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Permafrost Thaw Settlement Dataset
<p>The Permafrost Thaw Settlement Dataset comprises four data files containing measurements, properties and derived parameters related to permafrost thaw settlement tests. This dataset is intended to support research on mechanical properties of permafrost, particularly in understanding the relationship between soil properties and thaw settlement behavior.</p> <h3><strong>Data Paper Reference</strong></h3> <p>A detailed description of the dataset, including methodology, data sources, and analysis, is provided in the accompanying data paper submitted to <em>Earth System Science Data</em>:</p> <div> <div>Mohammadi, Z., Hayley, J.L., 2025. Compilation and Analysis of Thaw Settlement Test Results: Implications for Prediction Tools and Stress-Strain Characterization in Permafrost. <a href="https://doi.org/10.5194/essd-2024-514">https://doi.org/10.5194/essd-2024-514</a></div> </div> <p>An <strong>updated GitHub repository</strong> containing fully reproducible R code and figures: → <a href="https://github.com/Zucchii/ThawSettlement_DataPaper/releases/tag/essd-pts-final">https://github.com/Zucchii/ThawSettlement_DataPaper/releases/tag/essd-pts-final</a></p> <p> </p> <p>The GitHub repository includes:</p> <ul> <li> <p>R scripts for data processing, analysis, and figure generation</p> </li> <li> <p>Pre-generated figures and processed datasets for convenience</p> </li> <li> <p>Detailed instructions for reproducing all results</p> </li> </ul>
Haloferax volcanii theoretical proteome exported from HaloLex
<p>The theoretical proteome is derived from the annotated Haloferax volcanii DS2 genome (Hartmann et al, 2010).</p> <p>This proteome represents a Gold Standard Protein based annotation (Pfeiffer and Oesterhelt, 2015).</p> <p>Proteome data from Haloferax volcanii were comprehensively analyzed in a community-based effort (Schulze et al, 2020)</p> <p>Various proteome studies are based on versions of this theoretical proteome. Different versions, which are cited in proteome papers, are made available in this series of Zenodo uploads.</p>
Probabilistic-deterministic storm surge return level dataset for the Bengal delta
<p>Bengal delta shoreline, spanning Bangladesh and India, gets hit every 3 years on average by a major tropical cyclone. Although their occurrence is relatively moderate compared to other tropical regions (accounting for only 5% of global cyclones), the impact of these events is major, accounting for 50% of the victims recorded worldwide. This is due to the very low topography of the delta above sea level (less than 5 meters), high storm surge induced water level and flooding, combined with the high density of the vulnerable population. </p> <p>On one hand, the unavailability of long-term reliable water level data on a sparse tide-gauge network along the coastline has hindered the assessment of storm surge hazards. The application of hydrodynamic modelling to fill the data gap also suffers from the unavailability of a reliable long-term storm dataset over the region. The complex topography of the Bengal delta, with defence structures, and a dense network of rivers presents another modelling challenge. Finally, the interaction of tide, surge and wave further complicate the numerical complexity, needing a coupled modelling framework. </p> <p>Thanks to advancements made to acquire high-quality regional nearshore bathymetry and topography (Krien et al. 2016, Khan et al. 2019), as well as coupled storm surge modelling (Krien et al. 2017, Khan et al. 2021), the tidal and storm surge dynamics over the Bengal delta is now well captured by recent high-resolution coupled SCHISM-WWM Bay of Bengal model (Khan et al. 2021). To estimate the risk of storm surge and associated flooding across the Bengal delta, we have integrated the wave-coupled hydrodynamic model of Khan et al. (2021) for a large ensemble (~3600 cyclones, ~5000 years of storm activity) of synthetic cyclones generated through the statistical-deterministic method of Emanuel (2006). Our storm and surge ensemble covers the whole range of natural variability of storm frequency, size, intensity and track location, with a dense spatial distribution. The interactions among the tide, surge, and waves are modelled explicitly at high spatial resolution. The storm surge-induced water level at various return periods, up to 500 years, is then determined at high spatial resolution (250m at the coast) using a ranking-based technique.</p> <p>The dataset distributed here represents the storm surge water level estimate (e.g. total water level from the tide, surge, and wave computed dynamically through the model) at 25 to 500 year return period (25-year step). The corresponding variable in the self-describing netCDF data file is 'maxelev'. The estimated storm surge water level values are interpolated in a 30" (~1km at the equator) structured grid over the Bengal delta from the original unstructured-grid model outputs (250m resolution at the coast). </p> <p>This dataset is a part of a manuscript, currently being submitted to Natural Hazards and Earth System Sciences (https://nhess.copernicus.org/). Please cite the original paper, along with the dataset if used in your work as - Khan, M. J. U., Durand, F., Emanuel, K., Krien, Y., Testut, L., and Islam, A. K. M. S.: Storm surge hazard over Bengal delta: A probabilistic-deterministic modelling approach, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2021-329, in review, 2021.</p>
MetaFunc Databases: nr-go database
<p>MetaFunc is a computational pipeline that can take input reads and pass it through a pipeline that will then analyse host genes from the reads on one side, and microbiome taxonomies and gene ontology annotations on the other, and finally allowing for microbe-host gene correlations. This dataset contains databases used for analysing the microbiome component of the pipeline. Full description of the pipeline can be found at https://metafunc.readthedocs.io/en/latest/#.</p>
MetaFunc Databases: Kaiju database
<p>MetaFunc is a computational pipeline that can take input reads and pass it through a pipeline that will then analyse host genes from the reads on one side, and microbiome taxonomies and gene ontology annotations on the other, and finally allowing for microbe-host gene correlations. This dataset contains databases used for analysing the microbiome component of the pipeline. Full description of the pipeline can be found at https://metafunc.readthedocs.io/en/latest/#.</p>
PsPM-TC: SCR, ECG, EMG and respiration measurements in a discriminant trace fear conditioning task with visual CS and electrical US.
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG) and respiration measurements. Also included are CS and US information, keypress responses and keypress response times from 18 healthy unmedicated participants (8 males and 10 females aged 23.89+/-2.52 years) participating in a classical (Pavlovian) discriminant trace fear conditioning task. CS were a red and a blue rectangle presented for 3 seconds. US consisted of 0.5 s square electric pulses with 0.2 ms duration and 10 Hz frequency. SOA between the CS onset and US was 4 s. The ITI was randomly determined on each trial to be 7, 9, or 11 s.</p>
PsPM-VC7B: SCR and PSR measurements in a delay fear conditioning task with visual CS and electrical US.
<p>This dataset includes pupil size response (PSR) and skin conductance response (SCR) measurements. Also included are CS and US information, keypress responses, keypress response times, key correctness and shock ratings for each of 21 healthy unmedicated participants (6 males and 15 females aged 27.9+/-5.5 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. Four sets of CS+/CS- were used. Simple CS consisted of Gabor patches rotated to the left or to the right; complex CS consisted of plaids created from two Gabor patches that were overlaid on each other with a 230° angle, rotated to the left or to the right. US consisted of a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants’ dominant forearm through a pin-cathode/ring-anode configuration. SOA between the CS and US is 3.5 s. The ITI is randomly determined on each trial to be 7, 9, or 11 s.</p>
PsPM-DoxMemP: SCR, ECG and respiration measurements in a delay fear conditioning task with visual CS and electrical US.
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG) and respiration measurements. Also included are CS and US information, keypress responses and keypress response times for 20 healthy unmedicated participants (7 males and 13 females aged 26.15+/-4.15 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. CS were a red and a blue rectangle. US consisted of 0.5 s square electric pulses with 0.2 ms duration and 10 Hz frequency. SOA between the CS onset and US was 3.5 s. CS and US co-terminated. The ITI was randomly determined on each trial to be 7, 9, or 11 s.</p>
PsPM-FR: SCR, ECG and respiration measurements in a delay fear conditioning task with visual CS and electrical US.
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG) and respiration measurements. Also included are CS and US information, keypress responses and keypress response times for 31 healthy unmedicated participants (09 males and 23 females aged 23.32+/-3.61 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. CS were a red and a blue rectangle. US consisted of 0.5 s square electric pulses with 0.2-ms duration and 10 Hz frequency. SOA between the CS onset and US was 3.5 s. CS and US co-terminated. During extinction phase, an auditory startle probe (ST) was delivered 3.8 s after CS onset via headphones (100 dB, 50 ms duration with 2ms on- and offset ramp). The ITI was randomly determined on each trial to be 7, 9, or 11 s.</p> <p> </p>
WageIndicator Collective Agreements Database Dataset with Full Texts and Selected Clauses
<p>Since 2012, the <a href="https://wageindicator.org/">WageIndicator Foundation</a> has maintained a <a href="https://wageindicator.org/cbadatabase">Collective Agreements Database</a>, where the texts of 1600 collective agreements (CBAs) from 61 countries and in 27 languages have been uploaded, coded and annotated. This database is a unique example at global level: collective agreements are documents containing conditions of employment that result from negotiations between independent unions and employers, and their content is often surrounded by an atmosphere of secrecy. Under the <a href="https://sshopencloud.eu/">SSHOC project</a> and with the support of the <a href="https://www.clarin.eu/">CLARIN Research Infrastructure</a>, the agreements have been manually and automatically annotated on several levels: for each agreement, the team answers a series of questions and selects the appropriate piece of text (clause) for each. </p> <p>One of the results of the collective agreements' annotation process is the dataset which is available here and includes all the clauses selected for each variable (WageIndicator_CBADatabase_Selected_Clauses). The full collective agreements' texts are stored in another dataset, also available here (WageIndicator_CBADatabase_Full_Texts_211019). A codebook is also included (210125-wageindicator-cba-codebook.pdf).</p>
PsPM-PubFe: Pupil size response in a delay fear conditioning procedure with auditory CS and electrical US.
<p>This dataset includes pupil size response (PSR), skin conductance response (SCR), electrocardiogram (ECG) and respiration measurements. Also included are CS and US information, keypress responses, keypress response times and key correctness for each of 22 healthy unmedicated participants (7 males and 15 females aged 26.4+/-5.2 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. CS consists of two sine tones with constant frequency (220 Hz or 440 Hz, 50-ms onset and offset ramp). US is a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. SOA betwen the CS and US is 3.5 s. The ITI is randomly determined on each trial to be 7, 9, or 11 s.</p>
PsPM-SC4B: SCR, ECG, EMG, PSR and respiration measurements in a delay fear conditioning task with auditory CS and electrical US
<p>This dataset includes pupil size response (PSR), skin conductance response (SCR), electrocardiogram (ECG), electromyogram (EMG) and respiration measurements. Also included are CS and US information, keypress responses, keypress response times, key correctness and shock ratings for each of 21 healthy unmedicated participants (10 males and 11 females aged 22.9+/-3.0 years; discrepancies to published studies are due to misprints and exclusion of a subject with incomplete data, which was excluded in all conducted studies as well as here) participating in a classical (Pavlovian) discriminant delay fear conditioning task. Two pairs of CS+ and CS-, either complex or simple, were delivered with headphones (HD518, Sennheiser, Wedemark-Wennebostel, Germany) at about 68 dB. Complex stimuli were a sequence of four rising (400 to 800 Hz) or falling (800 to 400 Hz) sounds lasting 1 s each. Simple stimuli were tones with constant frequency (400 or 800 Hz) presented for 4 s. US consisted of square electric pulses with 0.2-ms duration and 10 Hz frequency, resulting in a total US duration of 0.5 s. SOA betwen the CS and US was 3.5 s. The ITI was randomly determined on each trial to be 7, 9, or 11 s.</p>
Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal
<p>These data are supplements for the calculations of the methods from the article "Alignment of scanning lidars in offshore wind farms".<br> The data was used to produce the results from the publication and is intended to be used here as sample data for illustrative purposes.</p>
Gravity Spy Machine Learning Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b
<p>This data set contains all classifications that the Gravity Spy Machine Learning model for LIGO glitches from the first three observing runs (<a href="https://doi.org/10.7935/K57P8W9D">O1</a>, <a href="https://doi.org/10.7935/CA75-FM95">O2</a> and O3, where O3 is split into <a href="https://doi.org/10.7935/nfnt-hm34">O3a</a> and <a href="https://doi.org/10.7935/pr1e-j706">O3b</a>). Gravity Spy classified all noise events identified by the <a href="https://doi.org/10.1016/j.softx.2020.100620">Omicron trigger pipeline</a> in which Omicron identified that the signal-to-noise ratio was above 7.5 and the peak frequency of the noise event was between 10 Hz and 2048 Hz. To classify noise events, Gravity Spy made <a href="https://en.wikipedia.org/wiki/Constant-Q_transform">Omega scans</a> of every glitch consisting of 4 different durations, which helps capture the morphology of noise events that are both short and long in duration.</p> <p>There are <a href="https://doi.org/10.1088/1361-6382/aa5cea">22 classes</a> used for O1 and O2 data (including No_Glitch and None_of_the_Above), while there are <a href="https://doi.org/10.1088/1361-6382/ac1ccb">two additional classes</a> used to classify O3 data (while None_of_the_Above was removed).</p> <p>For O1 and O2, the glitch classes were: 1080Lines, 1400Ripples, Air_Compressor, Blip, Chirp, Extremely_Loud, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line, Whistle</p> <p>For O3, the glitch classes were: 1080Lines, 1400Ripples, Air_Compressor, Blip, <strong>Blip_Low_Frequency</strong>, Chirp, Extremely_Loud, <strong>Fast_Scattering</strong>, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line, Whistle</p> <p>The data set is described in <a href="https://doi.org/10.1088/1361-6382/acb633"><strong>Glanzer </strong><em>et al</em><strong>. (2023)</strong></a>, which we ask to be cited in any publications using this data release. Example code using the data can be found in this <a href="https://colab.research.google.com/drive/19q_lItODPk7qw_sohlHyWPnAbY0FZyt8?usp=sharing"><strong>Colab notebook</strong></a>.</p> <p>If you would like to download the Omega scans associated with each glitch, then you can use the gravitational-wave data-analysis tool <a href="https://gwpy.github.io/docs/stable/">GWpy</a>. If you would like to use this tool, please install anaconda if you have not already and create a virtual environment using the following command</p> <pre><code class="language-bash">conda create --name gravityspy-py38 -c conda-forge python=3.8 gwpy pandas psycopg2 sqlalchemy</code></pre> <p>After downloading one of the CSV files for a specific era and interferometer, please run the following Python script if you would like to download the data associated with the metadata in the CSV file. We recommend not trying to download too many images at one time. For example, the script below will read data on Hanford glitches from O2 that were classified by Gravity Spy and filter for only glitches that were labelled as Blips with 90% confidence or higher, and then download the first 4 rows of the filtered table.</p> <pre><code class="language-python">from gwpy.table import GravitySpyTable H1_O2 = GravitySpyTable.read('H1_O2.csv') H1_O2[(H1_O2["ml_label"] == "Blip") & (H1_O2["ml_confidence"] > 0.9)] H1_O2[0:4].download(nproc=1)</code></pre> <p>Each of the columns in the CSV files are taken from various different inputs: </p> <p>[‘event_time’, ‘ifo’, ‘peak_time’, ‘peak_time_ns’, ‘start_time’, ‘start_time_ns’, ‘duration’, ‘peak_frequency’, ‘central_freq’, ‘bandwidth’, ‘channel’, ‘amplitude’, ‘snr’, ‘q_value’] contain metadata about the signal from the <a href="https://virgo.docs.ligo.org/virgoapp/Omicron/">Omicron pipeline</a>. </p> <p>[‘gravityspy_id’] is the unique identifier for each glitch in the dataset. </p> <p>[‘1400Ripples’, ‘1080Lines’, ‘Air_Compressor’, ‘Blip’, ‘Chirp’, ‘Extremely_Loud’, ‘Helix’, ‘Koi_Fish’, ‘Light_Modulation’, ‘Low_Frequency_Burst’, ‘Low_Frequency_Lines’, ‘No_Glitch’, ‘None_of_the_Above’, ‘Paired_Doves’, ‘Power_Line’, ‘Repeating_Blips’, ‘Scattered_Light’, ‘Scratchy’, ‘Tomte’, ‘Violin_Mode’, ‘Wandering_Line’, ‘Whistle’] contain the machine learning confidence for a glitch being in a particular Gravity Spy class (the confidence in all these columns should sum to unity). These use the original 22 classes in all cases.</p> <p>[‘ml_label’, ‘ml_confidence’] provide the machine-learning predicted label for each glitch, and the machine learning confidence in its classification. </p> <p>[‘url1’, ‘url2’, ‘url3’, ‘url4’] are the links to the publicly-available <a href="https://gwdetchar.readthedocs.io/en/stable/omega/">Omega scans</a> for each glitch. ‘url1’ shows the glitch for a duration of 0.5 seconds, ‘url2’ for 1 seconds, ‘url3’ for 2 seconds, and ‘url4’ for 4 seconds.</p> <p>For the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see <a href="https://zenodo.org/record/1486046#.YZfcar3MJqs">Gravity Spy Training Set</a> on Zenodo. </p> <p><br> For detailed information on the training set used for the original Gravity Spy machine learning paper, please see <a href="https://zenodo.org/record/1476156#.YZfchL3MJqs">Machine learning for Gravity Spy: Glitch classification and dataset</a> on Zenodo.</p>
Transparent exopolymer particle (TEP) and Coomassie stainable particle (CSP) data collected from the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>TEP are operationally defined as gel particles that are retained on 0.4 µm polycarbonate filters and stained with the cationic copper phthalocyanine dye Alcian Blue 8GX at pH 2.5. CSP are gel particles retained on 0.4 µm polycarbonate filters that are stained with a solution of Coomassie Brilliant Blue G (CCB) at pH 7.4. Seawater surface samples (5 m) were collected every 6 hours from the ship’s underway pump. In addition, vertical profiles (6 depths, generally from 5 to 100-150 m) were sampled from 19 CTD casts using a SBE 911 Plus attached to a rosette of 24 12-L PVC Niskin bottles. This dataset presents TEP and CSP from seawater samples collected from the ship’s underway pump and CTDs. Samples were collected around the Southern Ocean on the R/V Akademik Tryoshnikov in the austral summer of 2016/2017, as part of the Antarctic Circumnavigation Expedition (ACE).</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_seawater_csp.csv, data file, comma-separated values</li> <li>ace_seawater_tep.csv, data file, comma-separated values</li> <li>data_file_header_csp.txt, metadata, text</li> <li>data_file_header_tep.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This TEP and CSP dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Concentration of gaseous sulfuric acid measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>The authors would highly appreciate being contacted if the data is used for any purpose.</p> <p>We measured sulfuric acid concentration in the gas phase with a nitrate chemical ionization mass spectrometer (we used an APi-HTOF mass spectrometer produced by Tofwerk AG coupled with a Chemical ionization inlet A70 produced by Airmodus). Sulfuric acid is detected in the mass spectrometer either as a deprotonated ion or as a cluster with the reagent ion (NO3-). The concentration is calculated as the area of these two peaks normalized to the concentration of the reagent ions (monomer, dimer and trimer) and multiplied by a calibration factor that was experimentally derived at Paul Scherrer Institute in the summer 2017, after the campaign. In the atmosphere, sulfuric acid is produced by the oxidation of sulfur dioxide that could have both a natural or anthropogenic origin (e.g. phytoplankton emission or fossil fuel combustion). Sulfuric acid in the atmosphere is responsible for a very large fraction of the newly formed particles, therefore it is important to have accurate measurements of this vapour in remote places like the Southern Ocean to better understand secondary aerosol sources and processes.</p> <p>The data have been cleaned from the influence of the exhaust of the research vessel. Temporal coverage is from January 22, 2017 to March 19, 2017. There are no data for the first leg of the expedition because the instrument was not on the ship. The instrument was operated during leg 4 but data has not been processed yet as they require additional work due to some instrumental issues that affects their quality. Data were collected with one-second time resolution but integrated to five minutes to increase the signal to noise ratio.</p> <p>Concentrations are reported as molecules per cubic centimeter. The lower limit of detection is in the ppq (part per quadrillion) range but was not determined exactly as it varies with the integration time and background noise.</p> <p><strong>Dataset contents</strong></p> <ul> <li>gas_sulfuric_acid_concentration_data.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>change_log.txt, metadata, text format</li> </ul> <p>Data that were missing or bad because of instrumental problems were simply removed from the file (no entry).</p> <p><strong>Change log</strong></p> <p>v1.1 - data files updated</p> <ul> <li>re-analysis of the raw data with an improved mass spectra peak fitting routine, a different calibration factor was also applied to the data following a more detailed analysis of the calibration experiment</li> <li>use updated, corrected GPS data</li> </ul> <p>v1.0 - initial release of dataset</p>
Concentration of gaseous methanesulfonic acid measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>The authors would highly appreciate being contacted if the data is used for any purpose.</p> <p>We measured methanesulfonic acid concentration in the gas phase with a nitrate chemical ionization mass spectrometer (we used an APi-HTOF mass spectrometer produced by Tofwerk AG coupled with a Chemical ionization inlet A70 produced by Airmodus). Methanesulfonic acid is detected in the mass spectrometer either as a deprotonated ion or as a cluster with the reagent ion (NO3-). The concentration is calculated as the area of these two peaks normalized to the concentration of the reagent ions (monomer, dimer and trimer) and multiplied by a calibration factor that was experimentally derived at Paul Scherrer Institute in the summer 2017, after the campaign.</p> <p>In the atmosphere, methanesulfonic acid is produced by the oxidation of dimethyl sulfide that is mostly emitted in the atmosphere by phytoplankton. Methanesulfonic acid can contribute to the growth of aerosol particles by condensation or aqueous phase processing in clouds. The data have been cleaned from the influence of the exhaust of the research vessel. Temporal coverage is from 22 January 2017 to 19 March 2017. There are no data for the first leg of the expedition because the instrument was not on the ship. The instrument was operated during leg 4 but data has not been processed yet as they require additional work due to some instrumental issues that affects their quality. Data were collected with one-second time resolution but integrated to five minutes to increase the signal-to-noise ratio.</p> <p>Concentrations are reported as molecules per cubic centimeter. The lower limit of detection is in the ppq (part per quadrillion) range but was not determined exactly as it varies with the integration time and background noise.</p> <p><strong>Dataset contents</strong></p> <ul> <li>gas_methanesulfonic_acid_concentration_data.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>change_log.txt, metadata, text format</li> </ul> <p>There are no null values in the file. Data that were missing or bad because of instrumental problems were simply removed from the file (no entry).</p> <p><strong>Change log</strong></p> <p>v1.1 - data files updated</p> <ul> <li>re-analysis of the raw data with an improved mass spectra peak fitting routine, a different calibration factor was also applied to the data following a more detailed analysis of the calibration experiment</li> <li>use updated, corrected GPS data</li> </ul> <p>v1.0 - initial release of dataset</p>
The MoLLIST dataset for 16O1H
<p>The dataset is an archive of ExoMol page, https://exomol.com/data/molecules/OH/16O-1H/MoLLIST.<br>Please check the reference details according to the following description or directly from the website.<br> <strong>NB: The html description skips data which are not included in the current version for the purpose of simplicity. Please check OH_16O1H_MoLLIST.md for detailed information.</strong> <br></p> <strong>Definitions file</strong> <blockquote> <p><strong>16O-1H__MoLLIST.def</strong>[5.59 KB]<br></p> <p><strong>References:</strong><br> 1. Tennyson, J., Yurchenko, S. N., Al-Refaie, A. F., Clark, V. H. J., Chubb, K. L., Conway, E. K., Dewan, A., Gorman, M. N., Hill, C., Lynas-Gray, A. E., Mellor, T., McKemmish, L. K., Owens, A., Polyansky, O. L., Semenov, M., Somogyi, W., Tinetti, G., Upadhyay, A., Waldmann, I., Wang, Y., Wright, S., Yurchenko, O. P., "The 2020 release of the ExoMol database: molecular line lists for exoplanet and other hot atmospheres", J. Quant. Spectrosc. Rad. Transf., 255, 107228 (2020). [<a href="https://doi.org/10.1016/j.jqsrt.2020.107228">https://doi.org/10.1016/j.jqsrt.2020.107228</a>]</p> </blockquote> <strong>Spectroscopic Model</strong> <blockquote> <p><a href="https://exomol.com/models/OH/16O-1H/MoLLIST/">https://exomol.com/models/OH/16O-1H/MoLLIST/</a><br></p> </blockquote> <strong>MoLLIST: line list</strong> <p><em>Empirical Diatomic line lists MoLLIST from Bernath lab (bernath.uwaterloo.ca) in standard ExoMol format [Tennyson and Yurchenko (2016)]</em><br></p> <blockquote> <p><strong>16O-1H__MoLLIST.trans.bz2</strong>[584.47 KB]<br>MoLLIST (16O)(1H) line list: Transition file, compiled by Yixin Wang</p> <p><strong>16O-1H__MoLLIST.states.bz2</strong>[16.51 KB]<br>MoLLIST (16O)(1H) line list: States, compiled by Yixin Wang</p> <p><strong>References:</strong><br> 1. Brooke, J. S. A., Bernath, P. F., Western, C. M., Sneden, C., Afşar, M., Li, G., Gordon, I. E., "Line strengths of rovibrational and rotational transitions in the Χ2Π ground state of OH", Journal of Quantitative Spectroscopy and Radiative Transfer 138, 142-157 (2016). <a href="[http://dx.doi.org/10.1016/j.jqsrt.2015.07.021]">[http://dx.doi.org/10.1016/j.jqsrt.2015.07.021]</a>[16BrBeWe.OH]<br> 2. Yousefi, M., Bernath, P. F., Hodges, J., Masseron, T., "A new line list for the A2 S - X2 electronic transition of OH", Journal of Quantitative Spectroscopy & Radiative Transfer 217, 416-424 (2018). <a href="[https://doi.org/10.1016/j.jqsrt.2018.06.016]">[https://doi.org/10.1016/j.jqsrt.2018.06.016]</a><br> 3. Bernath, P.F., "MoLLIST: Molecular Line Lists, Intensities and Spectra", Journal of Quantitative Spectroscopy and Radiative Transfer 240, 106687 (2020). <a href="[https://doi.org/10.1016/j.jqsrt.2019.106687]">[https://doi.org/10.1016/j.jqsrt.2019.106687]</a><br></p> </blockquote> <strong>MoLLIST: partition function</strong> <p><em>Empirical Diatomic line lists MoLLIST from Bernath lab (bernath.uwaterloo.ca) in standard ExoMol format [Tennyson and Yurchenko (2016)]</em><br></p> <blockquote> <p><strong>16O-1H__MoLLIST.pf</strong>[122.07 KB]<br>MoLLIST (16O)(1H) line list: Partition function, compiled by Yixin Wang</p> <p><strong>References:</strong><br> 1. Yousefi, M., Bernath, P. F., Hodges, J., Masseron, T., "A new line list for the A2 S - X2 electronic transition of OH", Journal of Quantitative Spectroscopy & Radiative Transfer 217, 416-424 (2018). <a href="[https://doi.org/10.1016/j.jqsrt.2018.06.016]">[https://doi.org/10.1016/j.jqsrt.2018.06.016]</a><br></p> </blockquote> <strong>MoLLIST: opacity</strong> <p><em>Empirical Diatomic line lists MoLLIST from Bernath lab (bernath.uwaterloo.ca) in standard ExoMol format [Tennyson and Yurchenko (2016)]</em><br></p> <blockquote> <p><strong>16O-1H__MoLLIST.R1000_0.3-50mu.ktable.ARCiS.fits.gz</strong>[437.21 MB]<br>ARCiS k-tables at R= 1000 (0.3-50mu) in fits format (gzipped): MoLLIST (16O)(1H) line list.</p> <p><strong>16O-1H__MoLLIST.R1000_0.3-50mu.ktable.petitRADTRANS.h5</strong>[370.98 MB]<br>petitRADTRANS k-tables at R= 1000 (0.3-50mu) in HDF5 format: MoLLIST (16O)(1H) line list.</p> <p><strong>16O-1H__MoLLIST.R1000_0.3-50mu.ktable.NEMESIS.kta</strong>[232.01 MB]<br>NEMESIS k-tables at R= 1000 (0.3-50mu) in NEMESIS-kta format: MoLLIST (16O)(1H) line list.</p> <p><strong>16O-1H__MoLLIST.R15000_0.3-50mu.xsec.TauREx.h5</strong>[348.39 MB]<br>TauREx k-tables at R= 15000 (0.3-50mu) in HDF5 format: MoLLIST (16O)(1H) line list.</p> <p><strong>References:</strong><br> 1. Brooke, J. S. A., Bernath, P. F., Western, C. M., Sneden, C., Afşar, M., Li, G., Gordon, I. E., "Line strengths of rovibrational and rotational transitions in the Χ2Π ground state of OH", Journal of Quantitative Spectroscopy and Radiative Transfer 138, 142-157 (2016). <a href="[http://dx.doi.org/10.1016/j.jqsrt.2015.07.021]">[http://dx.doi.org/10.1016/j.jqsrt.2015.07.021]</a>[16BrBeWe.OH]<br> 2. Bernath, P.F., "MoLLIST: Molecular Line Lists, Intensities and Spectra", Journal of Quantitative Spectroscopy and Radiative Transfer 240, 106687 (2020). <a href="[https://doi.org/10.1016/j.jqsrt.2019.106687]">[https://doi.org/10.1016/j.jqsrt.2019.106687]</a><br> 3. Chubb, K. L., Rocchetto, M., Yurchenko, S. N., Min, M., Waldmann, I., Barstow, J. K., Molliere, P., Al-Refaie, A. F, Phillips, M. W., Tennyson, J., "The ExoMolOP database: Cross sections and k-tables for molecules of interest in high-temperature exoplanet atmospheres", Astronomy and Astrophysics 646, A21 (2020). <a href="[http://dx.doi.org/10.1051/0004-6361/202038350]">[http://dx.doi.org/10.1051/0004-6361/202038350]</a>[20ChRoYu.]<br></p> </blockquote>
The LaTY dataset for 31P1H
<p>The dataset is an archive of ExoMol page, https://exomol.com/data/molecules/PH/31P-1H/LaTY.<br>Please check the reference details according to the following description or directly from the website.<br> <strong>NB: The html description skips data which are not included in the current version for the purpose of simplicity. Please check PH_31P1H_LaTY.md for detailed information.</strong> <br></p> <strong>Definitions file</strong> <blockquote> <p><strong>31P-1H__LaTY.def</strong>[5.97 KB]<br></p> <p><strong>References:</strong><br> 1. Tennyson, J., Yurchenko, S. N., Al-Refaie, A. F., Clark, V. H. J., Chubb, K. L., Conway, E. K., Dewan, A., Gorman, M. N., Hill, C., Lynas-Gray, A. E., Mellor, T., McKemmish, L. K., Owens, A., Polyansky, O. L., Semenov, M., Somogyi, W., Tinetti, G., Upadhyay, A., Waldmann, I., Wang, Y., Wright, S., Yurchenko, O. P., "The 2020 release of the ExoMol database: molecular line lists for exoplanet and other hot atmospheres", J. Quant. Spectrosc. Rad. Transf., 255, 107228 (2020). [<a href="https://doi.org/10.1016/j.jqsrt.2020.107228">https://doi.org/10.1016/j.jqsrt.2020.107228</a>]</p> </blockquote> <strong>Spectroscopic Model</strong> <blockquote> <p><a href="https://exomol.com/models/PH/31P-1H/LaTY/">https://exomol.com/models/PH/31P-1H/LaTY/</a><br></p> </blockquote> <strong>LaTY: line list</strong> <p><em>An empirical line list for PH covering the ground and 1st exited electronic states</em><br></p> <blockquote> <p><strong>31P-1H__LaTY.states</strong>[204.91 KB]<br>LaTY (31P)(1H) line list States file.</p> <p><strong>31P-1H__LaTY.trans</strong>[3.59 MB]<br>LaTY (31P)(1H) line list Transition file.</p> <p><strong>References:</strong><br> 1. Langleben, J., Tennyson, J., Yurchenko, S. N., Bernath, P., "ExoMol line list – XXXIV. A rovibrational line list for phosphinidene (PH) in its X 3Σ− and a 1Δ electronic states", Monthly Notices of the Royal Astronomical Society 488, 2332-2342 (2019). <a href="[https://doi.org/10.1093/mnras/stz1856]">[https://doi.org/10.1093/mnras/stz1856]</a>[19LaTeYu.PH]<br></p> </blockquote> <strong>LaTY: partition function</strong> <p><em>An empirical line list for PH covering the ground and 1st exited electronic states</em><br></p> <blockquote> <p><strong>31P-1H__LaTY.pf</strong>[97.66 KB]<br>LaTY (31P)(1H) line list Partition Function.</p> <p><strong>References:</strong><br> 1. Langleben, J., Tennyson, J., Yurchenko, S. N., Bernath, P., "ExoMol line list – XXXIV. A rovibrational line list for phosphinidene (PH) in its X 3Σ− and a 1Δ electronic states", Monthly Notices of the Royal Astronomical Society 488, 2332-2342 (2019). <a href="[https://doi.org/10.1093/mnras/stz1856]">[https://doi.org/10.1093/mnras/stz1856]</a>[19LaTeYu.PH]<br></p> </blockquote> <strong>LaTY: opacity</strong> <p><em>An empirical line list for PH covering the ground and 1st exited electronic states</em><br></p> <blockquote> <p><strong>31P-1H__LaTY.R1000_0.3-50mu.ktable.ARCiS.fits.gz</strong>[420.52 MB]<br>ARCiS k-tables at R= 1000 (0.3-50mu) in fits format (gzipped): LaTY (31P)(1H) line list.</p> <p><strong>31P-1H__LaTY.R1000_0.3-50mu.ktable.petitRADTRANS.h5</strong>[370.98 MB]<br>petitRADTRANS k-tables at R= 1000 (0.3-50mu) in HDF5 format: LaTY (31P)(1H) line list.</p> <p><strong>31P-1H__LaTY.R1000_0.3-50mu.ktable.NEMESIS.kta</strong>[232.01 MB]<br>NEMESIS k-tables at R= 1000 (0.3-50mu) in NEMESIS-kta format: LaTY (31P)(1H) line list.</p> <p><strong>31P-1H__LaTY.R15000_0.3-50mu.xsec.TauREx.h5</strong>[348.39 MB]<br>TauREx k-tables at R= 15000 (0.3-50mu) in HDF5 format: LaTY (31P)(1H) line list.</p> <p><strong>References:</strong><br> 1. Langleben, J., Tennyson, J., Yurchenko, S. N., Bernath, P., "ExoMol line list – XXXIV. A rovibrational line list for phosphinidene (PH) in its X 3Σ− and a 1Δ electronic states", Monthly Notices of the Royal Astronomical Society 488, 2332-2342 (2019). <a href="[https://doi.org/10.1093/mnras/stz1856]">[https://doi.org/10.1093/mnras/stz1856]</a>[19LaTeYu.PH]<br> 2. Chubb, K. L., Rocchetto, M., Yurchenko, S. N., Min, M., Waldmann, I., Barstow, J. K., Molliere, P., Al-Refaie, A. F, Phillips, M. W., Tennyson, J., "The ExoMolOP database: Cross sections and k-tables for molecules of interest in high-temperature exoplanet atmospheres", Astronomy and Astrophysics 646, A21 (2020). <a href="[http://dx.doi.org/10.1051/0004-6361/202038350]">[http://dx.doi.org/10.1051/0004-6361/202038350]</a>[20ChRoYu.]<br></p> </blockquote>
The MoLLIST dataset for 14N1H
<p>The dataset is an archive of ExoMol page, https://exomol.com/data/molecules/NH/14N-1H/MoLLIST.<br>Please check the reference details according to the following description or directly from the website.<br> <strong>NB: The html description skips data which are not included in the current version for the purpose of simplicity. Please check NH_14N1H_MoLLIST.md for detailed information.</strong> <br></p> <strong>Definitions file</strong> <blockquote> <p><strong>14N-1H__MoLLIST.def</strong>[6.0 KB]<br></p> <p><strong>References:</strong><br> 1. Tennyson, J., Yurchenko, S. N., Al-Refaie, A. F., Clark, V. H. J., Chubb, K. L., Conway, E. K., Dewan, A., Gorman, M. N., Hill, C., Lynas-Gray, A. E., Mellor, T., McKemmish, L. K., Owens, A., Polyansky, O. L., Semenov, M., Somogyi, W., Tinetti, G., Upadhyay, A., Waldmann, I., Wang, Y., Wright, S., Yurchenko, O. P., "The 2020 release of the ExoMol database: molecular line lists for exoplanet and other hot atmospheres", J. Quant. Spectrosc. Rad. Transf., 255, 107228 (2020). [<a href="https://doi.org/10.1016/j.jqsrt.2020.107228">https://doi.org/10.1016/j.jqsrt.2020.107228</a>]</p> </blockquote> <strong>Spectroscopic Model</strong> <blockquote> <p><a href="https://exomol.com/models/NH/14N-1H/MoLLIST/">https://exomol.com/models/NH/14N-1H/MoLLIST/</a><br></p> </blockquote> <strong>MoLLIST: line list</strong> <p><em>Empirical Diatomic line lists MoLLIST from Bernath lab (bernath.uwaterloo.ca) in standard ExoMol format [Tennyson and Yurchenko (2016)]</em><br></p> <blockquote> <p><strong>14N-1H__MoLLIST.trans.bz2</strong>[236.11 KB]<br>MoLLIST (14N)(1H) line list: Transition file. The data are from Brooke et al (2015), http://dx.doi.org/10.1063/1.4923422, converted to ExoMol by Geronimo Villanueva (NASA/GSFC) Feb/2019.</p> <p><strong>14N-1H__MoLLIST.states.bz2</strong>[11.57 KB]<br>MoLLIST (14N)(1H) line list: States from Brooke et al (2015), http://dx.doi.org/10.1063/1.4923422, converted to ExoMol by Geronimo Villanueva (NASA/GSFC) Feb/2019.</p> <p><strong>14N-1H__MoLLIST.readme</strong>[2.09 KB]<br>MoLLIST's line list: readme file</p> <p><strong>References:</strong><br> 1. Brooke, J. S. A., Bernath, P. F., Western, C. M., van Hemert, M. C., Groenenboom, G. C., "Line strengths of rovibrational and rotational transitions within the X3Σ- ground state of NH", Journal of Chemical Physics 141, 54310 (2014). <a href="[http://dx.doi.org/10.1063/1.4891468]">[http://dx.doi.org/10.1063/1.4891468]</a>[14BrBeWe.NH]<br> 2. Fernando, A. M., Bernath, P. F., Hodges, J. N., Masseron, T., "A new linelist for the A3 P - X3 transition of the NH free radical", Journal of Quantitative Spectroscopy & Radiative Transfer 217, 29-34 (2018). <a href="[https://doi.org/10.1016/j.jqsrt.2018.05.021]">[https://doi.org/10.1016/j.jqsrt.2018.05.021]</a><br> 3. Brooke, J. S. A., Bernath, P. F., Western, C. M., "Note: Improved line strengths of rovibrational and rotational transitions within the X 3Σ− ground state of NH", The Journal of Chemical Physics 143, 026101/1-3 (2015). <a href="[https://doi.org/10.1063/1.4923422]">[https://doi.org/10.1063/1.4923422]</a>[15BrBeWe.NH]<br> 4. Bernath, P.F., "MoLLIST: Molecular Line Lists, Intensities and Spectra", Journal of Quantitative Spectroscopy and Radiative Transfer 240, 106687 (2020). <a href="[https://doi.org/10.1016/j.jqsrt.2019.106687]">[https://doi.org/10.1016/j.jqsrt.2019.106687]</a><br></p> </blockquote> <strong>MoLLIST: partition function</strong> <p><em>Empirical Diatomic line lists MoLLIST from Bernath lab (bernath.uwaterloo.ca) in standard ExoMol format [Tennyson and Yurchenko (2016)]</em><br></p> <blockquote> <p><strong>14N-1H__MoLLIST.pf</strong>[122.07 KB]<br>MoLLIST (14N)(1H) line list: partition function, data are from Brooke et al (2015), http://dx.doi.org/10.1063/1.4923422, compiled by Geronimo Villanueva (NASA/GSFC) Feb/2019.</p> <p><strong>References:</strong><br> 1. Sauval, A. J., Tatum, J. B., "A set of partition functions and equilibrium constants for 300 diatomic molecules of astrophysical interest", Astrophysical Journal Supplement Series 56, 193-209 (1984). <a href="[http://dx.doi.org/10.1086/190980]">[http://dx.doi.org/10.1086/190980]</a>[84SaTaxx.FeH]<br></p> </blockquote> <strong>MoLLIST: opacity</strong> <p><em>Empirical Diatomic line lists MoLLIST from Bernath lab (bernath.uwaterloo.ca) in standard ExoMol format [Tennyson and Yurchenko (2016)]</em><br></p> <blockquote> <p><strong>14N-1H__MoLLIST.R1000_0.3-50mu.ktable.ARCiS.fits.gz</strong>[351.22 MB]<br>ARCiS k-tables at R= 1000 (0.3-50mu) in fits format (gzipped): MoLLIST (14N)(1H) line list.</p> <p><strong>14N-1H__MoLLIST.R1000_0.3-50mu.ktable.petitRADTRANS.h5</strong>[370.98 MB]<br>petitRADTRANS k-tables at R= 1000 (0.3-50mu) in HDF5 format: MoLLIST (14N)(1H) line list.</p> <p><strong>14N-1H__MoLLIST.R1000_0.3-50mu.ktable.NEMESIS.kta</strong>[232.01 MB]<br>NEMESIS k-tables at R= 1000 (0.3-50mu) in NEMESIS-kta format: MoLLIST (14N)(1H) line list.</p> <p><strong>14N-1H__MoLLIST.R15000_0.3-50mu.xsec.TauREx.h5</strong>[348.39 MB]<br>TauREx k-tables at R= 15000 (0.3-50mu) in HDF5 format: MoLLIST (14N)(1H) line list.</p> <p><strong>References:</strong><br> 1. Brooke, J. S. A., Bernath, P. F., Western, C. M., van Hemert, M. C., Groenenboom, G. C., "Line strengths of rovibrational and rotational transitions within the X3Σ- ground state of NH", Journal of Chemical Physics 141, 54310 (2014). <a href="[http://dx.doi.org/10.1063/1.4891468]">[http://dx.doi.org/10.1063/1.4891468]</a>[14BrBeWe.NH]<br> 2. Brooke, J. S. A., Bernath, P. F., Western, C. M., "Note: Improved line strengths of rovibrational and rotational transitions within the X 3Σ− ground state of NH", The Journal of Chemical Physics 143, 026101/1-3 (2015). <a href="[https://doi.org/10.1063/1.4923422]">[https://doi.org/10.1063/1.4923422]</a>[15BrBeWe.NH]<br> 3. Bernath, P.F., "MoLLIST: Molecular Line Lists, Intensities and Spectra", Journal of Quantitative Spectroscopy and Radiative Transfer 240, 106687 (2020). <a href="[https://doi.org/10.1016/j.jqsrt.2019.106687]">[https://doi.org/10.1016/j.jqsrt.2019.106687]</a><br> 4. Chubb, K. L., Rocchetto, M., Yurchenko, S. N., Min, M., Waldmann, I., Barstow, J. K., Molliere, P., Al-Refaie, A. F, Phillips, M. W., Tennyson, J., "The ExoMolOP database: Cross sections and k-tables for molecules of interest in high-temperature exoplanet atmospheres", Astronomy and Astrophysics 646, A21 (2020). <a href="[http://dx.doi.org/10.1051/0004-6361/202038350]">[http://dx.doi.org/10.1051/0004-6361/202038350]</a>[20ChRoYu.]<br></p> </blockquote>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.