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Data and results of the example used in the SI-Hg D1 protocol for the SI-traceable calibration of elemental mercury (Hg0) gas generators used in the field

During the SI-Hg project a metrological traceable protocol for the calibration of mercury gas generators used in the field was developed and validated. The SI-Hg calibration protocol specifies the procedures for establishing traceability to the SI units for the quantitative output of elemental mercury generators that are employed in regulatory applications for emission monitoring or testing. This protocol provides methods for<ul><li>the experimental procedures to compare the output of elemental mercury gas generators</li><li>the data processing for determination of mercury concentration and the expanded uncertainty of the mercury concentration obtained from the elemental mercury gas generator.</li></ul>In the protocol examples are given to explain the data processing, determining the mercury concentration and corresponding uncertainty. In this repository the raw data and results used for the example calculated with the data processing script can be found.

opencc-by-4.0Apr 2024View details →

JR100 Expedition 379T Site J1002 beryllium isotope, XRF element count and carbon isotope data sets

<h2>JR100 Expedition 379T Site J1002 beryllium isotope, XRF element count and carbon isotope data sets (Finalised 8th of April 2024)</h2> <h3>How to cite these data:</h3> The full data were published in Sproson et al., 2024. Sproson AD, Yokoyama Y, Miyairi Y, Aze T, Clementi VJ, Riechelson H, Bova SC, Rosenthal Y, Childress LB & Expedition 379T Scientists. Near-synchronous Northern Hemisphere and Patagonian ice sheet variation over the last glacial cycle. Nature Geoscience <a href="https://doi.org/10.1038/s41561-024-01436-y">https://doi.org/10.1038/s41561-024-01436-y</a> (2024). <h3>Files:</h3> Supplementary Table 1: Multiple linear regression results between 10Be/9Be ratios and sedimentation rate, K/Ca, Fe/Ca, Al/Ti (this study), Green/Blue (Li et al., 2022), and Global Mean Sea Level (Lambeck et al., 2014). The multiple linear regression was calculated using the MATLAB(R) function “regress”. Supplementary Table 2:  Age-depth model and beryllium isotope measurements for Site J1002. The age-depth model was calculated from radiocarbon dates and oxygen isotope stratigraphy (Li et al., 2022) using the BIGMACS modelling routine (Lee et al., 2022). Beryllium-9 and beryllium-10 were measured by Adam D. Sproson by HR-ICP-MS and AMS at the Atmosphere and Ocean Research Institute (Sproson et al., 2021) and University of Tokyo (Matsuzaki et al., 2007), respectively. 10Be/9Be* ratios were corrected for 10Be paleo-production following von Blanckenburg et al. (2015).  Supplementary Table 3: X-ray Fluorescence Ti, K, Fe, Ca, and Al element counts per second for Site J1002 measured at the Lamont-Doherty Earth Observatory by Vincent J. Clementi. Supplementary Table 4: Carbon isotope measurements for the benthic foraminifera, U. peregrina, measured at Rutgers University by Vincent J. Clementi. <h3>Format:</h3> Depth (m CCSF-A) = core composite depth below seafloor. Calendar age (kyr BP) = age in thousand years before present. [10Be]reac, [9Be]reac = the concentration of 10Be and 9Be in the reactive phase of marine sediments.  Sample ID = expedition sample designation specifying hole (e.g., A), core number (e.g., 1), type (i.e., H), section number (e.g., 1), and then section half (i.e., W). σ = standard deviation. <h3>References:</h3> Lambeck K, Rouby H, Purcell A, Sun Y, Sambridge M. Sea level and global ice volumes from the Last Glacial Maximum to the Holocene. Proceedings of the National Academy of Sciences. 2014;111(43):15296-15303.  Lee T, Rand D, Lisiecki LE, Gebbie G, Lawrence CE. Bayesian age models and stacks: Combining age inferences from radiocarbon and benthic δ18O stratigraphic alignment. EGUsphere. 2022;2022:1-29. Li C, Clementi VJ, Bova SC, et al. The sediment green‐blue color ratio as a proxy for biogenic silica productivity along the Chilean Margin. Geochemistry, Geophysics, Geosystems. 2022:e2022GC010350.  Matsuzaki H, Nakano C, Tsuchiya Y, et al. Multi-nuclide AMS performances at MALT. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms. 2007;259(1):36-40.  Sproson AD, Aze T, Behrens B, Yokoyama Y. Initial measurement of beryllium‐9 using high‐resolution inductively coupled plasma mass spectrometry allows for more precise applications of the beryllium isotope system within the Earth Sciences. Rapid Communications in Mass Spectrometry. 2021;35(8):e9059.  Von Blanckenburg F, Bouchez J, Ibarra DE, Maher K. Stable runoff and weathering fluxes into the oceans over Quaternary climate cycles. Nature Geoscience. 2015;8(7):538-542.

opencc-by-4.0Feb 2022View details →

Detrital Carbonate Minerals in Earth's Element Cycles (Data & Scripts)

Earth surface conditions, including climate and sea level, are largely controlled by the cycling of carbon and biogeochemically coupled elements. However, most elemental budgets cannot be consentaneously balanced for the present state. Here, we investigate the possible role of riverine carbonate minerals in biogeochemical cycles. We derive individual river basin export fluxes, the global export flux to the ocean and its reduction by human influence, utilizing state-of-the-art regression techniques and published global-scale datasets. Results point to a significance of riverine detrital carbonates for the global mass balances of carbon, calcium, alkalinity and strontium, which might help solving this long-standing problem.       [Plain Language summary from: Müller et al. 2022, Detrital Carbonates in Earth's Element Cycles, GBC, <a href="https://doi.org/10.1002/essoar.10508409.1">https://doi.org/10.1002/essoar.10508409.1</a> ]. Here data and scripts on which these investigations are based can be accessed.   Funding: This work was carried out under the umbrella of the Netherlands Earth System Science Centre (NESSC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie, grant agreement No 847504. Funding was also provided by BMBF-project PALMOD (Ref 01LP1506C) through the German Federal Ministry of Education and Research (BMBF) as Research for Sustainability inititative (FONA). AS thanks the European Research Council for Consolidator Grant 771497.

opencc-by-4.0Sep 2022View details →

gyroScatterEFF data: 590k element cyclone mesh

Arrays and constants needed to run the gyroScatterEFF kernel on a single GPU without XGCm. Created with https://github.com/SCOREC/xgcm/tree/cws/extractGyroScatter @ <a href="https://github.com/SCOREC/xgcm/commit/5a8f0b47262c2466085e8fca983add502eef64b8">5a8f0b4</a>.  The build on OLCF Summit used the following scripts: https://github.com/zhangchonglin/XGCm_build_scripts/tree/792c9378b6a12ca141811cbd788c6d6542e59938/Summit_gcc11.2.0_cuda11.5.2 And the Cyclone 590k mesh case here: https://github.com/SCOREC/xgc1_data/tree/0d32f7f1354346a37df1fac517fa1f4fceff2015/Cyclone_ITG/Cyclone_ITG_deltaf_590kmesh  At the time of dataset publication, the XGCm and xgc1_data repos were private.

opencc-by-4.0Jan 2024View details →

Data and code for figures: Temperature dependence of microwave losses in lumped-element resonators made from superconducting nanowires with high kinetic inductance

This directory contains the datasets and code (if applicable) for generating the figures in the research article: Temperature dependence of microwave losses in lumped-element resonators made from superconducting nanowires with high kinetic inductance, Supercond. Sci. Technol. 37 075013

opencc-by-4.0Oct 2019View details →

Data and Code for "Cell Type-specific Genome Scans of DNA Methylation Diversity Indicate an Important Role for Transposable Elements"

This is a release of the gitlab repository "meta-methylome" (https://gitlab.com/okartal/meta-methylome.git) that, in addition to the code, also contains the resulting genomic data. Extract the directory on the command line using <pre><code class="language-bash">$ tar -xhzvf meta-methylome.tar.gz</code></pre> to preserve the symbolic links.

opencc-by-4.0Jul 2024View details →

Data accompanying the manuscript "Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake", published in Limnology and Oceanography (doi: 10.1002/lno.12687)

CTD and geochemical data accompanying the publication: Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake in Limnology & Oceanography (doi: 10.1002/lno.12687).

opencc-by-4.0Jul 2021View details →

Generation of transcriptional novelty by transposable element insertions in Arabidopsis, Genome Sequencing and eccDNA Data

Raw Illumina sequencing data from the Manuscript entitled "Generation of transcriptional novelty by transposable element insertions in Arabidopsis" A. Illumina genome sequencing reads of Arabidopsis control and hcLines that contain novel transposable element insertions. To identify the genomic position of the new ONSEN insertions, the extracted DNA of the 11 selected lines (nine lines with new insertions and two control lines) was sent to BGI, Hong-Kong for Illumina paired-end 150 bp sequencing, aiming for a minimum of 20X sequencing coverage. Quality control of the raw reads was done using FastQC (Andrews S. (2010). FastQC: a quality control tool for high throughput sequence data. Available online at: <a href="http://www.bioinformatics.babraham.ac.uk/projects/fastqc">http://www.bioinformatics.babraham.ac.uk/projects/fastqc</a>) and trimming/clipping was done using Trimmomatic with parameters ILLUMINACLIP: TruSeq3:2:30:10 LEADING:20 TRAILING:20 SLIDINGWINDOW:4:20 and MINLEN:36. Quality of the reads was deemed excellent and no further actions were taken. Samples identifications: genome_hcLineX with "_1" indicating the forward and "_2" the reverse reads. B. Illumina eccDNA sequencing of Arabidopsis control and hcLines following stress treatments Extrachromosomal circular DNA was prepared and sequenced as follows: twenty plants from each petri dish were pooled separately and DNA was extracted using the CTAB method (<a href="https://dx.doi.org/10.17504/protocols.io.quidwue">dx.doi.org/10.17504/protocols.io.quidwue</a>). Following the mobilome-seq method described in (Lanciano et al., 2017), for all samples, we digested linear DNA from 2 µg of total DNA for 17 hours at 37oC using 10 U of PlasmidSafe (LubioScience cat# E3101K), followed by enzyme denaturation (30 mins at 70oC). Digested DNA was precipitated with isopropanol supplemented with 1 µg of GlycoBlue coprecipitant (Fisher Scientific cat# 10391565). Circular DNA was then amplified through rolling circle amplification (RCA) with the Illustra TempliPhi kit (GE Healthcare cat# 25-6400-10), following the manufacturer recommendation and leaving the reaction for 16h at 30oC. DNA was once again precipitated with isopropanol and sent for Illumina paired end 150 bp sequencing at BGI, Hong Kong.  Samples identification:  eccDNA_A.thaliana_ctrl: control reads eccDNA_A.thaliana_HS: heat stressed plants reads eccDNA_A.thaliana_AZ_HS: reads of alpha-amanitin, zebularine and heat-stressed plants "R1" indicates forward and "R2" reverse reads.

opencc-by-4.0Sep 2021View details →

PQRI Elemental Impurity Interlaboratory Study Raw Data

The pharmaceutical industry recently implemented a new paradign in drug product elemental impurity (EI) analysis in ICH Q3D Guidelines and USP General Chapters <232> and <233>, which allow for EI analysis by inductively coupled plasma-mass spectrometry (ICP-MS) and similar techniques. To date, there have been few systematic evaluations of laboratory performance on EI analysis in drug products due to a lack of standardized samples and ongoing efforts to adopt best practices. We organized an interlaboratory study to provide a data-driven way to address key technical challenges faced by laboratories during the implementation of EI regulations, including laboratory equipment, interference correction strategies, and the method of calculating the final concentrations. The data contained in this file includes the analytical results and characteristics for all participant laboratories in a format readable and analyzable by R. A manuscript describing the analysis of this data is in preparation, with a target submission date of October 2021.

opencc-by-4.0Nov 2020View details →

New fault slip distribution for the 2010 Mw 7.2 El Mayor Cucapah earthquake based on realistic 3D finite element inversions of coseismic displacements using space geodetic data

The .csv files included in this repository contain the data used in the numerical model as input, while the .txt file is the output (slip on a regular grid of points on the fault planes from the joint inversion of the geodetic datasets.

openmit-licenseFeb 2021View details →

Ultrasonic guided-wave experiment data for manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textilecomposites through multiscale wave and finite element modelling'

This data set contains the ultrasonic guided wave signals (signal amplitudes as a function of time for different sensors) that were generated and recorded using the transducers and controlling instrument in support of the manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'. The controlling software was programmed in MATLAB and that the attached files are in accordance to the .mat file format. The file names follow the notation described below with an example: S1_10kHz_2cyc (illustrated with an example): S1 represents the number of sensors; 10kHz represents the exciting frequency; 2cyc represents the cycle number of input waveform. Details on the experiment setup are provided within an extra file ('Readme' file).

openmit-licenseFeb 2021View details →

Experimental data in support of manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'

dryad40/100

Data from: DCDC2 READ1 regulatory element: how temporal processing differences may shape language

Classic linguistic theory ascribes language change and diversity to population migrations, conquests, and geographic isolation, with the assumption that human populations have equivalent language processing abilities. We hypothesize that spectral and temporal characteristics make some consonant manners vulnerable to differences in temporal precision associated with specific population allele frequencies. To test this hypothesis, we modeled association between RU1-1 alleles of DCDC2 and manner of articulation in 51 populations spanning five continents, and adjusting for geographic proximity, genetic and linguistic relatedness. RU1-1 alleles, acting through increased expression of DCDC2, appear to increase auditory processing precision that enhances stop-consonant discrimination, favoring retention in some populations and loss by others. These findings enhance classical linguistic theories by adding a genetic dimension, which until recently, has not been considered to be a significant catalyst for language change.

opencc-zeroMay 2020View details →

Data for Tekran Model 3425 performance evaluation report for elemental mercury

During the SI-Hg performance evaluation of elemental mercury gas generators on the market three generators were tested, e.g., PSA 10.536 elemental Hg generator, bell-jar and Tekran Model 3425. Key characteristics were determined e.g.; the stabilisation period, short-term drift, precision, i.e., reproducibility and repeatability of the concentration generated, linearity, bias, sensitivity to sample gas pressure, sensitivity to surrounding temperature and sensitivity to electrical voltage. All three generators could be tested according to the calibration protocol developed within the project. The results obtained with the different gas generator clearly shows the importance of a metrological calibration. All three candidate generators show a different bias for the setpoint compared to the calibrated output. The data obtained during the performance evaluation of the Tekran Model 3425 is published in this repository. The files of the following experiments can be found here:<ul><li>m1<ul><li>Calibration Tekran mercury gas generator m1 20230612</li><li>Calibration_Tekran_m1</li></ul></li><li>m2<ul><li>Calibration Tekran mercury gas generator m2 20230619</li><li>Calibration_Tekran_m2</li></ul></li><li>m3<ul><li>Calibration Tekran mercury gas generator m3 20230626</li><li>Calibration_Tekran_m3</li></ul></li><li>m4<ul><li>Calibration Tekran mercury gas generator m4 20230629</li><li>Calibration_Tekran_m4</li></ul></li><li>short-term drift<ul><li>m2<ul><li>Calibration Tekran mercury gas generator short term drift m2</li><li>Tekran_Short_Term_M2</li></ul></li><li>m3<ul><li>Calibration Tekran mercury gas generator short term drift m3</li><li>Tekran_Short_Term_M3</li></ul></li><li>m4<ul><li>Calibration Tekran mercury gas generator short term drift m4</li><li>Tekran_Short_Term_M4</li></ul></li><li>m5<ul><li>Calibration Tekran mercury gas generator short term drift m5</li><li>Tekran_Short_Term_M5</li></ul></li></ul></li><li>stability<ul><li>Calibration Tekran mercury gas generator 20230609 stability</li></ul></li></ul>

Data for SI-Hg D2 validation report for the calibration of elemental mercury gas generators including information on repeatability, reproducibility and uncertainty evaluation at emission and ambient levels extended to the sub ng/m3 level

In deliverable 2 of the SI-Hg project the first validation results of the SI-Hg calibration protocol are reported. Within the SI-Hg project a protocol for the metrological calibration of elemental mercury gas generators used in the field was developed. For the validation the output of two different mercury gas generators was calibrated according to the protocol. As metrological reference standard the primary mercury gas standard from the Van Swinden Laboratory (VSL) was used. The measurements described in the protocol could be performed during the validation and the data was processed using a script to determine the output of the candidate generator and the uncertainty of the mercury concentration. Based on the validation measurements and data processing several improvements for the calibration protocol were identified and were used to improve the calibration protocol. In this repository data obtained during the validation is published. The files of the following comparisons between reference generator and candidate generator can be found in this repository:<ul><li>VSL vs VSL<ul><li>m1<ul><li>09022022 calibration mercury gas generator VSL vs VSL m1</li><li>VSL_vs_VSL_m1</li></ul></li><li>m2 <ul><li>05072022 calibration mercury gas generator VSL vs VSL m2</li><li>VSL_vs_VSL_m2</li></ul></li><li>m3<ul><li>07072022 calibration mercury gas generator VSL vs VSL m3</li><li>VSL_vs_VSL_m3</li></ul></li></ul></li><li>VSL vs PSA before modification<ul><li>m1<ul><li>15032022 calibration mercury gas generator VSL vs PSA fixed m1</li><li>single_point_VSL_vs_PSA_fixed_m1_4</li><li>single_point_VSL_vs_PSA_fixed_m1_6</li><li>single_point_VSL_vs_PSA_fixed_m1_8</li><li>single_point_VSL_vs_PSA_fixed_m1_12</li></ul></li><li>m2<ul><li>28032022 calibration mercury gas generator VSL vs PSA fixed m2</li><li>single_point_VSL_vs_PSA_fixed_m2_4</li><li>single_point_VSL_vs_PSA_fixed_m2_6</li><li>single_point_VSL_vs_PSA_fixed_m2_8</li><li>single_point_VSL_vs_PSA_fixed_m2_12</li></ul></li><li>m3 <ul><li>06042022 calibration mercury gas generator VSL vs PSA fixed m3</li><li>single_point_VSL_vs_PSA_fixed_m3_4</li><li>single_point_VSL_vs_PSA_fixed_m3_6</li><li>single_point_VSL_vs_PSA_fixed_m3_8</li><li>single_point_VSL_vs_PSA_fixed_m3_12</li></ul></li><li>m4 <ul><li>12042022 calibration mercury gas generator VSL vs PSA fixed m4</li><li>single_point_VSL_vs_PSA_fixed_m4_4</li><li>single_point_VSL_vs_PSA_fixed_m4_6</li><li>single_point_VSL_vs_PSA_fixed_m4_8</li><li>single_point_VSL_vs_PSA_fixed_m4_12</li></ul></li><li>less tubing <ul><li>14042022 calibration mercury gas generator VSL vs PSA fixed less tubing</li><li>single_point_VSL_vs_PSA_fixed_less_tubing</li></ul></li><li>less tubing and air as complementary gas <ul><li>19042022 calibration mercury gas generator VSL vs PSA fixed less tubing in air</li><li>single_point_VSL_vs_PSA_fixed_less_tubing_air</li></ul></li></ul></li><li>VSL vs PSA after modification<ul><li>m1 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m1 20230324</li><li>PSA_fixed_air_m1_9</li><li>PSA_fixed_air_m1_11</li><li>PSA_fixed_air_m1_14</li></ul></li><li>m2 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m2 20230327</li><li>PSA_fixed_air_m2_9</li><li>PSA_fixed_air_m2_11</li><li>PSA_fixed_air_m2_14</li></ul></li><li>m3 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m3 20230329</li><li>PSA_fixed_air_m3_9</li><li>PSA_fixed_air_m3_11</li><li>PSA_fixed_air_m3_14</li></ul></li><li>m4 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m4 20230907</li><li>PSA_fixed_air_m4_9</li><li>PSA_fixed_air_m4_11</li><li>PSA_fixed_air_m4_14</li></ul></li><li>m5 air as complemantary gas <ul><li>Calibration PSA fixed mercury gas generator air m5 20230911</li><li>PSA_fixed_air_m5_9</li><li>PSA_fixed_air_m5_11</li><li>PSA_fixed_air_m5_14</li></ul></li><li>m1 nitrogen (N2) as complementary gas<ul><li>Calibration PSA fixed mercury gas generator nitrogen m1 20230330</li><li>PSA_fixed_N2_m1_9</li><li>PSA_fixed_N2_m1_11</li><li>PSA_fixed_N2_m1_14</li></ul></li><li>m2 N2 as complementary gas <ul><li>Calibration PSA fixed mercury gas generator nitrogen m2 20230331</li><li>PSA_fixed_N2_m2_9</li><li>PSA_fixed_N2_m2_11</li><li>PSA_fixed_N2_m2_14</li></ul></li><li>m3 N2 as complementary gas <ul><li>Calibration PSA fixed mercury gas generator nitrogen m3 20230405</li><li>PSA_fixed_N2_m3_9</li><li>PSA_fixed_N2_m3_11</li><li>PSA_fixed_N2_m3_14</li></ul></li><li>measurement at TUV<ul><li>PSA_Fixed_at_TUV</li></ul></li></ul></li></ul>

Data for PSA 10.536 Elemental Hg generator performance evaluation report

During the SI-Hg performance evaluation of elemental mercury gas generators on the market three generators were tested, e.g., PSA 10.536 elemental Hg generator, bell-jar and Tekran Model 3425. Key characteristics were determined e.g.; the stabilisation period, short-term drift, precision, i.e., reproducibility and repeatability of the concentration generated, linearity, bias, sensitivity to sample gas pressure, sensitivity to surrounding temperature and sensitivity to electrical voltage. All three generators could be tested according to the calibration protocol developed within the project. The results obtained with the different gas generator clearly shows the importance of a metrological calibration. All three candidate generators show a different bias for the setpoint compared to the calibrated output. The data obtained during the performance evaluation of the PSA 10.536 elemental Hg generator is published in this repository. The files of the following experiments can be found here:<ul><li>range1 m1<ul><li>Calibration_PSA_range1_m1_20221130</li><li>multi_point_calibration_PSA_range1_m1</li></ul></li><li>range1 m2<ul><li>Calibration_PSA_range1_m2_20221209</li><li>multi_point_calibration_PSA_range1_m2</li></ul></li><li>range1 m3<ul><li>Calibration_PSA_range1_m3_20221214</li><li>multi_point_calibration_PSA_range1_m3</li></ul></li><li>range1 m4<ul><li>Calibration_PSA_range1_m4_20230915</li><li>multi_point_calibration_PSA_range1_m4</li></ul></li><li>range1 m5<ul><li>Calibration_PSA_range1_m5_20230919</li><li>multi_point_calibration_PSA_range1_m5</li></ul></li><li>range2 m1<ul><li>Calibration_PSA_range2 m1 20221006</li><li>multi_point_calibration_PSA_range2_m1</li></ul></li><li>range2 m2<ul><li>Calibration_PSA_range2 m2 20221011</li><li>multi_point_calibration_PSA_range2_m2</li></ul></li><li>range2 m3<ul><li>Calibration_PSA_range2 m3 20221012</li><li>multi_point_calibration_PSA_range2_m3</li></ul></li><li>short-term drift<ul><li>m1<ul><li>Calibration mercury gas generator range1 short term drift 20221012 deel 1</li><li>PSA_short_term_drift_M1</li></ul></li><li>m2<ul><li>Calibration mercury gas generator range1 short term drift 20221012 deel 2</li><li>PSA_short_term_drift_M2</li></ul></li><li>m3<ul><li>Calibration mercury gas generator range1 short term drift 20221012 deel 3</li><li>PSA_short_term_drift_M3</li></ul></li><li>m4<ul><li>Calibration mercury gas generator range1 short term drift 20221012 deel 4</li><li>PSA_short_term_drift_M4</li></ul></li></ul></li><li>stability<ul><li>Calibration mercury gas generator range2 stability 20220811</li><li>Calibration mercury gas generator stability 20221018</li></ul></li></ul>

IDSL.GOA - data elements and relationships

The files are used as input for the IDSL.GOA online tool to compute the gene ontology analysis enrichment statistics for a list of metabolites.