Continuous monitoring and discrete water-quality sampling were coupled in a karst aquifer to assess drivers and timescales of water-quality change. Water-quality data included environmental tracers of groundwater age (tritium [3H], tritiogenic helium-3 [3He-trit], sulfur hexafluoride [SF6], carbon-14 [14C], and radiogenic helium-4 [4He-rad]). All water quality data is available from the U.S. Geological Survey NWIS database (U.S. Geological Survey, 2019). Groundwater ages were estimated by calibration of environmental tracers to lumped parameter models of groundwater age for multiple samples collected at six groundwater wells. The final estimates for mean groundwater ages ranged from less than 10 to greater than 700 years and provide insight into timescales of aquifer vulnerability. This data release includes five Microsoft Excel tables detailing these data (each table is also provided as a tab-delimited text file): Table_1_TXETN_AgeInterpretations: Table containing dissolved gas modeling results, environmental tracer concentrations, and lumped parameter modeling results. Table_2_TXETN_DissolvedGasModeling: Table containing dissolved gas modeling results. Table_3_TXETN_ComputedTracerConcentrations: Table containing computed tracer concentrations. Table_4_TXETN_14CAdjustments: Table containing Carbon 14 adjustments. Table_5_TXETN_Abbreviations: Table containing definitions for all abbreviations found in tables 1 through 4.

Summary of relational tables in the TSCEvolTree_Aze&2011_CorrJul2018 database

MorphospeciesAze_TableS3

Details for the 339 morphospecies of the Aze & others paper [1], augmented from [1, Appendix S1, Table S3 and Appendix S5, worksheet aM]. The main focus is on clarifying the choice of stratigraphic ranges and ancestry, and incorporating post-publication corrections by the authors of Aze & others or selective corrections/amendments during conversion to TimeScale Creator.

Stratigraphic ranges are given in Ma values; the time scales of the sources for the Ma values are made explicit (via links to table, MorphospeciesAze_TableS3DateRef). Almost all ranges are simple, as per those provided by the 2011 paper, delineated by lowest (start date) and highest occurrence (end date). However, a small number of ranges more closely represent those given by the nominated sources by also including range extensions: “questioned” or “questioned (rare)” for less confident stratigraphic occurrences; and “conjectured”, where a range extension is hypothesized, usually to support an ancestry proposal lacking contiguous stratigraphic occurrences. A proportion (~15 %) of Ma values are corrected where minor differences in Ma values were found between the 2011 paper and the nominated source; however, a systematic check was not conducted across the dataset. A further proportion (~15 %) of Ma values are amended where alternative sources appear to better represent the intention of the 2011 paper; these include a few instances where there would be a conflict with the index (marker) datum sequence of the Wade & others [2] zonation. Corrections to Ma values are accompanied by brief explanatory comments. Minor changes to Ma values were also made by one of us (TA) for a proportion (~17 %) of entries; most of these corresponded to the already invoked corrections or amendments.

Entries for ancestors follow the 2011 paper, with two exceptions in which adjustments to Ma values have removed the overlap in range between ancestor and descendant: a correction made by Tracy Aze (for Pulleniatina finalis, P. obliquiloculata replaced P. spectabilis); and an amendment (for Paragloborotalia pseudokugleri, Dentoglobigerina galavisi is amended to D. globularis). Levels of evidential support for the ancestor–descendant proposals were not critically appraised as part of the TimeScale Creator conversion. However, column [PhylogenyMethod] was employed to distinguish a small number of proposals which were distinctly less (“not well”) or better (“strongly”) supported than the typical “well supported” proposals presumed for this group.

All other information given in [1, Table S3] was incorporated, including indications of morphology, ecology, geography, and analyses made using the Neptune database. This information from Table S3 also included the lists of segments from both morphospecies (ID) and lineage (LID) trees within which each morphospecies occurred; in terms of relational logic, these could be supplanted by a single entry, the code for the lineage containing the highest occurrence of the morphospecies, and this was added manually for the TimeScale Creator conversion.

BiospeciesAze_aL

Details for the 210 lineages of the 2011 paper, augmented from [1, Appendix S5, worksheet aL]. The main focus is to maximize and maintain consistency and transparency between morphospecies and lineages for Ma values of their stratigraphic ranges. This is achieved for the TimeScale Creator conversion by nominating a morphospecies whose Ma value (start or end date) potentially defines the date (start or end) for a lineage; each morphospecies chosen for this is based on the apparent link between morphospecies and lineage dates used in the 2011 paper; this morphospecies is given by column [StartDateOrigLinkMph]. For start dates, ~40 % of lineages could be linked in this way; for end dates, almost all (93 %) were. Where a lineage range point of the 2011 study did not correspond to a morphospecies range point, then this morphospecies is at least used to provide the time scale applied to the date for the lineage.

Entries for ancestral lineages follow the 2011 paper, with two exceptions necessitated by changes in Ma values which place the ancestral lineage outside the date of origin of the descendant lineage: N150-N151-T153, involving the origin of morphospecies Paragloborotalia pseudokugleri; and N52-N54-T53, involving the origin of morphospecies Hirsutella cibaoensis. Levels of evidential support for the ancestor–descendant proposals were not critically appraised as part of the TimeScale Creator conversion. However, column [PhylogenyMethod] was employed to distinguish two proposals that were distinctly less (“not well”) or better (“strongly”) supported than the typical “well supported” proposals presumed for this group. The assignment of branching type as bifurcating or budding in the 2011 paper is incorporated.

Ecogroup and morphogroup allocations follow the 2011 paper (these data were not provided with the 2011 paper, but were indicated by colours employed in [1, Appendices S2, S3]; some colours for lineage morphogroups needed to be corrected; the ecogroup and morphogroup data for lineages were provided for the TimeScale Creator conversion by one of us [TA]). Some minor exceptions to these ecogroup and morphogroups were invoked for the TimeScale Creator conversion, in order to better match those of the contained morphospecies.

MorphospeciesAze_TableS1_Morphogroup

Details for morphogroups used for morphospecies and lineages; as for [1, Appendix 1, Table S1, "Morphogroup"], with explicit colour codes.

MorphospeciesAze_TableS1_Ecogroup

Details for ecogroups used for morphospecies and lineages; as for [1, Appendix 1, Table S1, "Ecogroup"], with explicit colour codes.

MorphospeciesAze_TableS3_EcogroupReference

Sources for ecogroups assigned to morphospecies; as for "Ecogroup reference", taken from [1, Appendix 1, Table S3]; multiple references in the original entries are accorded a row each.

MorphospeciesAze_TableS3_AppendixS1C_References

References for [1, Appendix 1, Table S3 ].

MorphospeciesAze_TableS3DateRef

Sources, and their time-scales, used for Ma values (sources from [1, Appendix 1, Table S3, "Date reference"] "Date reference", Table S3, Appendix 1 of the 2011 paper). The key purpose is to make explicit the time scale against which the source has (apparently) provided the Ma value, essential in order to appropriately recalibrate to the current GTS time scale and also to maintain the capability to recalibrate to future time scales. An important example of this need is where dates from the Paleocene Atlas [3] have here been remeasured directly from the Atlas and so are against the time scale of Berggren & others [4], rather than calibrated to Wade & others [2] as in the 2011 study.

In the interests of transparency and to provide a pointer to recalibration steps needed, a further level of specificity is needed for those sources which imply more than one time scale for Ma values used. For the TimeScale Creator conversion, references to these sources also have the time scale specified. Examples include chapters from the Eocene Atlas [5]. For instance, in order for the TimeScale Creator conversion to record the questionable parts of the stratigraphic ranges given for some Clavigerinella morphospecies by Coxall & Pearson [6], additional start dates for these morphospecies have been measured directly from their Figure 8.1, drawn against the scale of Berggren & Pearson [7]. However, these dates need to be integrated with the Ma values from Coxall & Pearson already used in the 2011 paper, which were presented recalibrated by them to the scale of Wade & others. These two sets of sources are given as, respectively, “Coxall & Pearson (2006: BP05)” (against Berggren & Pearson) and “Coxall & Pearson (2006)” (against the time-scale option of Wade & others which was calibrated to Cande & Kent [8]). Analogous examples came from sources such as Berggren & others, which include some dates for which the usual recalibration is not applicable (reasons are specific to each instance and are indicated in comments fields in table, MorphospeciesAze_TableS3; Appendix S1b includes descriptions of these fields in worksheet, DesignMorphospeciesAze_TableS3, and corresponding data in worksheet, MorphospeciesAze_TableS3).

MorphospeciesAze_TableS3DateRef_DateScale

This simply gives full names for the four time scales requiring recalibration: BKSA95: Berggren & others, 1995 [4] BP05: Berggren & Pearson, 2005 [7] WPBP11(CK95): Wade & others, 2011 [2]; calibrated to Cande & Kent, 1995 [8] WPBP11(GTS04): Wade & others, 2011 [2]; calibrated to Gradstein & others, 2004 (GTS2004) [9].

Wade & others, 2011 Datum

Details for datums relative to zonations, compiled from [2, Tables 1, 3, 4 ].

Zonal (marker) datums are indicated, but other datums are also included, almost all of which provide intrazonal intervals employed for calibration between time scales. Datums specific to the BKSA95 zonation are separately tabulated from those of BP05, allowing calibration between zonations BKSA95, BP05, WPBP11(CK95), and WPBP11(GTS04) (see MorphospeciesAze_TableS3DateRef_DateScale, above). The WPBP11(GTS04) zonation corresponds to GTS2004 and so allows calibration to later GTS time scales (GTS2012, GTS2016).

Additional columns provide brief indications of adjustments needed for calibration, including a small number of alternative datums resulting from revised definitions of zonations. Nomenclatural links are provided for datum-naming taxa.

Global tables:

SpeciesGroupName GenusGroupName ChronosPortal ColoursClofordWebSafeByHue

augmented from TimeScale Creator spreadsheet data:

TimeUnit_ReferenceUnit TimeUnit TSCPlanktonicForaminifersDatum TSCPlanktonicForaminifersDatumMorphospecies

Datapack

Birth-death models are stochastic processes describing speciation and extinction through time and across taxa and are widely used in biology for inference of evolutionary timescales. Previous research has highlighted how the expected trees under constant-rate birth-death (crBD) tend to differ from empirical trees, for example with respect to the amount of phylogenetic imbalance. However, our understanding of how trees differ between crBD and the signal in empirical data remains incomplete. In this Point of View, we aim to expose the degree to which crBD differs from empirically inferred phylogenies and test the limits of the model in practice. Using a wide range of topology indices to compare crBD expectations against a comprehensive dataset of 1189 empirically estimated trees, we confirm that crBD trees frequently differ topologically compared with empirical trees. To place this in the context of standard practice in the field, we conducted a meta-analysis for a subset of the empirical...

We release dynamical ejecta data from binary neutron star merger simulations. The outflows are extracted at a fixed coordinate sphere with radius 300 G/c^2 Msun (= 443 km). Only material unbound according to the geodesic criterion is considered to be part of the dynamical ejecta. See [1] for more details. Included data: Table2.txt: Table 2 of the paper in machine readable format tabulated_nucsyn.h5: nucleosynthesis yields from pre-computed parametrized trajectories. The first three indices of each dataset are Ye, entropy, and expansion timescale tau. For example Y_final[iYe, ientr, itau, iiso] gives the final abundance of isotope iiso with A[iiso] and Z[iiso] for a trajectory with initial Ye = Ye[iYe], initial entropy s[ientr], and expansion timescale tau[itau]. tabulated_rho.h5: gives the density at T = 6 GK corresponding to the Ye, entropy, and expansion timescale used in tabulated_nucsyn.h5. [model].tar: ejecta data for individual simulations. The naming convention is the same as in the paper. For each model we provide: outflow.txt: angle integrated outflow rate and cumulated ejecta mass. Data are given in units with Msun = G = c = 1 (eg, the conversion factor for time to seconds is 4.9258e-6). hist_entropy.dat: histogram of the ejecta as a function of the entropy (in kb) hist_vinf.dat: histogram of the ejecta as a function of the asymptotic velocity (in units of c) hist_ye.dat: histogram of the ejecta as a function of the electron fraction Ye. profile.txt: time integrated ejecta profiles as a function of the polar angle. hist_vinf_theta.h5: histograms of the ejecta as a function of the asymptotic velocity and the polar angle. hist_ye_theta.h5: histograms of the ejecta as a function of the asymptotic velocity and the polar angle. hist_ye_entropy_tau.h5: histograms of the ejecta as a function of Ye, entropy, and expansion timescale tau. Additionally we distribute: Initial data generated with LORENE and associated EOS tables. EOS tables used for the evolution Parameter file used for each simulation For the multidimensional histograms the indices are ordered as specified in the file name, ie the file hist_ye_theta.h5 tabulates the ejecta mass as a function of Ye (first index) and polar angle theta (second index). [1] D. Radice, A. Perego, K. Hotokezaka, S. A. Fromm, S. Bernuzzi, and L. F. Roberts, Binary Neutron Star Mergers: Mass Ejection, Electromagnetic Counterparts, and Nucleosynthesis, ApJ 869:130 (2018), arXiv:1809.11161

Description This dataset is designed for whole life cycle management of civil engineering projects, integrating Building Information Modeling (BIM) and Artificial Intelligence (AI). It includes comprehensive project data covering cost, schedule, structural health, environmental conditions, resource allocation, safety risks, and drone-based monitoring.

Key Features Project Metadata: ID, type (bridge, road, building, etc.), location, and timeline. Financial Data: Planned vs. actual cost, cost overruns. Scheduling Data: Planned vs. actual duration, schedule deviation. Structural Health Monitoring: Vibration levels, crack width, load-bearing capacity. Environmental Factors: Temperature, humidity, air quality, weather conditions. Resource & Safety Management: Material usage, labor hours, equipment utilization, accident records. Drone-Based Monitoring: Image analysis scores, anomaly detection, completion percentage. Target Variable: Risk Level (Low, Medium, High) based on cost, schedule, safety, and structural health. Use Cases Predictive Modeling: Train AI models to forecast project risks and optimize decision-making. BIM & AI Integration: Leverage real-time IoT and drone data for smart construction management. Risk Assessment: Identify early signs of cost overruns, delays, and structural failures. Automation & Efficiency: Develop automated maintenance and safety monitoring frameworks

AK_CK_speech_tracking_2018

The high spatial resolution and century-long Standardized Precipitation Evapotranspiration Index (SPEI) dataset with a spatial resolution of 0.0083 degrees (~1 km) was spatially downscaled from the global SPEI data with a 0.5 degrees spatial resolution (https://spei.csic.es/database.html) based on machine learning integrated with high spatial resolution climatic and topographic variables. The 1-km SPEI datasets are across the land areas of China from January 1901 to December 2020, including 1-month, 3-month, 6-month and 12-month SPEIs. The unit of the data is 0.01. The dataset was evaluated using the root zone soil moisture and the historical drought events, and the evaluation indicated that the high spatial resolution SPEI dataset is reliable.

Data Information:

GPRChinaSPEI1km: High spatial resolution and century-long SPEI datasets over China from 1901 to 2020 generated by machine learning

Publication:

He, Q., Wang, M., Liu, K., & Wang, B. (2025). High-resolution Standardized Precipitation Evapotranspiration Index (SPEI) reveals trends in drought and vegetation water availability in China. Geography and Sustainability, 6(2), 100228. https://doi.org/10.1016/j.geosus.2024.08.007

----------------------------------------------------data description---------------------------------------------

This is a gridded dataset for the Standardized Precipitation Evapotranspiration Index (SPEI) at a spatial resolution of 1 km over the main terrestrial lands of China for each month during 1901-2020, which is generated using the Gaussian process regression (GPR) based on the Global SPEI database (https://spei.csic.es/database.html) integrated with high spatial resolution climatic and topographic variables. Four timescales of SPEI were generated: 1-month (SPEI-1), 3-month (SPEI-3), 6-month (SPEI-6) and 12-month (SPEI-12). The details are as follows:

Region: China

Temporal Extent: January 1901 to December 2020

Spatial resolution: 0.0083° (~1 km)

Temporal resolution: month

Timescales: 1-month, 3-month, 6-month and 12-month

Data format: GeoTIFF

Unit: unitless (0.01)

Geographic coordinate system: WGS 1984

---------------------------------------------------dataset filename---------------------------------------------

The file name specifically shows the data information.

For example,

“SPEI_1_2020_1.tif” means “1-month SPEI of January 2020”.

“SPEI_3_2020_1.tif” means “3-month SPEI of January 2020”.

All the file names are formatted in “SPEI_timescale_year_month”

timescale: 1, 3, 6 and 12 indicate 1-month, 3-month, 6-month and 12-month, respectively

year: from 1901 to 2020

month: from 1 to 12

--------------------------------------------------storage information-------------------------------------------

The high-resolution SPEI dataset is stored in TIFF format using WGS 1984 coordinate system. The data type is int16 with a scale factor of 0.01. The nodata value is -32768. The dataset requires multiplication by 0.01 during application to obtain the actual value ranges.

The data were compressed into .rar format every 10 years for each timescale SPEI.

Dataset Description from the EGA: "To better understand the pattern of genetic changes over time, we performed whole exome sequencing of sequential bone marrow samples from 9 patients taken overtime including some paired SMM/newly diagnosed MM/Relapse MM samples.
Samples from 9 patients (9 controls and 53 tumors) underwent whole exome sequencing with an additional capture for the IGH, IHK, IGL, and MYC loci. DNA was obtained from either CD138+ cells from the bone marrow of smoldering myeloma patients through time (tumor) or from stem cell harvests or peripheral blood cells from the same patient (control). 100 ng of DNA was fragmented, end-repaired, and adapters ligated using NimbleGen's MedExome. After PCR amplification hybridized libraries underwent further amplification before being sequenced on a NextSeq500 (Illumina) using 75 bp paired end reads. "

This timeline of music notation is by no means complete and should be treated as a work in progress. I regularly come across a couple dozen or so music notation systems a month now, often quite by accident while doing research in other areas, and as many of those intentional [or not] searches fall into regional clusters it made more sense to start documenting them as such. This means that this Music Notation Timeline page gets updated far less frequently.

I’ll only be updating this intermittently while I focus on several other annotated text documents of the timeline (separated primarily by region) and populating the database with examples. The timeline will eventually be incorporated into a Global Music Theory resource which will include other projects like the Arabic Music Theory Bibliography (650-1650) Project, the Early Black Musicians, Composers, and Music Scholars (505-1505 CE), and the Bibliography of Slave Orchestras and Ensembles.

With literally hundreds, if not thousands of examples from the 20th century to today, that may very well be divided into a separate project especially as a number of new notations are being developed directly as notation programs and software (see the Non-CWN Music Notation Software list for many examples).

One thing to keep in mind with music notation is Sandeep Bhagwati’s (2013a and 2013b) idea of Notational Perspective: the idea that all notations have a universal AND a context-dependent feature which shapes what’s stable over time and what is malleable.

PUBLISHED 2/4/2017; UPDATED 4/30/2023

Boy Bands and Members Analysis

Investigating Popular Music Groups and their Impact on Pop Culture

By Priyanka Dobhal [source]

About this dataset

This dataset provides an in-depth analysis of popular boy bands from the past and present. You can explore the detailed information about various boy bands, including their names, members, and years active. With this dataset you can trace the evolution and legacy of each band by studying their timeline over time. You can also get an insight into which bands are still active today and which ones have disbanded or changed members. All in all, this dataset will help you understand why these boy bands had such a big impact on pop culture!

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For more datasets, click here.

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How to use the dataset

This dataset, Boy Bands and their Members: An Analysis of provides a comprehensive analysis of data on various boy bands, their members, and the years they were active. This dataset can be used to analyze popular trends in music as well as get an overview of each boy band.

To get started with this dataset, you should first understand the columns that are included in this dataset. They are: S.No., Band, Years Active, Members, Active?, and Timeline. From these columns could gather information about the band’s timeline (when it was most active) its current activity status (if it is active or not), and the specific names of its members from which you could further explore their careers afterwards.

Once you have an understanding of what is provided in this data set - namely

• The serial number associated with a boy band;
• The name of said boy band;

• Years during which they were active ; 4) A list/breakdown of all its members; 5 ) It's current active status ('Active' or 'Inactive', accordingly); 6 ) And lastly- a sequential timeline depicting when each member joined said Band - you can begin to effectively analyze within your commands/queries each factors associated with any given Boy Band. Such field work may yield various insights derived from the actual records found within this database (examples being added depth to one's musical knowledge- more insight into musical diversity when analyzying different boys vs girl bands). Ultimately we hope that such exploration encourages well rounded investigations for readers who enjoy delving into aggregate data!

  Happy Exploring & Enjoying!

Research Ideas

• Analyzing the lifespans of boy bands and use that data to inform potential new boy band’s expectations and trajectories.
• Examining successful partnerships between members in order to encourage collaboration between similar artists.
• Creating an interactive website that showcases various themes related to specific boy band, such as sound and visual style, milestones achieved, cultural context during their reign etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: Boy Band.csv | Column name | Description | |:-----------------|:-------------------------------------------------------------------------| | S.No. | Unique identifier for each band representing an integer value. (Integer) | | Band | Name of the boy band. (String) | | Years Active | Years in which the boy band was active. (String) |

File: Boy Band Members.csv | Column name | Description | |:--------------|:------------------------------------------------------------------------------------------| | Band | Name of the boy band. (String) | | Members | Names of members of the boy band. (String) | | Active? | Whether or not the group is still together. (Boolean) | | Timeline | The years when each member entered/left their time with their respective groups. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Priyanka Dobhal.

Additional file 2. Table S1. Species-level classification of the short read sequencing data for the four timepoints. The relative abundance of the species in the samples was determined after removing human and viral reads from the data. A subset of these data (the top 9 species in the samples) are shown in Fig. 1.

The data includes the raw mechanical data (time, load, displacement, pore pressure, pore pressure volume and confining pressure) and the meaningful processed data used to plot figures and draw main conclusions (stress, strain, strain rate, pore volume change, effective mean stress, inelastic strain, yield points and Youngs modulus). In total 10 samples of Bluersville Sandstone were deformed under either constant strain rate or constant stress (creep) conditions and at room temperature, 75°C, 150 °C. Bluersville Sandstone is from Bleurville, Vosges, north-eastern France. This pale beige coloured sandstone has a starting porosity of 22.7%Used as clean, porous sandstone of homogenous nature. Data generated at University College London on a conventional triaxial apparatus. This dataset is used and fully described/interpreted in the paper: M. Jefferd, N. Brantut, P.G. Meredith and T.M. Mitchell, The Influence of Elevated Temperature on Time Dependent Compaction Creep in Sandstone , submitted to J. Geophys. Res.

ASFV_dataDataset for the African Swine fever virus used in this study.asfv_dat.nexprimates_dataThis contains the dataset of the primates used in this study.primate_data.nex

Geochemical measurements which quantify the lithium isotope ratios (d7Li) of Paleozoic (541–251 Mya) mudstones. Samples were obtained from various field locations across Scotland, Wales, England and New Brunswick, Nova Scotia, Canada. Sampled mudstones are listed under their formation name, with information on the locations of each outcrop belt and further details on lithological characteristics, including environment of formation, freely available on the British Geological Survey Lexicon of named rock units (https://www.bgs.ac.uk/technologies/the-bgs-lexicon-of-named-rock-units/) and Government of Canada weblex (https://weblex.canada.ca/weblexnet4/weblex_e.aspx), for UK and Canadian samples, respectively. Stratigraphic age is given in accordance to the GSA geological timescale v.5.0. Following sampling, specific methodologies for preparation for Lithium isotope analysis are provided in the Metadata Lineage. The data was collected to understand changes in weathering intensity coeval with the Paleozoic expansion of land plants, with lithium isotopes a powerful trace for silicate weathering as they are sensitive to the balance between rock dissolution and clay formation. The tabulated lithium isotope ratios were compared at different temporal stages of plant evolution through the Paleozoic. Samples were collected by the University of Cambridge. Lithium isotope ratios were obtained by William McMahon and supervised by Edward Tipper and Mohd Tarique. Mass spectrometry was carried out by William McMahon and David Wilson at University College London.

Stl. files of optical scans of footprints. All files with 'Australia' in the name are of footprints from slab MV P258240 (see Fig. 2 of paper). Files with 'Poland' in the name show footprints from the Sudetic Basin of Poland (see Fig. 3 of paper). All scans made by author Grzegorz Niedzwiedzki, with permission, from registered museum specimens (see Figs. 2 and 3 of paper for details). The files can be viewed in any software that can handle the stl format, for example in Blender.

The dataset provides model output from the GEOS-Chem chemical transport model to support the CAMMPCAN and MARCUS 2017-2018 voyages.

The model version used was GEOS-Chem v12.8.1, with DOI: 10.5281/zenodo.3837666. The DOI should be cited when using this dataset. Modifications were made to the standard v12.8.1 to include abiotic ocean emissions of volatile organic compounds as implemented in Travis et al. (2020).

The model was running using MERRA-2 meteorology at 2°x2.5° (latitude x longitude) horizontal resolution with 47 vertical levels throughout the entire period of the voyages. These model runs were preceded by a 6-month spin-up at 4°x5° beginning 1 May 2017.

The following output types are included (for variable names, see below):

1. Output along the shiptrack Filenames: mrg20m_SS_ss_YYYYMMDD.txt The model has been sampled to directly match the location of the ship at every minute during the voyages. The output is then averaged to ensure there is only one data point for each unique model gridbox-timestep combination. The resulting dataset is 1-dimensional, with values for latitude, longitude, and time along with the data variables. These are text files (with space as separator character).
2. Global monthly means Filenames: GEOSChem.{DATA_TYPE}.{YYYYMM}01_monmean.nc4 {DATA_TYPE} can be any of SpeciesConc, StateMet, Aerosols, AerosolMass (see below). {YYYYMM} is the year and month for the data included in the file. The model output has been averaged on a monthly timescale and is provided for all model gridboxes globally. The resulting dataset is 3-dimensional (longitude, latitude, level). These are netcdf files.
3. Regional daily means Filenames: GEOSChem.{DATA_TYPE}.{YYYYMM}01_regional.nc4 {DATA_TYPE} can be any of SpeciesConc, StateMet, Aerosols, AerosolMass (see below). {YYYYMM} is the year and month for the data included in the file. The model output has been averaged on a daily timescale and is provided for all gridboxes in the region bounded by 30-90°S (inclusive). The resulting dataset is 4-dimensional (longitude, latitude, level, time). These are netcdf files.
4. Emissions Filenames: HEMCO_diagnostics.{YYYYMM}01.nc4 {YYYYMM} is the year and month for the data included in the file. The model emissions (and related variables) have been averaged on a monthly timescale and are provided for all gridboxes globally. The resulting dataset includes both 2-dimensional (longitude, latitude) and 3-dimensional (longitude, latitude, level) variables. These are netcdf files.

Output data types (correspond to filenames given above): 1. SpeciesConc: Concentrations of advected model species. 2. StateMet: Meteorological fields and other derived quantities. 3. Aerosols: Diagnostics for aerosol optical depth and related quantities from full-chemistry simulations. 4. AerosolMass: Diagnostics for aerosol mass and particulate matter

Variable names for each output data type are provided in the file GEOSChem_Diagnostics.xlsx (one tab for each output data type). For species names (used in the SpeciesConc files and along-shiptrack files) and properties, see file GEOS-Chem_Species_Database.json. For emission diagnostic names (used in the HEMCO_diagnostics files) see file HEMCO_Diagn.rc.

References: The International GEOS-Chem User Community. (2020, May 21). geoschem/geos-chem: GEOS-Chem 12.8.1 (Version 12.8.1). Zenodo. http://doi.org/10.5281/zenodo.3837666

Travis, K. R., Heald, C. L., Allen, H. M., Apel, E. C., Arnold, S. R., Blake, D. R., Brune, W. H., Chen, X., Commane, R., Crounse, J. D., Daube, B. C., Diskin, G. S., Elkins, J. W., Evans, M. J., Hall, S. R., Hintsa, E. J., Hornbrook, R. S., Kasibhatla, P. S., Kim, M. J., Luo, G., McKain, K., Millet, D. B., Moore, F. L., Peischl, J., Ryerson, T. B., Sherwen, T., Thames, A. B., Ullmann, K., Wang, X., Wennberg, P. O., Wolfe, G. M., and Yu, F.: Constraining remote oxidation capacity with ATom observations, Atmos. Chem. Phys., 20, 7753–7781, https://doi.org/10.5194/acp-20-7753-2020, 2020.

Additional file 7. Table S9. Total reads, sequencing coverage, assembled genome draft size and N50, and taxonomic annotation for all 53 isolates from stool samples of time points A, C, and D.

Gene expression data have been presented as non-normalized (2-Ct*109) in all but the last two rows; this allows for the back-calculation of the raw threshold cycle (Ct) values so that the typical range of expression of each gene can be more easily assessed by interested individuals. The sample number fraction following the island name represents the number of outliers over the total number of samples for which a Mahalanobis distance could be calculated (rather than the number of samples analyzed from that site). Values representing aberrant levels for a particular response variable (i.e., that contributed to the heat map score) have been highlighted in bold. When there was a statistically significant difference (student’s t-test, p

This dataset consists of physical, chemical and ecological water quality variables collected at Blelham Tarn during 2016-2017. Profiles for the following variables were collected on a weekly scale during the stratified period of the lake and fortnightly or monthly timescales outside of the stratified period; light, chlorophyll a (phytoplankton biomass proxy), temperature, specific conductivity, phycocyanin (cyanobacteria pigment), pH and oxygen (mg/l and percentage saturation). The secchi depth was also collected at the same sampling frequency. Phytoplankton taxonomy and biovolumes were collected at 1-6 m June-November 2016 at a weekly interval. Nutrient profiles (nitrogen, phosphorus and silica) were collected on a monthly timescale in June-October 2016. In addition to lake data, samples from three lake inflows and the outflow were collected for nutrients (nitrogen, phosphorus and silica) at monthly intervals along with discharge. Water level and water temperature were also recorded at a 15 minute frequency at one inflow and the outflow.