Rest1 and Rest3 are resting state Rest2 is music, three subjects had technical problems with the music and should not be used (S03, S12, S15) ratings: subjects rated the questions with a VAS after each scan. The 11D-ASC was rated at the end of the scanning day.

LSD, BOLD Pre-processing
Four different but complementary imaging software packages were used to analyse the fMRI data. Specifically, FMRIB Software Library (FSL), AFNI , Freesurfer and Advanced Normalization Tools (ANTS) were used. One subject did not complete the BOLD scans due to anxiety and an expressed desire to exit the scanner and four others were discarded from the group analyses due to excessive head movement. Principally, motion was measured using frame-wise displacement (FD) (Power et al., 2014). The criterion for exclusion was subjects with >15% scrubbed volumes when the scrubbing threshold is FD = 0.5. After discarding these subjects we reduced the threshold to FD = 0.4. The between-condition difference in mean FD for the 4 subjects that were discarded was 0.323±0.254 and for the 15 subjects that were used in the analysis the difference in mean FD was 0.046 ±0.032. The following pre-processing stages were performed: 1) removal of the first three volumes; 2) de-spiking (3dDespike, AFNI); 3) slice time correction (3dTshift, AFNI); 4) motion correction (3dvolreg, AFNI) by registering each volume to the volume most similar, in the least squares sense, to all others (in-house code); 5) brain extraction (BET, FSL); 6) rigid body registration to anatomical scans (twelve subjects with FSL’s BBR, one subject with Freesurfer’s bbregister and two subjects manually); 7) non-linear registration to 2mm MNI brain (Symmetric Normalization (SyN), ANTS); 8) scrubbing (Power et al., 2012) - using an FD threshold of 0.4 (the mean percentage of volumes scrubbed for placebo and LSD was 0.4 ±0.8% and 1.7 ±2.3%, respectively). The maximum number of scrubbed volumes per scan was 7.1%) and scrubbed volumes were replaced with the mean of the surrounding volumes. Additional pre-processing steps included: 9) spatial smoothing (FWHM) of 6mm (3dBlurInMask, AFNI); 10) band-pass filtering between 0.01 to 0.08 Hz (3dFourier, AFNI); 11) linear and quadratic de-trending (3dDetrend, AFNI); 12) regressing out 9 nuisance regressors (all nuisance regressors were bandpassed filtered with the same filter as in step 10): out of these, 6 were motion-related (3 translations, 3 rotations) and 3 were anatomically-related (not smoothed). Specifically, the anatomical nuisance regressors were: 1) ventricles (Freesurfer, eroded in 2mm space), 2) draining veins (DV) (FSL’s CSF minus Freesurfer’s Ventricles, eroded in 1mm space) and 3) local white matter (WM) (FSL’s WM minus Freesurfer’s subcortical grey matter (GM) structures, eroded in 2mm space). Regarding WM regression, AFNI’s 3dLocalstat was used to calculate the mean local WM time-series for each voxel, using a 25mm radius sphere centred on each voxel (Jo et al., 2010).

fMRI motion correction After discarding four subjects due to head motion, fifteen were left for the BOLD analysis. There was still a significant between-condition difference in motion for these subjects however (mean FD of placebo = 0.074 ±0.032, mean FD of LSD = 0.12 ±0.05, p = 0.0002). RSFC analysis is extremely sensitive to head motion (Power et al., 2012) and therefore special consideration was given to the pre-processing pipeline to account for motion. This section goes into more detail about the pre-processing steps that were performed to reduce artefacts associated with motion as well as other non-neural sources of noise. De-spiking has been shown to improve motion-correction and create more accurate FD values (Jo et al., 2013) and low-pass filtering at 0.08 Hz has been shown to perform well in removing high frequency motion (Satterthwaite et al., 2013). Six motion regressors were used as covariates in linear regression. It was decided that using more than six (e.g., “Friston 24-parameter motion regression” (Friston et al., 1996)) would be redundant and may impinge on neural signal (Bright and Murphy, 2015) (especially when other rigorous processes such as scrubbing (Power et al., 2012) and local WM were applied (Jo et al., 2010)) . Using anatomical regressors is also a common step to clean noise and ventricles, DV and local WM were used in the pipeline employed in the present analyses. local WM regression has been suggested to perform better than global WM regression (Jo et al., 2013). It has previously been shown that head motion biases functional connectivity results in a distance-dependant manner (Power et al., 2014). Therefore, as a quality control step, at the end of the pre-processing procedure, cloud plots were constructed to test for relationships between inter-node Euclidian distance and correlations between FD and RSFC across subjects. In cases in which motion is affecting the results, proximal nodes will have high FD-RSFC correlations and distal nodes will have low FD-RSFC correlations. This would result in a negative correlation between distance and FD-RSFC correlation. In the present dataset, the distance to FD-RSFC correlation was very close to zero for both the placebo and LSD conditions (Fig. S7), suggesting that the extensive pre-processing measures had successfully controlled for distance-related motion artefacts. The final quality control step was to correlate the results with mean FD across subjects (Table S6). Reassuringly, very few results correlated with mean motion (FD) and these were: vmPFC-PCC (r = -0.48, p = 0.035), V1-bilateral angular gyrus (r = 0.56, p=0.015). The significant correlation between changes in vmPFC-PCC RSFC and FD is also mentioned in (Power et al., 2012) and (Van Dijk et al., 2012); therefore, we decided not to elaborate on this result in the manuscript as it may have been an artifact of motion.

Fig. S7. Correlation between inter-node Euclidian distance (mm) and FD-RSFC correlation (r) for both LSD (a) and placebo (b). Nodes were defined using the Craddock atlas with 240 parcellations, excluding supplementary motor and motor areas. For each pair of nodes, RSFC was calculated with pearson’s r and transformed into z using fisher transformation. For each pair of nodes, a correlation across subjects was calculated between mean FD and RSFC (r) and transformed into z using fisher’s transformation. This correlation is plotted against the distance between nodes (mm). The correlations for LSD and placebo were r = -0.0009 (p = 0.089) and r = -0.025 (p < 0.001), respectively, suggesting that motion did not affect RSFC in a distant dependant manner after pre-processing.

REFERENCES Bright MG, Murphy K (2015) Is fMRI “noise” really noise? Resting state nuisance regressors remove variance with network structure. NeuroImage 114:158-169. Friston KJ, Williams S, Howard R, Frackowiak RS, Turner R (1996) Movement‐related effects in fMRI time‐series. Magnetic resonance in medicine 35:346-355. Jo HJ, Saad ZS, Simmons WK, Milbury LA, Cox RW (2010) Mapping sources of correlation in resting state FMRI, with artifact detection and removal. Neuroimage 52:571-582. Jo HJ, Gotts SJ, Reynolds RC, Bandettini PA, Martin A, Cox RW, Saad ZS (2013) Effective preprocessing procedures virtually eliminate distance-dependent motion artifacts in resting state FMRI. Journal of applied mathematics 2013. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59:2142-2154. Power JD, Mitra A, Laumann TO, Snyder AZ, Schlaggar BL, Petersen SE (2014) Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84:320-341. Satterthwaite TD, Elliott MA, Gerraty RT, Ruparel K, Loughead J, Calkins ME, Eickhoff SB, Hakonarson H, Gur RC, Gur RE (2013) An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage 64:240-256. Van Dijk KR, Sabuncu MR, Buckner RL (2012) The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 59:431-438.

Email leor.roseman13@imperial.ac.uk for any questions

Fragility Multi-Center Retrospective Study

iEEG and EEG data from 5 centers is organized in our study with a total of 100 subjects. We publish 4 centers' dataset here due to data sharing issues.

Acquisitions include ECoG and SEEG. Each run specifies a different snapshot of EEG data from that specific subject's session. For seizure sessions, this means that each run is a EEG snapshot around a different seizure event.

For additional clinical metadata about each subject, refer to the clinical Excel table in the publication.

Data Availability

NIH, JHH, UMMC, and UMF agreed to share. Cleveland Clinic did not, so requires an additional DUA.

All data, except for Cleveland Clinic was approved by their centers to be de-identified and shared. All data in this dataset have no PHI, or other identifiers associated with patient. In order to access Cleveland Clinic data, please forward all requests to Amber Sours, SOURSA@ccf.org:

Amber Sours, MPH Research Supervisor | Epilepsy Center Cleveland Clinic | 9500 Euclid Ave. S3-399 | Cleveland, OH 44195 (216) 444-8638

You will need to sign a data use agreement (DUA).

Sourcedata

For each subject, there was a raw EDF file, which was converted into the BrainVision format with mne_bids. Each subject with SEEG implantation, also has an Excel table, called electrode_layout.xlsx, which outlines where the clinicians marked each electrode anatomically. Note that there is no rigorous atlas applied, so the main points of interest are: WM, GM, VENTRICLE, CSF, and OUT, which represent white-matter, gray-matter, ventricle, cerebrospinal fluid and outside the brain. WM, Ventricle, CSF and OUT were removed channels from further analysis. These were labeled in the corresponding BIDS channels.tsv sidecar file as status=bad. The dataset uploaded to openneuro.org does not contain the sourcedata since there was an extra anonymization step that occurred when fully converting to BIDS.

Derivatives

Derivatives include: * fragility analysis * frequency analysis * graph metrics analysis * figures

These can be computed by following the following paper: Neural Fragility as an EEG Marker for the Seizure Onset Zone

Events and Descriptions

Within each EDF file, there contain event markers that are annotated by clinicians, which may inform you of specific clinical events that are occuring in time, or of when they saw seizures onset and offset (clinical and electrographic).

During a seizure event, specifically event markers may follow this time course:

* eeg onset, or clinical onset - the onset of a seizure that is either marked electrographically, or by clinical behavior. Note that the clinical onset may not always be present, since some seizures manifest without clinical behavioral changes.
* Marker/Mark On - these are usually annotations within some cases, where a health practitioner injects a chemical marker for use in ICTAL SPECT imaging after a seizure occurs. This is commonly done to see which portions of the brain are active metabolically.
* Marker/Mark Off - This is when the ICTAL SPECT stops imaging.
* eeg offset, or clinical offset - this is the offset of the seizure, as determined either electrographically, or by clinical symptoms.

Other events included may be beneficial for you to understand the time-course of each seizure. Note that ICTAL SPECT occurs in all Cleveland Clinic data. Note that seizure markers are not consistent in their description naming, so one might encode some specific regular-expression rules to consistently capture seizure onset/offset markers across all dataset. In the case of UMMC data, all onset and offset markers were provided by the clinicians on an Excel sheet instead of via the EDF file. So we went in and added the annotations manually to each EDF file.

Seizure Electrographic and Clinical Onset Annotations

For various datasets, there are seizures present within the dataset. Generally there is only one seizure per EDF file. When seizures are present, they are marked electrographically (and clinically if present) via standard approaches in the epilepsy clinical workflow.

Clinical onset are just manifestation of the seizures with clinical syndromes. Sometimes the maker may not be present.

Seizure Onset Zone Annotations

What is actually important in the evaluation of datasets is the clinical annotations of their localization hypotheses of the seizure onset zone.

These generally include:

* early onset: the earliest onset electrodes participating in the seizure that clinicians saw
* early/late spread (optional): the electrodes that showed epileptic spread activity after seizure onset. Not all seizures has spread contacts annotated.

Surgical Zone (Resection or Ablation) Annotations

For patients with the post-surgical MRI available, then the segmentation process outlined above tells us which electrodes were within the surgical removed brain region.

Otherwise, clinicians give us their best estimate, of which electrodes were resected/ablated based on their surgical notes.

For surgical patients whose postoperative medical records did not explicitly indicate specific resected or ablated contacts, manual visual inspection was performed to determine the approximate contacts that were located in later resected/ablated tissue. Postoperative T1 MRI scans were compared against post-SEEG implantation CT scans or CURRY coregistrations of preoperative MRI/post SEEG CT scans. Contacts of interest in and around the area of the reported resection were selected individually and the corresponding slice was navigated to on the CT scan or CURRY coregistration. After identifying landmarks of that slice (e.g. skull shape, skull features, shape of prominent brain structures like the ventricles, central sulcus, superior temporal gyrus, etc.), the location of a given contact in relation to these landmarks, and the location of the slice along the axial plane, the corresponding slice in the postoperative MRI scan was navigated to. The resected tissue within the slice was then visually inspected and compared against the distinct landmarks identified in the CT scans, if brain tissue was not present in the corresponding location of the contact, then the contact was marked as resected/ablated. This process was repeated for each contact of interest.

References

Adam Li, Chester Huynh, Zachary Fitzgerald, Iahn Cajigas, Damian Brusko, Jonathan Jagid, Angel Claudio, Andres Kanner, Jennifer Hopp, Stephanie Chen, Jennifer Haagensen, Emily Johnson, William Anderson, Nathan Crone, Sara Inati, Kareem Zaghloul, Juan Bulacio, Jorge Gonzalez-Martinez, Sridevi V. Sarma. Neural Fragility as an EEG Marker of the Seizure Onset Zone. bioRxiv 862797; doi: https://doi.org/10.1101/862797

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

Holdgraf, C., Appelhoff, S., Bickel, S., Bouchard, K., D'Ambrosio, S., David, O., … Hermes, D. (2019). iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Scientific Data, 6, 102. https://doi.org/10.1038/s41597-019-0105-7

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8

About the Dataset

Title: 9,565 Top-Rated Movies Dataset

Description:
This dataset offers a comprehensive collection of 9,565 of the highest-rated movies according to audience ratings on the Movie Database (TMDb). The dataset includes detailed information about each movie, such as its title, overview, release date, popularity score, average vote, and vote count. It is designed to be a valuable resource for anyone interested in exploring trends in popular cinema, analyzing factors that contribute to a movie’s success, or building recommendation engines.

Key Features: - Title: The official title of each movie. - Overview: A brief synopsis or description of the movie's plot. - Release Date: The release date of the movie, formatted as YYYY-MM-DD. - Popularity: A score indicating the current popularity of the movie on TMDb, which can be used to gauge current interest. - Vote Average: The average rating of the movie, based on user votes. - Vote Count: The total number of votes the movie has received.

Data Source: The data was sourced from the TMDb API, a well-regarded platform for movie information, using the /movie/top_rated endpoint. The dataset represents a snapshot of the highest-rated movies as of the time of data collection.

Data Collection Process: - API Access: Data was retrieved programmatically using TMDb’s API. - Pagination Handling: Multiple API requests were made to cover all pages of top-rated movies, ensuring the dataset’s comprehensiveness. - Data Aggregation: Collected data was aggregated into a single, unified dataset using the pandas library. - Cleaning: Basic data cleaning was performed to remove duplicates and handle missing or malformed data entries.

Potential Uses: - Trend Analysis: Analyze trends in movie ratings over time or compare ratings across different genres. - Recommendation Systems: Build and train models to recommend movies based on user preferences. - Sentiment Analysis: Perform text analysis on movie overviews to understand common themes and sentiments. - Statistical Analysis: Explore the relationship between popularity, vote count, and average ratings.

Data Format: The dataset is provided in a structured tabular format (e.g., CSV), making it easy to load into data analysis tools like Python, R, or Excel.

Usage License: The dataset is shared under [appropriate license], ensuring that it can be used for educational, research, or commercial purposes, with proper attribution to the data source (TMDb).

This description provides a clear and detailed overview, helping potential users understand the dataset's content, origin, and potential applications.

Learned bird songs often have a hierarchical organization. In the case of zebra finches, each bird’s song is made up of a string of notes delivered in a stereotyped sequence to form a “motif†, and motifs are repeated to form a song bout. During song learning, young males copy “chunks†of two or more consecutive notes from their tutors’ songs. These chunks are represented as distinct units within memory (during learning) and within motor systems (during song production). During song performance, motifs may deviate from the learned sequence by stopping short, starting late, or by skipping, inserting or repeating notes. We measured acoustic and temporal variables related to the respiratory and vocal physiology of song production and asked how they related to deviations from each bird’s “canonical†sequence. The best predictor of deviations from that sequence was the duration of the silent interval between notes, when inspiration normally occurs. Deviations from the canonical motif occurred..., Subjects: The subjects were 50 adult male zebra finches (Taeniopygia guttata) bred and raised in the Williams College colony. Birds were hatched in aviaries that housed at least three breeding pairs, and so could copy songs from their fathers or from other adult males. Young males in their own and other broods could also influence song development (Tchernichovski & Nottebohm, 1998) or be the source of song material, and both young and older females were present and could influence song development (Carouso-Peck & Goldstein, 2019). When birds reached maturity (approximately 90 days) they were removed from their natal aviaries and housed in single-sex cages, grouped by age cohort, in a colony room that also housed females and older males. All procedures were approved by the Williams College Institutional Animal Care and Use Committee (Williams College IACUC protocol WH-A). Song Recording:  Recordings of female-directed song were obtained by placing a caged adult male (> 120 d..., R, with the following packages: ggplot2, Rcpp, Matrix, lme4, reshape2, sciplot, lattice, plyr, magrittr, MuMIn, Amelia, pscl, readr, performance, dplyr, tidyverse, ggeasy, car, glmmTMB, bbmle, GLMMadaptive, sandwich, lmtest, MASS, DescTools (all are open-source), # Physiological Constraints and Cognitive Chunking in Zebra Finch Songs: Data and R script

The data set for the associated paper was obtained by analyzing an average of more than 100 songs from each of 50 zebra finches. Zebra finches have a stereotyped song consisting of approximately ten notes, which are delivered in a stereotyped sequence called a motif. The full sequence is the canonical motif. To address the question of what physiological and cognitive factors are associated with deviations of the canonical motif sequence, we scored deviations as start, stop, and other (repeats, skips, insertions) and counted their occurrence at each transition between notes in each birds canonical motif. We then characterized the acoustic and temporal properties of each transition between notes. Finally, we characterized the cognitive chunks within the song by looking at 21 birds songs that were copies of the same motif, and noting where the copied songs did not match; the transitions where t...

There are two Excel .csv files containing questions on type 2 diabetes mellitus tagged by the specific topics each question covers. clinicquestions.csv has questions collected from 100 patients. crowdsourcedquestions.csv has questions collected from 300 members of the public. Questions on type 1 diabetes were removed, as were questions on clinic operations. Details on the coding can be found in the citation (below).clinicquestions.csvNumber of instances: 152Number of attributes: 25Attribute characteristics: text; integer IDs, one-hot encodingsMissing data: nonecrowdsourcedquestions.csv Number of instances: 284Number of attributes: 26Attribute characteristics: text; categorical (Female/Male); integer IDs, one-hot encodingsMissing data: noneATTRIBUTES:gender – categorical (Male/Female)person_ID – Integer identifier for the person asking the question qx_ID – Integer identifier for the questionQx – The text of the question itself, as asked, without corrections or edits22 topic categories one-hot encoded:CAUSE ; RISK; PREVENTION; DIAGNOSIS; MANIFESTATIONS; TREATMENT; ANATOMY; CURE; DIET; EXERCISE; WEIGHT; SELF-MANAGEMENT; DISEASE COMPLICATIONS; TREATMENT COMPLICATIONS; PERSON or ORGANIZATION; PROGNOSIS; DISTRIBUTION of a DISEASE in a POPULATION; INHERITANCE PATTERNS; RESEARCH; PSYCHOSOCIAL; Own HEALTH RECORD RELATED; OTHER CITATION:

Crangle CE, Bradley C, Carlin PF, Esterhay RJ, Harper R, Kearney PM, Lorig K, McCarthy VJC, McTear M, Savage E, Tuttle MS, Wallace JG. (2018, to appear) Exploring Patient Information Needs: A Cross Sectional Study of Questions on Type 2 Diabetes. PLOS ONE

From 20 October 2023, COVID-19 datasets will no longer be updated.\r Detailed information is available in the fortnightly NSW Respiratory Surveillance Report: https://www.health.nsw.gov.au/Infectious/covid-19/Pages/reports.aspx.\r Latest national COVID-19 spread, vaccination and treatment metrics are available on the Australian Government Health website: https://www.health.gov.au/topics/covid-19/reporting?language=und\r \r The data is for locations associated with confirmed COVID-19 cases that have been classified by NSW Health for action. Refer to the latest COVID-19 news and updates for information on action advice provided by NSW Health.\r \r From Monday 15 November 2021, NSW Health will no longer list case locations that a COVID-19 positive person has attended. This is due to a number of reasons, including high vaccination rates in the community. If you are told to self-isolate by NSW Health or get tested for COVID-19 at any time you must follow this advice. \r \r This dataset provides COVID-19 case locations by date of known outbreak, location, address and action. \r This data is subject to change as further locations are identified. Locations are removed when 14 days have passed since the last known date that a confirmed case was associated with the location. \r \r The Government has obligations under the Privacy and Personal Information Protection Act 1998 and the Health Records and Information Privacy Act 2002 in relation to the collection, use and disclosure of the personal, including the health information, of individuals. Information about NSW Privacy laws is available here: https://data.nsw.gov.au/understand-key-data-legislation. \r \r The information collected about confirmed case locations does not include any information to directly identify individuals, such as their name, date of birth or address.\r \r Other governments and private sector bodies also have legal obligations in relation to the protection of personal, including health, information. The Government does not authorise any reproduction or visualisation of the data on this website which includes any representation or suggestion in relation to the personal or health information of any individual. The Government does not endorse or control any third party websites including products and services offered by, from or through those websites or their content.\r \r For any further enquiries, please contact us on datansw@customerservice.nsw.gov.au\r

Summary\r

The 2024 APS Employee Census was administered to eligible Australian Public Service (APS) employees between 6 May and 7 June 2024. Overall, 140,396 APS employees responded to the APS Employee Census in 2024, a response rate of 81%.\r \r The APS Employee Census is an annual employee perception survey of the Australian Public Service workforce. The APS Employee Census has been conducted since 2012 and collects APS employee opinions and perspectives on a range of topics, including employee engagement, wellbeing, and leadership.\r \r The APS Employee Census provides a comprehensive view of the APS and ensures no eligible respondents are omitted from the survey sample, removing sampling bias and reducing sample error. \r \r Please be aware that the very large number of respondents to the APS Employee Census means these files are over 200MB in size. \r \r Downloading and opening these files may take some time.\r \r

Technical notes\r

Three files are available for download.\r \r • 2024 APS Employee Census - Questionnaire: This contains the 2024 APS Employee Census questionnaire.\r \r • 2024 APS Employee Census - 5 point dataset with data values: This CSV file contains individual responses to the 2024 APS Employee Census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document. Data in this file are presented as data values.\r \r • 2024 APS Employee Census - 5 point dataset with data labels: This CSV file contains individual responses to the 2024 APS Employee Census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document. Data in this file are presented as data labels.\r \r • 2024 APS Employee Census - 5 point dataset.sav: This file contains individual responses to the 2024 APS Employee Census for use with the SPSS software package. \r \r • 2024 APS Employee Census - data dictionary: This file contains a list of variables and labels within the APS Employee Census.\r \r To protect the privacy and confidentiality of respondents to the 2024 APS Employee Census, the datasets provided on data.gov.au include responses to a limited number of demographic or other attribute questions.\r \r \r Full citation of this dataset should list the Australian Public Service Commission (APSC) as the author. \r \r A recommended short citation is: 2024 APS Employee Census, Australian Public Service Commission. \r \r Any queries can be directed to research@apsc.gov.au.\r

Linked to the journal article published in iScience (https://doi.org/10.1016/j.isci.2024.109935). Abstract Argentine ants, Linepithema humile, are a particularly concerning invasive species. Control efforts often fall short likely due to a lack of sustained bait consumption. Using neuroactives, such as caffeine, to improve ant learning and navigation could increase recruitment and consumption of toxic baits. Here, we exposed L. humile to a range of caffeine concentrations and a complex ecologically relevant task: an open landscape foraging experiment. Without caffeine, we found no effect of consecutive foraging visits on the time the ants take to reach a reward, suggesting a failure to learn the reward’s location. However, under low to intermediate caffeine concentrations ants were 38% faster with each consecutive visit, implying that caffeine boosts learning. Interestingly, such improvements were lost at high doses. In contrast, caffeine had no impact on the ants’ homing behavior. Adding moderate levels of caffeine to baits could improve ant’s ability to learn its location, improving bait efficacy. sample_videos.zip: A subset of the videos used for data extraction. The complete collection of videos is not publicly accessible primarily due to their considerable size (105.35GB). Requests for access to the entire video set are encouraged. Preregistration.pdf: The preregistration created for data collection and analysis with justifications for deviations from it. OpLan_D1_metadata.csv: Manually collected metadata pertaining to experimental conditions, subjects, and treatments. OpLan_D2_DLC_coordinates.zip: Cartesian coordinates obtained from DeepLabCut for each of the videos analysed. OpLan_C1_reproject_coordinates.py: Python code used to standardise the ants' coordinates by ensuring the same corner of the A4 platform was used as the origin of the cartesian referential of all videos. The known dimensions of the A4 were further used to convert coordinates from pixels to millimetres. OpLan_C2_remove_impossibilities.py: Python code used to account for DeepLabCut tracking errors, with any ant movement exceeding two millimetres per frame being considered implausible and subsequently removed. OpLan_C3_find_changepoints.py: Python code used to automatically derive the times at which an ant reached and left the reward from the tracking data. OpLan_C4_inward_outward_data.py: Python code used to calculate relevant measures for the foodward (inward) and nestward (outward) journey such as journey duration, mean instantaneous speed and path tortuosity. OpLan_C5_Figure_2.R: R code used to produce the raw elements of Figure 2. OpLan_C6_Figure_4.R: R code used to produce the raw elements of Figure 4. OpLan_C7_Statistical_Analysis.html: Complete statistical analysis and code for the manuscript.

Contact Information

If you would like further information about PeakAffectDS, to purchase a commercial license, or if you experience any issues downloading files, please contact us at peakaffectds@gmail.com.

Description

PeakAffectDS contains 663 files (total size: 1.84 GB), consisting of 612 physiology files, and 51 perceptual rating files. The dataset contains 51 untrained research participants (39 female, 12 male), who had their body physiology recorded while watching movie clips validated to induce strong emotional reactions. Emotional conditions included: calm, happy, sad, angry, fearful, and disgust; along with baseline a neutral condition. Four physiology channels were recorded with a Biopac MP36 system: two facial muscles with fEMG (zygomaticus major, corrugator supercilii) using Ag/AgCl electrodes, heart activity with ECG using a 1-Lead, Lead II configuration, and respiration with a wearable strain-gauge belt. While viewing movie clips, participants indicated in real-time when they experienced a "peak" emotional event, including: chills, tears, or the startle reflex. After each clip, participants further rated their felt emotional state using a forced-choice categorical response measure, along with their felt Arousal and Valence. All data are provided in plaintext (.csv) format.

PeakAffectDS was created in the Affective Data Science Lab.

Physiology files

Each participant has 12 .CSV physiology files, consisting of 6 Emotional conditions, and 6 Neutral baseline conditions. All physiology channels were recorded at 2000 Hz. A 50Hz notch filter was then applied to fEMG and ECG channels to remove mains hum. Each .CSV file contains 6 columns, in order from left to right:

1. Sample timestamp (units: seconds)
2. EMG Zygomaticus (units: millivolts)
3. EMG Corrugator (units: millivolts)
4. ECG (units: millivolts)
5. Peak event makers: 0 = no event, 1 = chills, 2 = tears, 3 = startle

Perceptual files

There are 51 perceptual ratings files, one for each participant. Each .CSV file contains 4 columns, in order from left to right:

1. Filename of presented stimulus (see File naming Convention, below)
2. Felt emotional response: 1 = neutral, 2 = calm, 3 = happy, 4 = sad, 5 = angry, 6 = fearful, 7 = disgust
3. Felt Valence, ranging from: 1 = Very negative, to 7 = Very positive
4. Felt Arousal, ranging from: 1 = Very low, to 7 = Very high

File naming convention

Each of the 612 physiology files has a unique filename. The filename consists of a 3-part numerical identifier (e.g., 09-02-03.csv). The first identifier refers to the participant's ID (09), while the remaining two identifiers refer to the stimulus presented for that recording (02-03.mp4); these identifiers define the stimulus characteristics:

• Participant: 01 = participant 1, 02 = participant 2, ..., 51 = participant 51.
• Emotion: 01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust.
• Stimulus set. For Emotional files: 01 = group 1, 02 = group 2, 03 = group 3. For Neutral files: 01 = instance 1, 02 = instance 2, ..., 06 = instance 6.

Filename example: 09-02-03.csv

• Participant 9 (09)
• Calm (02)
• Stimulus Set 3 (03)

Filename example: 09-01-05.csv

• Participant 9 (09)
• Neutral (01)
• Instance 5 (05)

Methods

A 1-way mixed-design was used, with a within-subjects factor Emotion (6 levels: Calm, Happy, Sad, Angry, Fearful, Disgust) and a between-subjects factor Stimulus Set (3 levels). Trials were blocked by Affect Condition (Baseline, Emotional), with each participant presented 6 blocked trials: Baseline (neutral), then Emotional (Calm, ..., Disgust). This design reduced potential contamination from preceeding emotional trials, by ensuring that participant's physiology began close to a resting baseline for emotional conditions.

Emotion was presented in pseudorandom order using a carryover balanced generalised Youden design, generated by the crossdes package in R. Eighteen emotional movie clips were used as stimuli, with three instances for each emotion category (6x3). Clips were then grouped into one of three Stimulus Sets, with participants assigned to a given Set using Block randomisation. For example, participants assigned to Stimulus Set 1 (PID: 1, 4, 7, ...) all saw the same movie clips, but these clips differed to those in Sets 2 and 3. Six Neutral baseline movie clips were used as stimuli, with all participants viewing the same neutral clips, with their order also generated with a Youden design.

Stimulus duration varied, with clips lasting several minutes. Lengthy clips without repetition were used to help ensure that participants became engaged, and experienced genuine, strong emotional responses. Participants were instructed to immediately indicate using the keyboard when experiencing a "peak" emotional event, including: chills, tears, or startle. Participants were permitted to indicate multiple events in a single trial, and identified the type of the evens at the trial feedback stage, along with ratings of emotional category, arousal, and valence. The concept of peak physiological events was explained at the beginning of the experiment, but the three states were not described as being associated with any particular emotion or valence.

License information

PeakAffectDS is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, CC BY-NC-SA 4.0.

Citing PeakAffectDS

Greene, N., Livingstone, S. R., & Szymanski, L. (2022). PeakAffectDB [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6403363

In Western Australia, the Public Sector Commission's purpose is to bring leadership and expertise to the public sector to enhance integrity, effectiveness and efficiency. The Commission’s vision is of a high performing public sector serving the needs of the State now and into the future.\r \r In accordance with section 22D of the Public Sector Management Act 1994, the Public Sector Commissioner reports annually to Parliament on the state of public sector administration and management, and on the extent of compliance with public sector standards and ethical codes. One of the primary information sources used for the State of the sectors reports is the public sector entity survey (PSES).\r \r The 2016 PSES collected information across topics such as the application of Commissioner’s Instruction No. 7 – Code of Ethics, codes of conduct and general principles of human resource management, public interest disclosure, equal employment opportunity strategies and agency management at June 2016 or for the 2015/16 financial year. The survey was sent to chief executive officers and chief employees of public sector entities with over 20 full-time equivalents as at December 2015, including Senior Executive Service (SES) organisations, non-SES organisations and departments of state. In 2016, 78 public sector entities completed the survey.\r \r Three files are available for download on data.gov.au:\r \r 1) Public Sector Commission PSES 2016.pdf – the survey instrument sent to relevant entities to access online.\r \r 2) Public Sector Commission PSES 2016 data.xls – individual agency responses to the PSES with a question key and response key. Please note some variables have been removed to simplify the dataset.\r \r 3) Public Sector Commission PSES 2016 data.sav – individual agency responses to the PSES, in .sav format to be used with SPSS. Please note some variables have been removed to simplify the dataset.\r \r Further information and a summary of PSES data is available in the State of the sectors statistical bulletin 2016.\r

The data collected by Spire from it's 100 satellites launched into Low Earth Orbit (LEO) has a diverse range of applications, from analysis of global trade patterns and commodity flows to aircraft routing to weather forecasting. The data also provides interesting research opportunities on topics as varied as ocean currents and GNSS-based planetary boundary layer height. The following products can be requested:

GNSS Polarimetric Radio Occultation (STRATOS) Novel Polarimetric Radio Occultation (PRO) measurements collected by three Spire satellites are available over 15-May-2023 to 30-November-2023. PRO differ from regular RO (described below) in that the H and V polarizations of the signal are available, as opposed to only Right-Handed Circularly Polarized (RHCP) signals in regular RO. The differential phase shift between H and V correlates with the presence of hydrometeors (ice crystals, rain, snow, etc.). When combined, the H and V information provides the same information on atmospheric thermodynamic properties as RO: temperature, humidity, and pressure, based on the signal’s bending angle. Various levels of the products are provided.

GNSS Reflectometry (STRATOS) GNSS Reflectometry (GNSS-R) is a technique to measure Earth’s surface properties using reflections of GNSS signals in the form of a bistatic radar. Spire collects two types of GNSS-R data: Near-Nadir incidence LHCP reflections collected by the Spire GNSS-R satellites, and Grazing-Angle GNSS-R (i.e., low elevation angle) RHCP reflections collected by the Spire GNSS-RO satellites. The Near-Nadir GNSS-R collects DDM (Delay Doppler Map) reflectivity measurements. These are used to compute ocean wind / wave conditions and soil moisture over land. The Grazing-Angle GNSS-R collects 50 Hz reflectivity and additionally carrier phase observations. These are used for altimetry and characterization of smooth surfaces (such as ice and inland water). Derived Level 1 and Level 2 products are available, as well as some special Level 0 raw intermediate frequency (IF) data. Historical grazing angle GNSS-R data are available from May 2019 to the present, while near-nadir GNSS-R data are available from December 2020 to the present.

Name Temporal coverage Spatial coverage Description Data format and content Application Polarimetric Radio Occultation (PRO) measurements 15-May-2023 to 30-November-2023 Global PRO measurements observe the properties of GNSS signals as they pass through by Earth's atmosphere, similar to regular RO measurements. The polarization state of the signals is recorded separately for H and V polarizations to provide information on the anisotropy of hydrometeors along the propagation path. leoOrb.sp3. This file contains the estimated position, velocity and receiver clock error of a given Spire satellite after processing of the POD observation file PRO measurements add a sensitivity to ice and precipitation content alongside the traditional RO measurements of the atmospheric temperature, pressure, and water vapor. proObs. Level 0 - Raw open loop carrier phase measurements at 50 Hz sampling for both linear polarization components (horizontal and vertical) of the occulted GNSS signal. h(v)(c)atmPhs. Level 1B - Atmospheric excess phase delay computed for each individual linear polarization component (hatmPhs, vatmPhs) and for the combined (“H” + “V”) signal (catmPhs). Also contains values for signal-to-noise ratio, transmitter and receiver positions and open loop model information.
polPhs. Level 1C - Combines the information from the hatmPhs and vatmPhs files while removing phase continuities due to phase wrapping and navigation bit modulation.
patmPrf. Level 2 - Bending angle, dry refractivity, and dry temperature as a function of mean sea level altitude and impact parameter derived from the “combined” excess phase delay (catmPhs)
Near-Nadir GNSS Reflectometry (NN GNSS-R) measurements 25-January-2024 to 24-July-2024 Global Tracks of surface reflections as observed by the near-nadir pointing GNSS-R antennas, based on Delay Doppler Maps (DDMs). gbrRCS.nc. Level 1B - Along-track calibrated bistatic radar cross-sections measured by Spire conventional GNSS-R satellites. NN GNSS-R measurements are used to measure ocean surface winds and characterize land surfaces for applications such as soil moisture, freeze/thaw monitoring, flooding detection, inland water body delineation, sea ice classification, etc. gbrNRCS.nc. Level 1B - Along-track calibrated bistatic and normalized radar cross-sections measured by Spire conventional GNSS-R satellites.
gbrSSM.nc. Level 2 - Along-track SNR, reflectivity, and retrievals of soil moisture (and associated uncertainties) and probability of frozen ground.
gbrOcn.nc. Level 2 - Along-track retrievals of mean square slope (MSS) of the sea surface, wind speed, sigma0, and associated uncertainties.
Grazing angle GNSS Reflectometry (GA GNSS-R) measurements 25-January-2024 to 24-July-2024 Global Tracks of surface reflections as observed by the limb-facing RO antennas, based on open-loop tracking outputs: 50 Hz collections of accumulated I/Q observations. grzRfl.nc. Level 1B - Along-track SNR, reflectivity, phase delay (with respect to an open loop model) and low-level observables and bistatic radar geometries such as receiver, specular reflection, and the transmitter locations. GA GNSS-R measurements are used to 1) characterize land surfaces for applications such as sea ice classification, freeze/thaw monitoring, inland water body detection and delineation, etc., and 2) measure relative altimetry with dm-level precision for inland water bodies, river slopes, sea ice freeboard, etc., but also water vapor characterization from delay based on tropospheric delays. grzIce.nc. Level 2 - Along-track water vs sea ice classification, along with sea ice type classification.
grzAlt.nc. Level 2 - Along-track phase-delay, ionosphere-corrected altimetry, tropospheric delay, and ancillary models (mean sea surface, tides).

Additionally, the following products (better detailed in the ToA) can be requested but the acceptance is not guaranteed and shall be evaluated on a case-by-case basis: Other STRATOS measurements: profiles of the Earth’s atmosphere and ionosphere, from December 2018 ADS-B Data Stream: monthly subscription to global ADS-B satellite data, available from December 2018 AIS messages: AIS messages observed from Spire satellites (S-AIS) and terrestrial from partner sensor stations (T-AIS), monthly subscription available from June 2016

The products are available as part of the Spire provision with worldwide coverage. All details about the data provision, data access conditions and quota assignment procedure are described in the _\(Terms of Applicability\) https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf/0dd8b3e8-05fe-3312-6471-a417c6503639 .