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The synthetic networks in this dataset were generated using the RECCS protocol developed by Anne et al. (2024). Briefly, the RECCS process is as follows. An input network and clustering (by any algorithm) is used to pass input parameters to a stochastic block model (SBM) generator. The output is then modified to improve fit to the input real world clusters after which outlier nodes are added using one of three different options. See Anne et al. (2024): in press Complex Networks and Applications XIII (preprint : arXiv:2408.13647). The networks in this dataset were generated using either version 1 or version 2 of the RECCS protocol followed by outlier strategy S1. The input networks to the process were (i) the Curated Exosome Network (CEN), Wedell et al. (2021), (ii) cit_hepph (https://snap.stanford.edu/), (iii) cit_patents (https://snap.stanford.edu/), and (iv) wiki_topcats (https://snap.stanford.edu/). Input Networks: The CEN can be downloaded from the Illinois Data Bank: https://databank.illinois.edu/datasets/IDB-0908742 -> cen_pipeline.tar.gz -> S1_cen_cleaned.tsv The synthetic file naming system should be interpreted as follows: a_b_c.tsv.gz where a - name of inspirational network, e.g., cit_hepph b - the resolution value used when clustering a with the Leiden algorithm optimizing the Constant Potts Model, e.g., 0.01 c- the RECCS option used to approximate edge count and connectivity in the real world network, e.g., v1 Thus, cit_hepph_0.01_v1.tsv indicates that this network was modeled on the cit_hepph network and RECCSv1 was used to match edge count and connectivity to a Leiden-CPM 0.01 clustering of cit_hepph. For SBM generation, we used the graph_tool software (P. Peixoto, Tiago 2014. The graph-tool python library. figshare. Dataset. https://doi.org/10.6084/m9.figshare.1164194.v14) Additionally, this dataset contains synthetic networks generated for a replication experiment (repl_exp.tar.gz). The experiment aims to evaluate the consistency of RECCS-generated networks by producing multiple replicates under controlled conditions. These networks were generated using different configurations of RECCS, varying across two versions (v1 and v2), and applying the Connectivity Modifier (CM++, Ramavarapu et al. (2024)) pre-processing. Please note that the CM pipeline used for this experiment filters small clusters both before and after the CM treatment. Input Network : CEN Within repl_exp.tar.gz, the synthetic file naming system should be interpreted as follows: cen_<resolution><cm_status><reccs_version>sample<replicate_id>.tsv where: cen – Indicates the network was modeled on the Curated Exosome Network (CEN). resolution – The resolution parameter used in clustering the input network with Leiden-CPM (0.01). cm_status – Either cm (CM-treated input clustering) or no_cm (input clustering without CM treatment). reccs_version – The RECCS version used to generate the synthetic network (v1 or v2). replicate_id – The specific replicate (ranging from 0 to 2 for each configuration). For example: cen_0.01_cm_v1_sample_0.tsv – A synthetic network based on CEN with Leiden-CPM clustering at resolution 0.01, CM-treated input, and generated using RECCSv1 (first replicate). cen_0.01_no_cm_v2_sample_1.tsv – A synthetic network based on CEN with Leiden-CPM clustering at resolution 0.01, without CM treatment, and generated using RECCSv2 (second replicate). The ground truth clustering input to RECCS is contained in repl_exp_groundtruths.tar.gz.

This datasets provide basis of our analysis in the paper - Potential Impacts of Supersonic Aircraft on Stratospheric Ozone and Climate. All datasets here can be categorized into emission data and model output data (WACCM). All the model simulations (background and perturbation) were run to steady-state and only the datasets used in analysis are archived here.

Includes two files (.csv) behind all analyses and results in the paper published with the same title. <b>1) 'sites.species.counts'</b> is the raw 2018-2022 data from Angella Moorehouse (Illinois Nature Preserves Commission) including her 456 identified pollinator species and her raw counts per site (there may be a few errors of identification or naming, and there will always be name changes over time). Headers in columns F through Q correspond to the remnant-site labels in Figure 1 and Table 1 of the paper. Columns R to AB are the “nonremnant” sites, which have not been uniquely labelled since the specific sites aren't referenced anywhere in the manuscript. <b>2) 'C.scores'</b> has the 265 species assigned empirical C values (empirical.C) along with the four sets of expert C values and their confidence ranks (low, medium, high), and the Illinois/Indiana conservation ranks (S-ranks), following the methods described in the paper. Other headers in these files: - taxa.code: four-letter abbreviation for genus and specific name - genus: genus name - species: specific epithet - common.name: English name - group: general pollinator taxa group - empirical.C: empirically estimated conservatism score - expert#.C: conservatism score assigned by each of four experts - expert#.conf: expert's confidence in their conservatism score Blank cells in the site-species abundance matrix indicates species absence (or non-detection) Blank cells in C.scores.csv indicates missing S-ranks and unassigned C-scores (with associated missing confidence ranks) where experts lacked knowledge or confidence

Simulated sequences provide a way to evaluate multiple sequence alignment (MSA) methods where the ground truth is exactly known. However, the realism of such simulated conditions often comes under question compared to empirical datasets. In particular, simulated data often does not display heterogeneity in the sequence lengths, a common feature in biological datasets. In order to imitate sequence length heterogeneity, we here present a set of data that are evolved under a mixture model of indel lengths, where indels have an occasional chance of being promoted to long indels (emulating large insertion/deletion events, e.g., domain-level gain/loss). This dataset is otherwise (e.g., in GTR parameters) analogous to the 1000M condition as presented in the SATe paper (doi: 10.1126/science.1171243) but with 5000 sequences and simulated with INDELible (http://abacus.gene.ucl.ac.uk/software/indelible/). For more information, see README.txt. For the INDELible control files, see https://github.com/ThisBioLife/5000M-234-het.

The dataset is for a study conducted to understand genome-wide association (GWA) and genomic prediction of biomass yield and 14 yield-components traits in Miscanthus sacchariflorus. We evaluated a diversity panel with 590 accessions of M. sacchariflorus grown across four years in one subtropical and three temperate locations and genotyped with 268,109 single nucleotide polymorphisms (SNPs).

This study advances the production of potassium sorbate (KS) from triacetic acid lactone (TAL) utilizing food-grade solvents, ethanol (EtOH) and isopropyl alcohol (IPA). We have previously demonstrated the route to produce KS from TAL in tetrahydrofuran (THF) as the main solvent, but the use of THF is associated with environmental and health risks especially for food applications. The process employs a catalytic approach in food-grade solvents and includes three main steps: hydrogenation, etherification and hydrolysis, and ring-opening hydrolysis to produce KS from TAL. In the synthesis of KS from TAL, the use of IPA leads to higher yields and reduced reaction times compared to EtOH. As a result, the overall reaction time in IPA was reduced to 35.7 h, compared to 42.1 h in our previous study using THF and EtOH, while achieving a comparable KS yield of 84% from TAL. The synthesized KS exhibits a trans-2, trans-4 geometrical configuration, identical to that of commercially available KS. Through techno-economic analysis (TEA) and life cycle assessment (LCA), we estimated full-scale production of KS from sugarcane with the developed process in IPA could achieve a minimum product selling price (MPSP) of $8.27 per kg with a range of $7.06–10.16 per kg [5th–95th percentiles from 6000 Monte Carlo simulations] and a carbon intensity (CI) of 13.7 [9.6–18.6] kg CO2-eq per kg. This study highlights the synthesis of KS from TAL using food-grade solvents, demonstrating improved economic viability and environmental sustainability compared to our previous research (MPSP of $9.68 per kg [$8.47–11.45 per kg] and CI of 16.2 [12.0–21.2] kg CO2-eq per kg), as the total required reaction decreases while achieving the comparable overall yield of KS from TAL.

This dataset contains all the data for the results section in the study presented in the paper entitled "Chemistry Across Multiple Phases (CAMP) version 1.0: An integrated multi-phase chemistry mode" submitted to Geoscientific Model Development (GMD). In this paper, two sets of simulations were run to test CAMP with this results included here. This consists of (1) box model inputs and outputs presented in Section 4.2 for modal, binned and particle-resolved simulations to compare the application of identical chemical mechanisms to different aerosol representations and (2) the 3D Eulerian output presented in Section 4.3.

Nucleotide sequences from wild parsnip CYP71AJ4 (angelic in synthase. <a href ="https://www.ncbi.nlm.nih.gov/nuccore/EF191021">Genbank EF191021</a>) were obtained by Sanger sequencing. Seeds from individual plants from different populations were harvested to obtain corresponding cDNA. The cDNA was cloned and directly sequenced. Aminoacid translations were obtained using standard codon usage. Alignments of CYP71AJ4 sequences (involved in angular furanocoumarin biosynthesis) with as the reference sequence. Consistent amino acid variabilities were found between some populations. The relationship between sequencing variability and selective pressure is not yet known.

The organizations that contribute to the longevity of 67 long-lived molecular biology databases published in Nucleic Acids Research (NAR) between 1991-2016 were identified to address two research questions 1) which organizations fund these databases? and 2) which organizations maintain these databases? Funders were determined by examining funding acknowledgements in each database's most recent NAR Database Issue update article published (prior to 2017) and organizations operating the databases were determine through review of database websites.

This dataset contains nucleotide sequences of 16S rRNA gene from phytoplasmas and other bacteria detected in phloem-feeding insects (Hemiptera, Auchenorrhyncha). The datasets were used to compare traditional Sanger sequencing with a next-generation sequencing method, Anchored Hybrid Enrichment (AHE) for detecting and characterizing phytoplasmas in insect DNA samples. The file “Trivellone_etal_SangerSequencing.fas”, comprising 1397 positions (the longest sequence), includes 35 not aligned bacterial 16S rRNA sequences (16 phytoplasmas and 19 other bacterial strains) yielded using Sanger sequencing. The file “Trivellone_etal_AHEmethod1.fas” includes 34 not aligned bacterial 16S rRNA sequences (28 phytoplasmas and 6 other bacterial strains) and it contains 1530 positions (the longest sequence). Each sequence was assembled using assembled based on ABySS v2.1.0 pipeline. The file “Trivellone_etal_AHEmethod2.fas” includes 31 not aligned bacterial 16S rRNA sequences (27 phytoplasmas and 4 other bacterial strains) and it contains 1530 positions (the longest sequence). Each sequence was assembled based on the HybPiper v2.0.1 pipeline . Additional details in the "read_me_trivellone.txt" file attached below.

Bioenergy sorghum (Sorghum bicolor L. Moench) is a promising crop for contributing to the United States bioenergy supply. However, the varying limitations of the marginal lands targeted for its cultivation present a management challenge. This two-year study aimed to investigate how the limitations associated with marginal cropland impact the effects of nitrogen fertilization on the yield of bioenergy sorghum and the uptake of 11 macro- (N, P, K, Ca, Mg, and S) and micronutrients (Fe, Mn, Zn, Cu, and B). The study contrasted prime cropland in central Illinois (Urbana) with three marginal cropland sites in southern (Ewing) and central Illinois (Fairbury and Pesotum). These marginal cropland sites are characterized by varying limitations, including low soil fertility (P and K limitations), leaching and erosion, and flooding, respectively. Four nitrogen rates (0, 56, 112, and 168 kg N ha−1) were tested under eight environments. The average yields and ranges of sorghum biomass were 20.2 (17.0–23.2) Mg ha−1 in Urbana, 18.1 (13.1–19.8) Mg ha−1 in Ewing, 13.8 (9.0–17.3) Mg ha−1 in Fairbury, and 23.3 (14.6–33.0) Mg ha−1 in Pesotum. Optimal N rates were 56 N in Pesotum and 112 N in Urbana, Ewing, and Fairbury. Tissue macronutrient contents in Urbana were generally higher than in the marginal croplands, while micronutrient contents did not show discernible trends. Increasing N rate generally correlated with the macronutrient removal except in Ewing. Comparable sorghum biomass yields were observed between prime and marginal croplands (averaging 18.3 Mg ha−1), but optimal N rates varied between 56 N and 112 N. This suggests that yield gaps can be narrowed by applying the optimal N rates for the respective locations. However, increased removals of macronutrients, especially P and K, with increasing yields indicate the need to revise fertilizer recommendations, particularly for soils deficient in these nutrients. Our study suggests that while sorghum production on marginal cropland is feasible, N management needs to be adapted to the unique limitations associated with various types of marginal cropland.

Data sets to reproduce the results provided by the tutorial in paper "Data-Driven and Machine Learning Based Framework for Image-Guided Single-Cell Mass Spectrometry"

This dataset consists of the secondary ion mass spectrometry (SIMS) depth profiling data that was collected with a Cameca NanoSIMS 50 instrument from a 10 micron by 10 micron region on a Madin-Darby canine kidney (MDCK) cell that had been metabolically labeled so most of its sphingolipids and cholesterol contained the rare nitrogen-15 oxygen-18 isotopes, respectively.

Simulation trajectory data and scripts for Nature Nanotechnology manuscript "A DNA turbine powered by a transmembrane potential across a nanopore" that demonstrates a rationally designed nanoscale DNA-origami turbine with three chiral blades that uses a transmembrane electrochemical potential across a nanopore to drive a DNA bundle into sustained unidirectional rotations of up to 10 revolutions/s. Driven by the asymmetric mobility of a DNA duplex, the rotation direction of the turbine is set by its designed chirality and the salinity of the solvent.

This dataset is associated with the manuscript "Transcriptional responses of detoxification genes to coumaphos in a nontarget species, Galleria mellonella (greater wax moth) (Lepidoptera: Pyralidae), in the beehive environment" This dataset includes 2 Excel files: 1) raw_data_bioassay.xlsx: this file contains the raw data for waxworm bioassay. There are 2 worksheets within this file: - LC50: raw data for measuring LC50 in the laboratory and field strain of Galleria mellonella. - RGR: Relative Growth Rate, raw data for measuring body weight of field strain of Galleria mellonella . 2) raw-data_RT-qPCR.xlsx: this file contains raw data (Ct value) of RT-qPCR.

This study evaluates the bioethanol potential in response to irrigation (IR) and non-irrigation (NIR) of oilcane (OC) during a seasonal drought prior harvest. The juice was extracted through mechanical pressing of stems and fermented by Ethanol Red® yeast to produce first-generation bioethanol. Hydrothermal pretreatment followed by enzymatic hydrolysis of bagasse was performed to produce monomeric sugars from structural carbohydrates. The hydrolysates were fermented with engineered yeast for second-generation bioethanol production. The irrigated oilcane juice (276.3 ± 8.9 g/L) constitutes higher sugar concentrations than non-irrigated oilcane juice (236.5 ± 2.2 g/L). The enzymatic hydrolysis of IR-OC and NIR-OC pretreated bagasse yielded similar concentrations of 247.5 ± 2.22 and 249.7 ± 4.98 g/L fermentable sugars. Industry-relevant bioethanol titers of ≥99 g/L and ≥75 g/L were achieved from juice and hydrolysates, respectively. Therefore, the non-irrigation regime did not impact the 1G and 2G bioethanol titers. However, the overall bioethanol yield can be lower due to the reduction of stem yield (8 %) per hectare.

In a set of field studies across four years, the effect of self-shading on photosynthetic performance in lower canopy sorghum leaves was studied at sites in Champaign County, IL. Photosynthetic parameters in upper and lower canopy leaves, carbon assimilation, electron transport, stomatal conductance, and activity of three C4-specific photosynthetic enzymes, were compared within a genetically diverse range of accessions varying widely in canopy architecture and thereby in the degree of self-shading. Accessions with erect leaves and high light transmission through the canopy are henceforth referred to as ‘erectophile’ and those with low leaf erectness, ‘planophile’. In the final year of the study, bundle sheath leakiness in erectophile and planophile accessions was also compared.

This dataset includes data on soil properties, soil N pools, and soil N fluxes presented in the manuscript, "Refining the role of nitrogen mineralization in mycorrhizal nutrient syndromes". Please refer to that publication for details about methodologies used to generate these data and for the experimental design. For this verison 2, we added specific gross nitrogen mineralization rates (ugN/gOM/d), microbial biomass carbon (ugC/gdw), microbial biomass nitrogen (ugN/gdw) and microbial biomass C:N ratios to the newest version of the data set. Additionally, we updated values for gross nitrogen mineralization, microbial NO3 assimilation and microbial NH4 assimilation to reflect slight changes in data processing. Those changes are reflected in "220829_All data_repository.csv". "220829_nitrogen_mineralization_readme.txt " is updated readme for the new file. The other 2 files begin with “220426_” are older version and same as in V1.

These data were used to make a predictive model of when ornate box turtles (Terrapene ornata) are likely to be above ground and at risk from fire. The data were generated using shell temperatures, soil temperatures at 0.35 m deep from known overwintering sites, and the spring and fall soil temperature inversion dates during 2019–2022 to infer if 26 individual radio-tracked turtles were above or below ground at three sites in Illinois.

This dataset containes the images of B73xMS71 RIL population used in QTL linkage mapping for maize epidermal traits in year 2016 and 2017. 2016RIL_all_mns.rar and 2017RIL_all_mns.rar: contain raw images produced by Nanofocus lsurf Explorer Optical Topometer (Oberhausen, Germany) at 20X magnification with 0.6 numerical aperture. Files were processed in Nanofocus μsurf analysis extended software (Oberhausen,Germany). 2016RIL_all_TIF.rar and 2017RIL_all_TIF.rar: contain images processed from the Topology layer in each nms file to strengthen the edges of cell outlines, and used in downstream cell detection. 2016RIL_all_detection_result.rar and 2017RIL_all_detection_result.rar: contain images with epidermal cells predicted using the Mask R-CNN model. training data.rar: contain images used for Mask R-CNN model training and validation.

Rising global temperatures and urban heat island effects challenge environmental health and energy systems at the city level, particularly in summer. Increased heatwaves raise energy demand for cooling, stressing power facilities, increasing costs, and risking blackouts. Heat impacts vary across cities due to differences in urban morphology, geography, land use, and land cover, highlighting vulnerable areas needing targeted heat mitigation. Urban tree canopies, a nature-based solution, effectively mitigate heat. Trees provide shade and cooling through evaporation, improving thermal comfort, reducing air conditioning energy consumption, and enhancing climate resilience. This report focused on the ComEd service area in the Chicago Metropolitan Region and assessed the impacts of population growth, urbanization, climate change, and an ambitious plan to plant 1 million trees. The report evaluated planting 1 million trees to quantify regional cooling effects projected for the 2030s. Afforestation locations were selected to avoid interference with existing infrastructure. Key findings include (i) extreme hot hours (>95°F) will increase from 30 to 200 per year, adding 420 Cooling Degree Days (CCD) by the 2030s, (ii) greener areas can be up to 10°F cooler than less vegetated neighborhoods in summer, (iii) tree canopies can create localized cooling, reducing temperatures by 0.7°F and lowering annual CCD by 60 to 65, and (iv) afforestation can reduce the region’s temperature by 0.7°F, saving 400 to 1100 Megawatt hours of daily power usage during summer. <b>Note: The data is available upon request from <a href="mailto:dpiclimate@uilliois.edu">dpiclimate@uilliois.edu.

References - Li, Fu, Umberto Villa, Seonyeong Park, and Mark A. Anastasio. "3-D stochastic numerical breast phantoms for enabling virtual imaging trials of ultrasound computed tomography." IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 69, no. 1 (2021): 135-146. DOI: 10.1109/TUFFC.2021.3112544 - Li, Fu; Villa, Umberto; Park, Seonyeong; Anastasio, Mark, 2021, "2D Acoustic Numerical Breast Phantoms and USCT Measurement Data", https://doi.org/10.7910/DVN/CUFVKE, Harvard Dataverse, V1 Overview - This dataset includes 1,089 two-dimensional slices extracted from 3D numerical breast phantoms (NBPs) for ultrasound computed tomography (USCT) studies. The anatomical structures of these NBPs were obtained using tools from the Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE) project. The methods used to modify and extend the VICTRE NBPs for use in USCT studies are described in the publication cited above. - The NBPs in this dataset represent the following four ACR BI-RADS breast composition categories: > Type A - The breast is almost entirely fatty > Type B - There are scattered areas of fibroglandular density in the breast > Type C - The breast is heterogeneously dense > Type D - The breast is extremely dense - Each 2D slice is taken from a different 3D NBP, ensuring that no more than one slice comes from any single phantom. File Name Format - Each data file is stored as an HDF5 .mat file. The filenames follow this format: {type}{subject_id}.mat where{type} indicates the breast type (A, B, C, or D), and {subject_id} is a unique identifier assigned to each sample. For example, in the filename D510022534.mat, "D" represents the breast type, and "510022534" is the sample ID. File Contents - Each file contains the following variables: > "type": Breast type > "sos": Speed-of-sound map [mm/μs] > "den": Ambient density map [kg/mm³] > "att": Acoustic attenuation (power-law prefactor) map [dB/ MHzʸ mm] > "y": power-law exponent > "label": Tissue label map. Tissue types are denoted using the following labels: water (0), fat (1), skin (2), glandular tissue (29), ligament (88), lesion (200). - All spatial maps ("sos", "den", "att", and "label") have the same spatial dimensions of 2560 x 2560 pixels, with a pixel size of 0.1 mm x 0.1 mm. - "sos", "den", and "att" are float32 arrays, and "label" is an 8-bit unsigned integer array.

MapAffil 2016 dataset -- PubMed author affiliations mapped to cities and their geocodes worldwide. Prepared by Vetle Torvik 2018-04-05 The dataset comes as a single tab-delimited Latin-1 encoded file (only the City column uses non-ASCII characters), and should be about 3.5GB uncompressed. &bull; How was the dataset created? The dataset is based on a snapshot of PubMed (which includes Medline and PubMed-not-Medline records) taken in the first week of October, 2016. Check here for information to get PubMed/MEDLINE, and NLMs data <a href ="https://www.nlm.nih.gov/databases/download/pubmed_medline.html">Terms and Conditions</a> &bull; Affiliations are linked to a particular author on a particular article. Prior to 2014, NLM recorded the affiliation of the first author only. However, MapAffil 2016 covers some PubMed records lacking affiliations that were harvested elsewhere, from PMC (e.g., PMID 22427989), NIH grants (e.g., 1838378), and Microsoft Academic Graph and ADS (e.g. 5833220). &bull; Affiliations are pre-processed (e.g., transliterated into ASCII from UTF-8 and html) so they may differ (sometimes a lot; see PMID 27487542) from PubMed records. &bull; All affiliation strings where processed using the MapAffil procedure, to identify and disambiguate the most specific place-name, as described in: <i>Torvik VI. MapAffil: A bibliographic tool for mapping author affiliation strings to cities and their geocodes worldwide. D-Lib Magazine 2015; 21 (11/12). 10p</i> &bull; Look for <a href="https://doi.org/10.1186/s41182-017-0073-6">Fig. 4</a> in the following article for coverage statistics over time: <i>Palmblad M, Torvik VI. Spatiotemporal analysis of tropical disease research combining Europe PMC and affiliation mapping web services. Tropical medicine and health. 2017 Dec;45(1):33.</i> Expect to see big upticks in coverage of PMIDs around 1988 and for non-first authors in 2014. &bull; The code and back-end data is periodically updated and made available for query by PMID at <a href="http://abel.ischool.illinois.edu/">Torvik Research Group</a> &bull; What is the format of the dataset? The dataset contains 37,406,692 rows. Each row (line) in the file has a unique PMID and author postition (e.g., 10786286_3 is the third author name on PMID 10786286), and the following thirteen columns, tab-delimited. All columns are ASCII, except city which contains Latin-1. 1. PMID: positive non-zero integer; int(10) unsigned 2. au_order: positive non-zero integer; smallint(4) 3. lastname: varchar(80) 4. firstname: varchar(80); NLM started including these in 2002 but many have been harvested from outside PubMed 5. year of publication: 6. type: EDU, HOS, EDU-HOS, ORG, COM, GOV, MIL, UNK 7. city: varchar(200); typically 'city, state, country' but could inlude further subvisions; unresolved ambiguities are concatenated by '|' 8. state: Australia, Canada and USA (which includes territories like PR, GU, AS, and post-codes like AE and AA) 9. country 10. journal 11. lat: at most 3 decimals (only available when city is not a country or state) 12. lon: at most 3 decimals (only available when city is not a country or state) 13. fips: varchar(5); for USA only retrieved by lat-lon query to https://geo.fcc.gov/api/census/block/find

___________________________________SUMMARY This dataset contains derivative data from concurrent fMRI and scalp EEG recordings used in: Mostame Parham, Wirsich Jonathan, Alderson Thomas H, Ridley Ben, Giraud Anne-Lise, Carmichael David W, Vulliemoz Serge, Guye Maxime, Lemieux Louis, Sadaghiani Sepideh (2024) A multiplex of connectome trajectories enables several connectivity patterns in parallel eLife 13:RP98777. doi: https://doi.org/10.7554/eLife.98777.3 ___________________________________RAW DATA The data has been originally published and described as part of other studies (Morillon et al., 2010; Sadaghiani et al., 2012). Briefly, 10 minutes of eyes-closed resting state were analyzed from 26 healthy subjects (average age = 24.39 years; range: 18-31 years; 8 females) with no history of psychiatric or neurological disorders. Informed consent was given by each participant and the study was approved by the local Research Ethics Committee (CPP Ile de France III). FMRI was acquired using a 3T Siemens Tim Trio scanner with a GE-EPI pulse sequence (TR = 2 s; TE = 50 ms; 40 slices; 300 volumes; field of view: 192×192; voxel size: 3×3×3 mm3). Structural T1-weighted scan were acquired using the MPRAGE pulse sequence (176 slices; field of view: 256×256; voxel size: 1×1×1 mm3). 62-channel scalp EEG (Easycap, with an additional EOG and an ECG channel) was recorded using an MR-compatible amplifier (BrainAmp MR, Brain Products) at 5Hz sampling rate. ___________________________________PREPROCESSING fMRI and EEG data were preprocessed with standard preprocessing steps as explained in detail elsewhere (Wirsich et al., 2020). In brief, fMRI underwent standard slice-time correction, spatial realignment (SPM12, http://www.fil.ion.ucl.ac.uk/spm/software/spm12). Structural T1-weighted images were processed using Freesurfer (recon-all, v6.0.0, https://surfer.nmr.mgh.harvard.edu/) in order to perform non-uniformity and intensity correction, skull stripping and gray/white matter segmentation. The cortex was parcellated into 68 regions of the Desikan-Kiliany atlas (Desikan et al., 2006). This atlas was chosen because —as an anatomical parcellation— avoids biases towards one or the other functional data modality. The T1 images of each subject and the Desikan-Killiany were co-registered to the fMRI images (FSL-FLIRT 6.0.2, https://fsl.fmrib.ox.ac.uk/fsl/fslwiki). We extracted signals of no interest such as the average signals of cerebrospinal fluid (CSF) and white matter from manually defined regions of interest (ROI, 5 mm sphere, Marsbar Toolbox 0.44, http://marsbar.sourceforge.net) and regressed out of the BOLD timeseries along with 6 rotation, translation motion parameters and global gray matter signal (Wirsich et al., 2017a). Then we bandpass-filtered the timeseries at 0.009–0.08 Hz. Average timeseries of each region was then used to calculate connectivity. EEG underwent gradient and cardio-ballistic artifact removal using Brain Vision Analyzer software (Allen et al., 1998, 2000) and was down-sampled to 250 Hz. EEG was projected into source space using the Tikhonov-regularized minimum norm in Brainstorm software (Baillet et al., 2001; Tadel et al., 2011). Source activity was then averaged to the 68 regions of the Desikan-Killiany atlas. Band-limited EEG signals in each canonical frequency band and every atlas region were then used to calculate frequency-specific connectome dynamics. Note that the MEG-ROI-nets toolbox in the OHBA Software Library (OSL; https://ohba-analysis.github.io/osl-docs/) was used to minimize source leakage in the band-limited source-localized EEG data (Colclough et al., 2015). ___________________________________FOLDER STRUCTURE The dataset includes five separate folders as described below: 1) EEGfMRI_dFC folder: connectome dynamics of scalp data This folder contains 26 single MATLAB (.mat) files for each subject. Inside each `.mat` is a structure with fields `A`, `B`, and `C`, corresponding to fMRI, amplitude-coupling, and phase-coupling connectome dynamics, respectively. The fMRI data are 3-dimensional (ROI × ROI × timepoints). The EEG data are stored in a 1×5 cell array (Delta, Theta, Alpha, Beta, Gamma), each cell containing a 3-D ROI × ROI × timepoints matrix. 2) EEGfMRI_dFC_SourceOrtho foldeR: connectome dynamics of source-orthogonalized scalp data Same format as above, except that EEG connectome dynamics are derived from source-orthogonalized signals. The MEG-ROI-nets toolbox in the OHBA Software Library (OSL; https://ohba-analysis.github.io/osl-docs/) was used to minimize source leakage in the band-limited, source-localized EEG data (Colclough et al., 2015). 3-5) Cross-modal Recurrence Plot (CRP) data Each subject has an Excel file with five sheets (Delta through Gamma), corresponding to the five frequency bands. Each sheet contains a 2-D CRP matrix (rows = fMRI timepoints, columns = band-limited EEG timepoints). - Scalp EEG–fMRI CRPs (CRP_EEGfMRI and CRP_EEGfMRI_SourceOrtho folder): two versions (with and without source-orthogonalization), each has 52 Excel files, including amplitude- and phase-coupling CRPs. - Intracranial EEG–fMRI CRPs (CRP_iEEGfMRI folder): one version, 27 Excel files, containing three cases: amplitude coupling, HRF-convolved amplitude coupling, and phase coupling.

This repository provides the data and code used to reproduce key plots from the manuscript and to extend discussions that were only briefly covered therein. All MATLAB scripts were developed and tested in MATLAB R2024a. All Python scripts were developed and tested in Python 3.11.2. * <b> NOTE:</b> New in this V3: 1. 2 new MATLAB files (ChiralPointGroups.m and THz_current_estimation.m), ChiralPointGroups.pdf (a compiled version of ChiralPointGroups.m) and theoretical model code (theoretical_model.zip) are added. More information can be found in the readme. 2. Updated and renamed "publication_data.zip" (in V2) to "data_and_analysis.zip" 3. Change License from CC BY to "Other license". Licensing Terms: Data (all .mat files) is under CC BY and Code is released under MIT license. Therefore, V3 is bound to this new license. V2 is still under CC BY. <b>→ Data and analysis code (data_and_analysis.zip):</b> The dataset is organized into five subfolders. Each subfolder corresponds to a unique combination of experimental conditions, including: • Magnetic field orientation (B ∥ c or B ⟂ c) • Scan parameter (magnetic field or temperature) • Pump laser polarization (linear s, linear p, or circular) • Detection polarization (linear s) Each folder contains: • The raw time-domain data files (.mat) • Oscillator parameters extracted via linear prediction algorithm (.mat) • MATLAB scripts (.m) that generate plots of the raw data, processed fits, and amplified modes. Each script should be run within its corresponding folder to ensure proper loading of the associated data files. Folder summary: 1. B_parallel_c_linear_spump_sprobe_field: B ∥ c, s-polarized pump, s-polarized THz detection, magnetic field dependence 2. B_parallel_c_linear_spump_sprobe_temperature: B ∥ c, s-polarized pump, s-polarized THz detection, temperature dependence 3. B_perp_c_linear_spump_sprobe_field: B ⟂ c, s-polarized pump, s-polarized THz detection, magnetic field dependence 4. B_perp_c_linear_spump_sprobe_temperature: B ⟂ c, s-polarized pump, s-polarized THz detection, temperature dependence 5. B_parallel_c_LCPRCP_pump_sprobe_field: B ∥ c, circularly polarized pump (LCP & RCP), s-polarized THz detection, magnetic field dependence <b>→Theoretical model code (theoretical_model.zip):</b> The Python script depends on packages “numpy” and “matplotlib”. The script generates a plot of the dispersion relations of the theoretical model introduced in the Main Text. More precisely, it plots the real (red) and imaginary (blue) parts of the frequency (ω) as a function of wavenumber (k) as obtained by solving the characteristic equation, equation (6) of the Supplemental Information, with σ_E and σ_Μ given respectively by equations (3) and (2) of the Main Text. All branches of the dispersion relations are plotted simultaneously. All model parameters are adjustable. The included Mathematica notebook (printout also provided in .pdf format) was used to obtain symbolic expressions for the coefficients of powers of ω appearing in the characteristic determinant. These coefficients were copied directly into the Python function detCoeffs(). <b>→ Standalone scripts (not in subfolders):</b> • ChiralPointGroups.m Outputs a table summarizing the 2D matrix representation of σ_Μ in the 11 enantiomorphic point groups. ChiralPointGroups.pdf is a compiled version of chiral point groups table, identical to the output of ChiralPointGroups.m. • THz_current_estimation.m Estimates the photoinduced THz current in tellurium under magnetic field. The script evaluates a phenomenological resonant contribution to the magnetoelectric coupling (with negligible dependence on NIR polarization), leading to excitation of s-polarized, B-antisymmetric mode S_odd at ~0.37 THz. These standalone scripts provide additional physical discussion and calculation detail that are intentionally streamlined or omitted from the published manuscript and its supplementary materials for clarity and space.