. , cortical thickness, voxel-based morphometry), as well as for the assessment of myelin content  88, 89. Importantly, these T1-weighted images differ from MPRAGE T1-weighted images as they are uniform and free of other imaging properties (i. e. proton density, T2 * ) which can affect morphometric measurements  90. The total acquisition time for MP2RAGE was 8 min 22 s. In addition, a standard T2-weighted volume with 1 mm isotropic resolution was acquired within 4 min 43 s (for details see Table  7 ). T1 Data Preprocessing: The preprocessing of the T1 MP2RAGE data was implemented in Nipype and the details of it can be found in the complementary project by Mendes et al. 21. The pipeline is available at https://github.
Susceptibility-weighted data were acquired using a three-dimensional (3D) flow-compensated fast low-angle shot (FLASH) sequence (for parameter details see Table  7 ) in a sub-sample which was introduced only after 112 participants. Highquality phase maps (i. e. excluding coil-combination pole artifacts) were reconstructed from multi-channel complex signals using an automated, data-driven coil combination method (SVD-ESPIRiT)  99, 100. Both magnitude and phase images are provided for SWI and QSM calculation which could be achieved using varied techniques  96, 101. The total time of acquisition was 7 min 50 s. Continuous Peripheral Physiological Recordings During rs-fMRI. During the 15 min 30 s acquisition of resting-state fMRI, continuous beat-to-beat blood pressure (NIBP), electrocardiography (ECG), pulse, and respiration were recorded non-invasively with MR-compatible devices.
Blood pressure and pulse via photoplethysmography were recorded with a BIOPAC MP150 acquisition system (BIOPAC Systems Inc. , Goleta, CA, USA) and the acquisition software AcqKnowledge (Version 4. 0, BIOPAC Systems Inc. , Goleta, CA, USA). In addition to the MP150 main hardware unit, blood EEG. Resting-state EEG (rs-EEG) was obtained in 216 participants who completed the second MDBF just before the EEG recording and underwent the Multi 8/2 drug strip test. The whole experiment session took approximately 1. 5 hours (including the 16-minute EEG recording). The raw rs-EEG data with preprocessed rs-EEG and localizer files are openly available. Resting-state EEG: A 16-min rs-EEG was recorded with a BrainAmp MR plus amplifier in an electrically shielded and sound-attenuated EEG booth using 62-channel (61 scalp electrodes plus 1 electrode recording the VEOG below the right eye) active ActiCAP electrodes (both Brain Products GmbH, Gilching, Germany) attached according to the international standard 10-20 extended localization system, also known as 10-10 system,  103  and referenced to FCz.
Resting-State EEG Data Preprocessing: Data from 13 participants were excluded due to missing event information, different sampling rate, mismatching header files or insufficient data quality. The raw EEG data from 203 participants used for preprocessing was downsampled from 2500 Hz to 250 Hz, bandpass filtered within 1-45 Hz (8th order, Butterworth filter) and split into EO and EC conditions for the subsequent analyses. Outlier channels were rejected after visual inspection for frequent jumps/shifts in voltage and poor signal quality. Data intervals containing extreme peak-to-peak deflections or large bursts of high frequency activity were identified by visual inspection and removed. Intervals containing traces from eye blinks or eye movements were not removed at this stage. Further data preprocessing was done in EEGLAB 105 (version 14.
1. 1b) for MATLAB (Delorme and Makeig, 2004). The dimensionality of the data was reduced using principal component analysis (PCA), by keeping PCs (NZ30) that explain 95% of the total data variance. Next, independent component analysis  106  (ICA) was performed using the Infomax (runica) algorithm. Components reflecting eye movement, eye blink or heartbeat related artifacts were removed. Retained independent components for EO (mean: 19. 7, range: 9-30) and EC (mean: 21. 4, range: 14-28) conditions were back-projected to sensor space for further analysis. Additional Measures. Seated Resting Blood Pressure: Blood pressure (BP) was measured using an automatic oscillometric blood pressure monitor (OMRON M500, OMR HEM-7213-D) and a 22-42 cm arm cuff (OMRON HEM-RML30, both OMRON Medizintechnik Handelsgesellschaft mbH, Mannheim, Germany) after a seated resting period of 5 min.
All data comprised in the MPI-Leipzig Mind-Brain-Body database were derived from MPI-CBS so data import into XNAT was done from our local secured network. A specially customized XNAT uploader was used to upload all participants' data to XNAT. The native DICOM format was used for MRI data, whilst a standard ASCII ( *. csv, *. txt) format was employed to upload all other experimental data such as surveys, test batteries, and demographical data in XNAT also in local secured network. The anonymization measures applied to the MRI data consisted of removal of DICOM header tags containing information which could lead to the identification of test participants as well as the defacing of all structural (NIFTI) scans. This applied mainly for internal use. For releasing the data publicly, the MRI data in NIFTI files in JavaScript Object Notation (*.
MRI Data All MRI datasets are made available in NIFTI format, and all anatomical scans have been defaced. For more details see Mendes et al. The dataset is organized in concordance with the Brain Imaging Data Structure (BIDS) format. This facilitates data analysis, for example with BIDS-Apps 107 (http://bids-apps. neuroimaging. io). BIDS-Apps encapsulate standard MRI analysis tools within an application that understands the BIDS format and allows automatic access to relevant data and metadata. The MRI raw and preprocessed data can be found in GWDG (https://ftp. gwdg. de/pub/misc/MPI-Leipzig_Mind-Brain-Body-LEMON/) as well as in Functional Connectomes Project International Neuroimaging Data-Sharing Initiative/Child Mind Institute (Data Citation 1) and OpenNeuro repository (Data Citation 2).
txt file format with item details and Likert scores. For each participant, the average age across the course of the study was reported which was the same in both supplementary studies of MPI Leipzig Mind-Brain-Body database. For the purpose of anonymity, the mean age was then binned into five year width (5-year bins). Cutoff values for binning were 20. 0, 25. 0, 30. 0 and so forth. A meta file with demographic summary in *. csv format includes: gender, age (5-year bins), handedness, formal education, drug test results on day 2, smoking status, SKID, HAM-D, BLS-23, AUDIT, and relationship status. Separate subject-specific (. csv) files with information given in Text (. txt) file include the results of blood sample, blood pressure (for 3 occasions), and anthropometry. In addition to this meta file, we include a data availability (.
83I sequences. availableathttps://github. com/NeuroanatomyAndConnectivity/pipelines/tree/master/src/lsd_lemonandcomprised the following steps: (i) discarding the first five EPI volumes to allow for signal equilibration and steady state, (ii) 3D motion correction (FSL MCFLIRT)82, (iii) distortion correction (FSL FUGUE)83, (iv) rigid-body coregistration of unwarped temporal mean image to the individual's anatomical image (FreeSurfer bbregister).