1Interquartile range,2Kruskal-Wallis test.

We include a description of the data sets in the meta-data as well as sample code and results from a simulated data set. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The R code is available on line here: https://res1githubd-o-tcom.vcapture.xyz/warrenjl/SpGPCW. Format: Abstract The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publicly available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. File format: R workspace file. Metadata (including data dictionary) • y: Vector of binary responses (1: preterm birth, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate). This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

Number of patients for each group are indicated at the top, and the number of samples available for each analysis are indicated in each cell.

True values are η = (0.5, 1.0, 3.5) and p = (0.2, 0.3, 0.5).

These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: File format: R workspace file; “Simulated_Dataset.RData”. Metadata (including data dictionary) • y: Vector of binary responses (1: adverse outcome, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate) Code Abstract We provide R statistical software code (“CWVS_LMC.txt”) to fit the linear model of coregionalization (LMC) version of the Critical Window Variable Selection (CWVS) method developed in the manuscript. We also provide R code (“Results_Summary.txt”) to summarize/plot the estimated critical windows and posterior marginal inclusion probabilities. Description “CWVS_LMC.txt”: This code is delivered to the user in the form of a .txt file that contains R statistical software code. Once the “Simulated_Dataset.RData” workspace has been loaded into R, the text in the file can be used to identify/estimate critical windows of susceptibility and posterior marginal inclusion probabilities. “Results_Summary.txt”: This code is also delivered to the user in the form of a .txt file that contains R statistical software code. Once the “CWVS_LMC.txt” code is applied to the simulated dataset and the program has completed, this code can be used to summarize and plot the identified/estimated critical windows and posterior marginal inclusion probabilities (similar to the plots shown in the manuscript). Optional Information (complete as necessary) Required R packages: • For running “CWVS_LMC.txt”: • msm: Sampling from the truncated normal distribution • mnormt: Sampling from the multivariate normal distribution • BayesLogit: Sampling from the Polya-Gamma distribution • For running “Results_Summary.txt”: • plotrix: Plotting the posterior means and credible intervals Instructions for Use Reproducibility (Mandatory) What can be reproduced: The data and code can be used to identify/estimate critical windows from one of the actual simulated datasets generated under setting E4 from the presented simulation study. How to use the information: • Load the “Simulated_Dataset.RData” workspace • Run the code contained in “CWVS_LMC.txt” • Once the “CWVS_LMC.txt” code is complete, run “Results_Summary.txt”. Format: Below is the replication procedure for the attached data set for the portion of the analyses using a simulated data set: Data The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publically available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics, and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

Context

Our target was to predict gender, age and emotion from audio. We found audio labeled datasets on Mozilla and RAVDESS. So by using R programming language 20 statistical features were extracted and then after adding the labels these datasets were formed. Audio files were collected from "Mozilla Common Voice" and “Ryerson AudioVisual Database of Emotional Speech and Song (RAVDESS)”.

Content

Datasets contains 20 feature columns and 1 column for denoting the label. The 20 statistical features were extracted through the Frequency Spectrum Analysis using R programming Language. They are: 1) meanfreq - The mean frequency (in kHz) is a pitch measure, that assesses the center of the distribution of power across frequencies. 2) sd - The standard deviation of frequency is a statistical measure that describes a dataset’s dispersion relative to its mean and is calculated as the variance’s square root. 3) median - The median frequency (in kHz) is the middle number in the sorted, ascending, or descending list of numbers. 4) Q25 - The first quartile (in kHz), referred to as Q1, is the median of the lower half of the data set. This means that about 25 percent of the data set numbers are below Q1, and about 75 percent are above Q1. 5) Q75 - The third quartile (in kHz), referred to as Q3, is the central point between the median and the highest distributions. 6) IQR - The interquartile range (in kHz) is a measure of statistical dispersion, equal to the difference between 75th and 25th percentiles or between upper and lower quartiles. 7) skew - The skewness is the degree of distortion from the normal distribution. It measures the lack of symmetry in the data distribution. 8) kurt - The kurtosis is a statistical measure that determines how much the tails of distribution vary from the tails of a normal distribution. It is actually the measure of outliers present in the data distribution. 9) sp.ent - The spectral entropy is a measure of signal irregularity that sums up the normalized signal’s spectral power. 10) sfm - The spectral flatness or tonality coefficient, also known as Wiener entropy, is a measure used for digital signal processing to characterize an audio spectrum. Spectral flatness is usually measured in decibels, which, instead of being noise-like, offers a way to calculate how tone-like a sound is. 11) mode - The mode frequency is the most frequently observed value in a data set. 12) centroid - The spectral centroid is a metric used to describe a spectrum in digital signal processing. It means where the spectrum’s center of mass is centered. 13) meanfun - The meanfun is the average of the fundamental frequency measured across the acoustic signal. 14) minfun - The minfun is the minimum fundamental frequency measured across the acoustic signal 15) maxfun - The maxfun is the maximum fundamental frequency measured across the acoustic signal. 16) meandom - The meandom is the average of dominant frequency measured across the acoustic signal. 17) mindom - The mindom is the minimum of dominant frequency measured across the acoustic signal. 18) maxdom - The maxdom is the maximum of dominant frequency measured across the acoustic signal 19) dfrange - The dfrange is the range of dominant frequency measured across the acoustic signal. 20) modindx - the modindx is the modulation index, which calculates the degree of frequency modulation expressed numerically as the ratio of the frequency deviation to the frequency of the modulating signal for a pure tone modulation.

Acknowledgements

Gender and Age Audio Data Souce: Link: https://commonvoice.mozilla.org/en Emotion Audio Data Souce: Link : https://smartlaboratory.org/ravdess/

Title: Face-to-face Peer Dialogue: Students Talking about Feedback (submitted March 2021) A short description of the study set-up: 35 second-year university students were split into 12 groups. Students wrote a scientific report and gave written peer feedback. This was followed by face-to-face peer dialogue on the feedback without teacher facilitation. Dialogues were coded and analysed at the utterance level. Analysis For data analysis, we used the coding scheme by Visschers-Pleijers et al. (2006), which focuses on the analysis of verbal interactions in tutorial groups. To assess the dialogue, the verbal interactions in the discourses were scored at the utterance level as ‘Learning-oriented interaction’, ‘Procedural interaction’ or ‘Irrelevant interaction’ (Visschers-Pleijers et al. 2006). The Learning-oriented interactions were further subdivided in five subcategories: Opening statement, Question (open, critical or verification question), Cumulative reasoning (elaboration, offering suggestion, confirmation or intention to improve), Disagreement (counter argument, doubt, disagreement or no intention to improve) and Lessons learned (an adapted version of the coding scheme used by Visschers-Pleijers et al. 2006). The first and second authors, and a research assistant coded the first four transcripts and discussed their codes in three rounds until they reached consensus. See Appendix A for a description of the coding scheme. After reaching consensus on the coding, the first author and the research assistant, individually coded four new transcripts. For these four transcripts, interrater reliability analysis was performed using percent agreement according Gisev, Bell, and Chen (2013). The percent agreement between the first author and the research assistant ranged from 80 to 92. The first author then coded the remaining eight transcripts individually. Eventually, all transcripts were analysed according to the first author’s classification. For each single group session, the codes for each (sub)category of verbal interaction were counted and percentages were calculated for the number of utterances. The median (Mdn) and interquartile range (IQR) of percentage of utterances for each (sub)category of code were computed per coding category for all groups together. Explanation of all the instruments used in the data collection (including phrasing of items in surveys): This was a discourse analysis (see final coding scheme: separate file). Explanation of the data files: what data is stored in what file? • Final coding scheme (in Word). • Audiotapes (in MP3) and transcripts of 12 groups (in Word). • Data study 4 (in Excel). • Resulting data in table (in Word). In case of quantitative data: meaning and ranges or codings of all columns: • Data study 4 (in Excel): numbers and percentages of interactions. • Resulting data (Table in Word): per group (n=12) in percentages and medians In case of qualitative data: description of the structure of the data files: Not applicable

Mean, median, and interquartile range (IQR) scores for each concern according to gender, type of diagnosis, language, and age.

This dataset provides geospatial location data and scripts used to analyze the relationship between MODIS-derived NDVI and solar and sensor angles in a pinyon-juniper ecosystem in Grand Canyon National Park. The data are provided in support of the following publication: "Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States". The data and scripts allow users to replicate, test, or further explore results. The file GrcaScpnModisCellCenters.csv contains locations (latitude-longitude) of all the 250-m MODIS (MOD09GQ) cell centers associated with the Grand Canyon pinyon-juniper ecosystem that the Southern Colorado Plateau Network (SCPN) is monitoring through its land surface phenology and integrated upland monitoring programs. The file SolarSensorAngles.csv contains MODIS angle measurements for the pixel at the phenocam location plus a random 100 point subset of pixels within the GRCA-PJ ecosystem. The script files (folder: 'Code') consist of 1) a Google Earth Engine (GEE) script used to download MODIS data through the GEE javascript interface, and 2) a script used to calculate derived variables and to test relationships between solar and sensor angles and NDVI using the statistical software package 'R'. The file Fig_8_NdviSolarSensor.JPG shows NDVI dependence on solar and sensor geometry demonstrated for both a single pixel/year and for multiple pixels over time. (Left) MODIS NDVI versus solar-to-sensor angle for the Grand Canyon phenocam location in 2018, the year for which there is corresponding phenocam data. (Right) Modeled r-squared values by year for 100 randomly selected MODIS pixels in the SCPN-monitored Grand Canyon pinyon-juniper ecosystem. The model for forward-scatter MODIS-NDVI is log(NDVI) ~ solar-to-sensor angle. The model for back-scatter MODIS-NDVI is log(NDVI) ~ solar-to-sensor angle + sensor zenith angle. Boxplots show interquartile ranges; whiskers extend to 10th and 90th percentiles. The horizontal line marking the average median value for forward-scatter r-squared (0.835) is nearly indistinguishable from the back-scatter line (0.833). The dataset folder also includes supplemental R-project and packrat files that allow the user to apply the workflow by opening a project that will use the same package versions used in this study (eg, .folders Rproj.user, and packrat, and files .RData, and PhenocamPR.Rproj). The empty folder GEE_DataAngles is included so that the user can save the data files from the Google Earth Engine scripts to this location, where they can then be incorporated into the r-processing scripts without needing to change folder names. To successfully use the packrat information to replicate the exact processing steps that were used, the user should refer to packrat documentation available at https://cran.r-project.org/web/packages/packrat/index.html and at https://www.rdocumentation.org/packages/packrat/versions/0.5.0. Alternatively, the user may also use the descriptive documentation phenopix package documentation, and description/references provided in the associated journal article to process the data to achieve the same results using newer packages or other software programs.

aExcluding costs for international consultants (see Table 5);bEstimates only used in analysis from societal perspective;CI: Confidence intervals, IQR: Interquartile range, ZMO: Zonal medical officer.

Median values in milliseconds [interquartile range]. SDNN; standard deviation of all normal (NN) intervals measured between consecutive sinus beats, SDANN; standard deviation of the average NN intervals, Pre, stress and post; pre-stress, during stress and post-stress measurements, BB; beta-blocker, *p-value for difference within each group.The non-parametric Mann-Whitney U-test was used to compare the median values for the two independent groups. Wilcoxon Signed Rank test was used to compare the median value within each group.

A short description of the study set-up: Second-year university students (N=84) participated in a mixed-method study that included questionnaires and focus groups. The intervention comprised face-to-face dialogue in small groups about the participants’ written peer feedback on a draft report. Instruments Questionnaires A pre-intervention questionnaire before the start of the face-to-face dialogue measured students’ beliefs about written peer feedback (part 1). For this purpose, a validated questionnaire by Huisman, Saab, van Driel, et al. (2019) was used to measure four components: 1) degree to which peer feedback is perceived as meaningful and useful (3 items), 2) the degree to which peer feedback is considered an important skill (3 items), 3) confidence in quality of provided peer feedback (2 items) and 4) confidence in quality of received peer feedback (2 items). A five-point Likert scale was employed, ranging from 1 (=‘Completely disagree’ or ‘Completely not applicable to me’) to 5 (=‘Completely agree’ or ‘Completely applicable to me’). In part 2, students rated the presence of written peer feedback in terms of feed-up, feed-back and feed-forward information for which an adjusted version of a validated questionnaire by De Kleijn, Bronkhorst, Meijer, Pilot, and Brekelmans (2016) was used. This part of the questionnaire was also on a five-point Likert scale, ranging from 1 (=‘Agree not at all’) to 5 (=‘Agree a lot’) and contained four items about Feed up, six items about Feed back and five items about Feed forward. The pre-intervention questionnaire also measured the overall instructiveness of the written feedback on a 10-point scale (1=lowest, 10=highest). A post-intervention questionnaire measured students’ perception of improved understanding of the written feedback through face-to-face peer dialogue and the quality of this dialogue in terms of overall instructiveness, which was measured on a 10-point scale. The post-intervention questionnaire also contained items about Feed up (4 items), Feed back (6 items) and Feed forward (5 items). As in the pre-intervention questionnaire, these items were answered on a five-point Likert scale. A pilot study was conducted to test clarity of both pre- and post-intervention questionnaires items. Focus group Students were invited to participate in a focus group, which resulted in two groups of volunteers: N=9 (3 males, 6 females) and N=7 (4 males, 3 females). The participants all originated from different dialogue groups. Semi-structured, post-measurement interviews were conducted to search for explanations as to why dialogue improved students’ understanding and to distinguish important conditions for better understanding. The focus group sessions lasted one hour and were guided by a moderator (first author) while a second member of the research team (fourth author) acted as observer. The moderator and observer did not know the focus group members. Both interviews were audiotaped. Analysis Quantitative analysis Reliability analysis was performed for each subscale of ‘student beliefs’, as well as for Feed up, Feed back and Feed forward. The reliability of the subscales varied from 0.72 to 0.85, which was considered acceptable (Tavakol & Dennick, 2011). For all pre- and post-intervention variables, the median (Mdn) and interquartile range (IQR) was calculated, besides mean (M) and standard deviation (SD). The authors considered a median score equal or above 4.0 (scale 1–5) or 8.0 (scale 1–10) as very positive. A median score equal or below 3.0 (scale 1–5) or 6.0 (scale 1–10) was considered insufficient, while all the other scores were considered to be positive. A non-parametric Wilcoxon signed-rank test was performed to compare scores on ‘Instructiveness of written feedback’ and ‘Instructiveness of face-to-face dialogue’. Non-parametric Wilcoxon signed-rank tests were also performed to compare scores on pre- and post-intervention subscales of Feed up, Feed back and Feed forward. All tests were performed on the 5% level of significance. Qualitative analysis Both focus groups sessions were transcribed verbatim and two authors (moderator and observer) first analysed the transcripts in a theoretically thematic way (Braun & Clarke, 2006). This method involves deductive or top-down analysis, led by the research questions. Making an inventory of phrases related to the explanations and conditions for an improved understanding by dialogue led the analysis of the transcripts. To this end, in the first phase of the analysis and in an iterative process of three separate rounds, both authors formulated a set of themes comprising explanations and conditions. In the next phase, all authors discussed the formulated themes and reached consensus through discussion. Explanation of the data files: what data is stored in what file? The data files contain 84 anonymized pdf’s of the original questionnaires filled in by the participants; Data file FINAL.xlsx Instruction focus group interview; 2x audio files of focus groups (total 6 files); transcripts of both focus groups; In case of quantitative data: meaning and ranges or codings of all columns: meaning and ranges or codings of all columns 84 Original questionnaires (pdf’s) Data file FINAL (in Excel) + quantitative analysis study 3 (in Word) In case of qualitative data: description of the structure of the data files. Opzet focus groep gesprek (in pdf); 3x FG1 voice recorder (A B C) (MP3), 3x FG2 voice recorder (A B C) (in MP3); 2 transcripts of focus groups (in Word);

OBJECTIVE:

To evaluate safety and efficacy of image guided-hypofractionated radiation therapy (IG-HRT) in patients with thoracic nodes oligometastases.

METHODS:

The present study is a multicenter analysis. Oligometastatic patients, affected by a maximum of five active lesions in three or less different organs, treated with IG-HRT to thoracic nodes metastases between 2012 and 2017 were included in the analysis. Primary end point was local control (LC), secondary end points were overall survival (OS), progression-free survival, acute and late toxicity. Univariate and multivariate analysis were performed to identify possible prognostic factors for the survival end points.

RESULTS:

76 patients were included in the analysis. Different RT dose and fractionation schedules were prescribed according to site, number, size of the lymph node(s) and to respect dose constraints for relevant organs at risk. Median biologically effective dose delivered was 75 Gy (interquartile range: 59-86 Gy). Treatment was optimal; one G1 acute toxicity and seven G1 late toxicities of any grade were recorded. Median follow-up time was 23.16 months. 16 patients (21.05%) had a local progression, while 52 patients progressed in distant sites (68.42 %).Median local relapse free survival was not reached, LC at 6, 12 and 24 months was 96.05% [confidence interval (CI) 88.26-98.71%], 86.68% (CI 75.86-92.87) and 68.21% (CI 51.89-80.00%), respectively. Median OS was 28.3 months (interquartile range 16.1-47.2). Median progression-freesurvival was 9.2 months (interquartile range 4.1-17.93).At multivariate analysis, RT dose, colorectal histology, systemic therapies were correlated with LC. Performance status and the presence of metastatic sites other than the thoracic nodes were correlated with OS. Local response was a predictor of OS.

CONCLUSION:

IG-HRT for thoracic nodes was safe and feasible. Higher RT doses were correlated to better LC and should be taken in consideration at least in patients with isolated nodal metastases and colorectal histology.

ADVANCES IN KNOWLEDGE:

Radiotherapy is safe and effective treatment for thoracic nodes metastases, higher radiotherapy doses are correlated to better LC. Oligometastatic patients can receive IG-HRT also for thoracic nodes metastases.

A Mann-Whitney U test, with post hoc Bonferroni correction were used for statistical analysis. Adjusted significance value p<0.016 (*).MoM: multiple of the median; IQR: interquartile range; PAPP-A: Pregnancy-Associated Plasma Protein-A; fβ–hCG: free β–human Chorionic Gonadotropin; ADAM12: A Disintegrin And Metalloprotease 12; PlGF: Placental Growth Factor; MAP: Mean Arterial Pressure; EO-PE: early-onset preeclampsia; LO-PE: late-onset preeclampsia.

NA = data not available; IQR = interquartile range;*mean(95% confidence interval);†Clavien-Dino classification grade >3.

This dataset provides complementary material to the previously published dataset named “A dataset of EEG recordings from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects” with doi:10.18112/openneuro.ds004504.v1.0.8. It is consisted of eyes-open EEG recordings in multiple photic stimulation settings, according to the clinical protocol of the 2nd department of Neurology, AHEPA University of Thessaloniki, Greece. The participant numbers match the respective participant numbers of the aforementioned dataset. In the clinical protocol, the 1st datasets recordings came first, followed by the recordings of this dataset. The dataset is designed to complement a previously published dataset in which the same cohort underwent EEG recordings with their eyes closed. During the recordings, participants were seated with their eyes open while being exposed to photic stimulation. The stimulation was administered at incremental frequencies, beginning at 5 Hz, progressing to 10 Hz, 15 Hz, and in some cases, extending up to 30 Hz, with increments of 5 Hz at each level. This study compared cognitive function in 36 individuals with Alzheimer's disease (AD), 23 with Frontotemporal Dementia (FTD), and 29 healthy controls (CN). Cognitive function was measured using the Mini-Mental State Examination (MMSE), where lower scores indicate greater cognitive impairment. The AD group had an average MMSE score of 17.75 (standard deviation of 4.5), the FTD group averaged 22.17 (standard deviation of 8.22), and the CN group scored 30. The average age was 66.4 (standard deviation of 7.9) for the AD group, 63.6 (standard deviation of 8.2) for the FTD group, and 67.9 (standard deviation of 5.4) for the CN group. The median disease duration was 25 months, with an interquartile range of 24 to 28.5 months. Notably, the AD group had no reported dementia-related comorbidities. Recordings: Recordings were aquired from the 2nd Department of Neurology of AHEPA General Hospital of Thessaloniki by an experienced team of neurologists. For recording, a Nihon Kohden EEG 2100 clinical device was used, with 19 scalp electrodes (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2) according to the 10-20 international system and 2 additional ectrodes (A1 and A2) placed on the mastoids for impendance check, according to the manual of the device. Each recording was performed according to the clinical protocol with participants being in a sitting position having their eyes closed. Before the initialization of each recording, the skin impedance value was ensured to be below 5k?. The sampling rate was 500 Hz with 10uV/mm resolution. The recording montages were anterior-posterior bipolar and referential montage using Cz as the common reference. The referential montage was included in this dataset. The recordings were received under the range of the following parameters of the amplifier: Sensitivity: 10uV/mm, time constant: 0.3s, and high frequency filter at 70 Hz. Each recording lasted approximately 4.86 minutes for AD group (min=1.30 minutes , max= 8.77 minutes), 4.42 minutes for FTD group (min=1.25 minutes, max=10.05 minutes) and 6.43 minutes for CN group (min=3.17 minutes, max= 9.17 minutes). In total, 174.94 minutes of AD, 101.56 minutes of FTD and 186.50 minutes of CN recordings were collected and are included in the dataset. Preprocessing: The EEG recordings were exported in .eeg format and are transformed to BIDS accepted .set format for the inclusion in the dataset. Automatic annotations of the Nihon Kohden EEG device marking artifacts (muscle activity, blinking, swallowing) have not been included for language compatibility purposes (If this is an issue, please use the preprocessed dataset in Folder: derivatives). The unprocessed EEG recordings are included in folders named: sub-0XX. Folders named sub-0XX in the subfolder derivatives contain the preprocessed and denoised EEG recordings. The preprocessing pipeline of the EEG signals is as follows. First, a Butterworth band-pass filter 0.5-45 Hz was applied and the signals were re-referenced to A1-A2. Then, the Artifact Subspace Reconstruction routine (ASR) which is an EEG artifact correction method included in the EEGLab Matlab software was applied to the signals, removing bad data periods which exceeded the max acceptable 0.5 second window standard deviation of 15, which is considered a conservative window. Next, the Independent Component Analysis (ICA) method (RunICA algorithm) was performed, transforming the 19 EEG signals to 19 ICA components. ICA components that were classified as “eye artifacts” or “jaw artifacts” by the automatic classification routine “ICLabel” in the EEGLAB platform were automatically rejected. It should be noted that, even though the recording was performed in a resting state, eyes-closed condition, eye artifacts of eye movement were still found at some EEG recordings.

A Pearson’s chi square test and Mann-Whitney U test, both with post hoc Bonferroni correction were used for statistical analysis. Adjusted significance value p<0.016 (*). EO-PE: early-onset preeclampsia; LO-PE: late-onset preeclampsia; IQR: interquartile range; BMI: body mass index.

This dataset consists of near-global, analysis-ready, multi-resolution gridded vegetation structure metrics derived from NASA Global Ecosystem Dynamics Investigation (GEDI) Level 2 and 4A products associated with 25-m diameter lidar footprints. This dataset provides a comprehensive representation of near-global vegetation structure that is inclusive of the entire vertical profile, based solely on GEDI lidar, and validated with independent data. The GEDI sensor, mounted on the International Space Station (ISS), uses eight laser beams spaced by 60 m along-track and 600 m across-track on the Earth surface to measure ground elevation and vegetation structure between approximately 52 degrees North and South latitude. Between April 17th 2019 and March 16th 2023, GEDI acquired 11 and 7.7 billion quality waveforms suitable for measuring ground elevation and vegetation structure, respectively. This dataset provides GEDI shot metrics aggregated into raster grids at three spatial resolutions: 1 km, 6 km, and 12 km. In addition to many of the standard L2 and L4A shot metrics, several additional metrics have been derived which may be particularly useful for applications in carbon and water cycling processes in earth system models, as well as forest management, biodiversity modeling, and habitat assessment. Variables include canopy height, canopy cover, plant area index, foliage height diversity, and plant area volume density at 5 m strata. Eight statistics are included for each GEDI shot metric: mean, bootstrapped standard error of the mean, median, standard deviation, interquartile range, 95th percentile, Shannon's diversity index, and shot count. Quality shot filtering methodology that aligns with the GEDI L4B Gridded Aboveground Biomass Density, Version 2.1 was used. In comparison to the current GEDI L3 dataset, this dataset provides additional gridded metrics at multiple spatial resolutions and over several temporal periods (annual and the full mission duration). Files are provided in cloud optimized GeoTIFF format.

*The Wilcoxon rank sum test was used for statistical analysis.†The Chi-squared test was used for statistical analysis.‡The t-test was used for statistical analysis.§Fisher's exact test was used for statistical analysis. IQR: interquartile range.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple datasets. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Hydrological Response Variables (HRVs) are the hydrological characteristics of the system that potentially change due to coal resource development. These data refer to the HRVs related to the AWRA-R model for the Namoi subregion for the 54 simulation nodes. The nine hydrological response variables (AF, P99, FD, IQR, ZFD, P01, LFD, LFS, LLFS) were computed under CRDP and Baseline conditions, respectively and the ACRD is the difference between the Baseline and CRDP.

Abbreviation meaning

AF - the annual streamflow volume (GL/year)

P01 - the daily streamflow rate at the first percentile (ML/day)

P01 - the daily streamflow rate at the first percentile (ML/day)

IQR - the inter-quartile range in daily streamflow (ML/day). That is, the difference between the daily streamflow rate at the 75th percentile and at the 25th percentile.

LFD - the number of low streamflow days per year. The threshold for low streamflow days is the 10th percentile from the simulated 90-year period (2013 to 2102)

LFS - the number of low streamflow spells per year (perennial streams only). A spell is defined as a period of contiguous days of streamflow below the 10th percentile threshold

LLFS - the length (days) of the longest low streamflow spell each year

P99 - the daily streamflow rate at the 99th percentile (ML/day)

FD - flood days, the number of days with streamflow greater than the 90th percentile from the simulated 90-year period (2013 to 2102)

ZFD - Zero flow days

Purpose

This is the dataset used for the Namoi 2.6.1 product to evaluate additional coal mine and coal resource development impacts on hydrological response variables at 54 simulation nodes.

Dataset History

The Namoi AWRA-R model outputs were used to determine the impacts on the HRVs to produce these data. Readme files within the folders in the dataset provide an explanation on how the resource was created. The nine HRVs (AF, P99, FD, IQR, ZFD, P01, LFD, LFS, LLFS) were computed under CRDP and Baseline conditions, respectively. The difference between CRDP and Baseline is used for predicting ACRD impacts on hydrological response variables at 54 simulation nodes.

Dataset Citation

Bioregional Assessment Programme (2017) Namoi standard Hydrological Response Variables (HRVs). Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/189f4c7a-29e1-41f9-868d-b7f5184d829f.

Dataset Ancestors

• Derived From Historical Mining Footprints DTIRIS NAM 20150914

• Derived From Namoi AWRA-R (restricted input data implementation)

• Derived From River Styles Spatial Layer for New South Wales

• Derived From Namoi Surface Water Mine Footprints - digitised

• Derived From Namoi AWRA-R model implementation (post groundwater input)

• Derived From National Surface Water sites Hydstra

• Derived From Namoi AWRA-L model

• Derived From Namoi Hydstra surface water time series v1 extracted 140814

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From Namoi Environmental Impact Statements - Mine footprints

• Derived From Namoi Existing Mine Development Surface Water Footprints