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Expanded Supplementary Table
<p>This file is an expanded version of TableS1 of Coyne et al. 2022. It includes full annotation of all citations contributing to Figures 2 and 3. Detail included about thresholding for Algicidal Activity.</p>
Thesis: Supplementary Tables and reports
<p>Contains Supplementary information for my Thesis</p> <p>Files:</p> <p>model_perfs_and_motifs_top5_annot.mht: MHTML table containing top5 motifs, B1H alignments and dataset quality annotations along with other information. Download and open in Google Chrome browser</p> <p>all_reports.tar.gz : All TF Modisco reports bundled together which are linked in the Table as a standalone resource. Contains every motif, submotif , motif hit distribution with respect to the peak summits and alignments to B1H recognition-code</p> <p>model_archive.tar.gz : All models</p> <p>models_info.tsv: Maps model name to ENCODE ids and dataset related information.</p> <p>Datasets used other than ENCODE use these keys: </p> <p>HughesNB:</p> <p>Najafabadi, Hamed S., et al. "C2H2 zinc finger proteins greatly expand the human regulatory lexicon." <em>Nature biotechnology</em> 33.5 (2015): 555-562.</p> <p> </p> <p>HughesGR:</p> <p>Schmitges, Frank W., et al. "Multiparameter functional diversity of human C2H2 zinc finger proteins." <em>Genome research</em> 26.12 (2016): 1742-1752.</p> <p>ChipExo:</p> <p>Imbeault, Michaël, Pierre-Yves Helleboid, and Didier Trono. "KRAB zinc-finger proteins contribute to the evolution of gene regulatory networks." <em>Nature</em> 543.7646 (2017): 550-554.</p> <p> </p> <p> </p> <p> </p>
IODP Expedition 382: Supplementary Tables for "Episodes of early Pleistocene West Antarctic Ice Sheet retreat recorded by Iceberg Alley sediments"
<p>IODP Expedition 382: Supplementary Tables for "Episodes of early Pleistocene West Antarctic Ice Sheet retreat recorded by Iceberg Alley sediments"</p> <p>Includes SEM QEMSCAN® and <sup>40</sup>Ar/<sup>39</sup>Ar data for International Ocean Discovery Program (IODP) Expedition 382 Site U1538. Also includes a movie of a 3D-volume realization of an iceberg-rafted sedimentary layer from this site based on non-destructive X-ray microtomography imaging.</p> <p> </p> <p><strong>Data Set Captions:</strong></p> <p> </p> <p><strong>Data Set S1. </strong>Modal mineralogy data based on QEMSCAN® analyses, which infer minerals from chemistry. The mineral name assignations for each chemistry-based category stated in this table are aided by visual (microscope-based) inspection of the raw sieved samples.</p> <p><strong>Data Set S2. </strong>Mineral association data based on QEMSCAN® analyses. Please read data in columns, mineral against mineral (down then across left). These data define what touches what in the sample and is displayed as a percentage. Association refers to adjacency. Two minerals are “associated” if a pixel of one of the minerals occurs adjacent to a pixel of the other mineral. iExplorer software used scans the measured particles horizontally, from left to right, counting the associations that occur in the images (so the more pixels/closer the x-ray spacing the more accurate the data). Each column is independent. That is, it is split into a percentage of what touches what, so it is not expected that any two minerals’ data are reciprocal. The background category primarily reflects the free boundaries of ‘grains’ rather than liberated grains/particles. While it may provide an indicator of liberation, it does not represent liberation since it does not describe ‘particles’ which are made up of mineral grains. Inclusions and composite particles are therefore not described. Please consider the modal mineralogy (Tab. S1) when examining these mineral association data.</p> <p><strong>Data Set S3. </strong>Lithotyping data based on QEMSCAN® analyses. Particles have been digitally filtered using a set of lithotype rules (also displayed in this data set). These rules are based on the mineral grains in the particles themselves and use their area percent within each particle and their size in microns. The lithotype names stated here are largely assigned based on the dominant mineral grain in each category.</p> <p><strong>Data Set S4. </strong>40Ar/39Ar ages of individual sand-sized hornblende and mica. See main text for method used to generate these ages.</p> <p><strong>Data Set S5.</strong> Ties to place Hole U1538A NGR data on Dove Basin Stack (Reilly et al., 2021) depths.</p> <p><strong>Movie S1. </strong>3D-volume realization based on non-destructive X-ray microtomography imaging of a centimeter-scale iceberg-rafted debris-rich layer in Hole U1538A-36X-3W. 3D images were generated using a helical scanning trajectory that allows for long scan sequences and fast acquisition time. Based on the sample geometry, a voxel (pixel) resolution of ~14-μm was achieved. The 7000+ projection images were reconstructed to produce a 3D volume of image intensities (where higher values indicate greater x-ray attenuation). Avizo software was used for 3D segmentation and volume rendering to visualize gravel and sand to create this animation. The different colors assigned to each clast were chosen arbitrary.</p>
Functional genomics analysis to disentangle the role of genetic variants in major depression - Supplementary Tables
<p>This entry contains the data generated by the study "Functional genomics analysis to disentangle the role of genetic variants in major depression" that are part of the Supplementary information of the article describing the study.</p> <p>The entry contains the following data:</p> <p><strong>Supplementary Tables S1-S7</strong></p> <p>Supplementary Table S1. Summary of resources.</p> <p>Supplementary Table S2. Causal GVs for MD.</p> <p>Supplementary Table S3. pGenes functional and disease enrichment analysis.</p> <p>Supplementary Table S4. Fine-mapped MD causal GVs disease enrichment analysis.</p> <p>Supplementary Table S5. Colocalizing GWAS-eQTLs association to disease.</p> <p>Supplementary Table S6. TFBS analysis.</p> <p>Supplementary Table S7. GVs state annotation. </p>
Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"
<p><strong>Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"</strong></p> <p><em>cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</em></p> <p>These are CSV files of life tables over the years 2015 through 2021 across 29 countries analyzed in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</p> <p><strong>40-lifetables.csv</strong></p> <p>Life table statistics 2015 through 2021 by sex, region and quarter with uncertainty quantiles based on Poisson replication of death counts. Actual life tables and expected life tables (under the assumption of pre-COVID mortality trend continuation) are provided.</p> <p><strong>30-lt_input.csv</strong></p> <p>Life table input data.</p> <ul> <li>`id`: unique row identifier</li> <li>`region_iso`: iso3166-2 region codes</li> <li>`sex`: Male, Female, Total</li> <li>`year`: iso year</li> <li>`age_start`: start of age group</li> <li>`age_width`: width of age group, Inf for age_start 100, otherwise 1</li> <li>`nweeks_year`: number of weeks in that year, 52 or 53</li> <li>`death_total`: number of deaths by any cause</li> <li>`population_py`: person-years of exposure (adjusted for leap-weeks and missing weeks in input data on all cause deaths)</li> <li>`death_total_nweeksmiss`: number of weeks in the raw input data with at least one missing death count for this region-sex-year stratum. missings are counted when the week is implicitly missing from the input data or if any NAs are encounted in this week or if age groups are implicitly missing for this week in the input data (e.g. 40-45, 50-55)</li> <li>`death_total_minnageraw`: the minimum number of age-groups in the raw input data within this region-sex-year stratum</li> <li>`death_total_maxnageraw`: the maximum number of age-groups in the raw input data within this region-sex-year stratum</li> <li>`death_total_minopenageraw`: the minimum age at the start of the open age group in the raw input data within this region-sex-year stratum</li> <li>`death_total_maxopenageraw`: the maximum age at the start of the open age group in the raw input data within this region-sex-year stratum</li> <li>`death_total_source`: source of the all-cause death data</li> <li> <p>`death_total_prop_q1`: observed proportion of deaths in first quarter of year</p> </li> <li> <p>`death_total_prop_q2`: observed proportion of deaths in second quarter of year</p> </li> <li> <p>`death_total_prop_q3`: observed proportion of deaths in third quarter of year</p> </li> <li> <p>`death_total_prop_q4`: observed proportion of deaths in fourth quarter of year</p> </li> <li> <p>`death_expected_prop_q1`: expected proportion of deaths in first quarter of year</p> </li> <li> <p>`death_expected_prop_q2`: expected proportion of deaths in second quarter of year</p> </li> <li> <p>`death_expected_prop_q3`: expected proportion of deaths in third quarter of year</p> </li> <li> <p>`death_expected_prop_q4`: expected proportion of deaths in fourth quarter of year</p> </li> <li>`population_midyear`: midyear population (July 1st)</li> <li>`population_source`: source of the population count/exposure data</li> <li>`death_covid`: number of deaths due to covid</li> <li>`death_covid_date`: number of deaths due to covid as of <date></li> <li>`death_covid_nageraw`: the number of age groups in the covid input data</li> <li>`ex_wpp_estimate`: life expectancy estimates from the World Population prospects for a five year period, merged at the midpoint year</li> <li>`ex_hmd_estimate`: life expectancy estimates from the Human Mortality Database</li> <li>`nmx_hmd_estimate`: death rate estimates from the Human Mortality Database</li> <li>`nmx_cntfc`: Lee-Carter death rate projections based on trend in the years 2015 through 2019</li> </ul> <p><em>Deaths</em></p> <ul> <li>source: <ul> <li>STMF input data series (https://www.mortality.org/Public/STMF/Outputs/stmf.csv)</li> <li>ONS for GB-EAW pre 2020</li> <li>CDC for US pre 2020</li> </ul> </li> <li>STMF: <ul> <li>harmonized to single ages via pclm</li> <li>pclm iterates over country, sex, year, and within-year age grouping pattern and converts irregular age groupings, which may vary by country, year and week into a regular age grouping of 0:110</li> <li>smoothing parameters estimated via BIC grid search seperately for every pclm iteration</li> <li>last age group set to [110,111)</li> <li>ages 100:110+ are then summed into 100+ to be consistent with mid-year population information</li> <li>deaths in unknown weeks are considered; deaths in unknown ages are not considered</li> </ul> </li> <li>ONS: <ul> <li>data already in single ages</li> <li>ages 100:105+ are summed into 100+ to be consistent with mid-year population information</li> <li>PCLM smoothing applied to for consistency reasons</li> </ul> </li> <li>CDC: <ul> <li>The CDC data comes in single ages 0:100 for the US. For 2020 we only have the STMF data in a much coarser age grouping, i.e. (0, 1, 5, 15, 25, 35, 45, 55, 65, 75, 85+). In order to calculate life-tables in a manner consistent with 2020, we summarise the pre 2020 US death counts into the 2020 age grouping and then apply the pclm ungrouping into single year ages, mirroring the approach to the 2020 data</li> </ul> </li> </ul> <p><em>Population</em></p> <ul> <li>source: <ul> <li>for years 2000 to 2019: World Population Prospects 2019 single year-age population estimates 1950-2019</li> <li>for year 2020: World Population Prospects 2019 single year-age population projections 2020-2100</li> </ul> </li> <li>mid-year population <ul> <li>mid-year population translated into exposures: <ul> <li>if a region reports annual deaths using the Gregorian calendar definition of a year (365 or 366 days long) set exposures equal to mid year population estimates</li> <li>if a region reports annual deaths using the iso-week-year definition of a year (364 or 371 days long), and if there is a leap-week in that year, set exposures equal to 371/364\*mid_year_population to account for the longer reporting period. in years without leap-weeks set exposures equal to mid year population estimates. further multiply by fraction of observed weeks on all weeks in a year.</li> </ul> </li> </ul> </li> </ul> <p><em>COVID deaths</em></p> <ul> <li>source: COVerAGE-DB (https://osf.io/mpwjq/)</li> <li>the data base reports cumulative numbers of COVID deaths over days of a year, we extract the most up to date yearly total</li> </ul> <p><em>External life expectancy estimates</em></p> <ul> <li>source: <ul> <li>World Population Prospects (https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_Life_Table_Medium.csv), estimates for the five year period 2015-2019</li> <li>Human Mortality Database (https://mortality.org/), single year and age tables</li> </ul> </li> </ul>
Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"
<p><strong>Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"</strong></p> <p><em>cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</em></p> <p>These are CSV files of data in the figures and tables published in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</p> <p><strong>50-e0diffT.csv</strong></p> <p>Figure 1: Life expectancy changes 2019/20 and 2020/21 across countries. The countries are ordered by increasing cumulative life expectancy losses since 2019. Grey dots indicate the average annual LE changes over the years 2015 through 2019.</p> <p><strong>51-arriagaT.csv</strong></p> <p>Figure 2: Age contributions to life expectancy changes since 2019 separated for 2020 and 2021. The position of the arrowhead indicates the total contribution of mortality changes in a given age group to the change in life expectancy at birth since 2019. The discontinuity in the arrow indicates those contributions separately for the years 2020 and 2021. Annual contributions can compound or reverse. The total life expectancy change from 2019 to 2021 in a given country is the sum of the arrowhead positions across age.</p> <p><strong>52-sexdiff.csv</strong></p> <p>Figure 3: Change in the female life expectancy advantage from 2019 through 2021. Blue colors indicate an increase and red colors a decrease in the female life expectancy advantage. Muted colors indicate non-significant changes.</p> <p><strong>53-e0diffcodT.csv</strong></p> <p>Figure 4: Life expectancy deficit in 2021 decomposed into contributions by age and cause of death. LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.</p> <p><strong>55-vaxe0.csv</strong></p> <p>Figure 5: Years of life expectancy deficit during October through December 2021 contributed by ages <60 and 60+ against % of population twice vaccinated by October 1st in the respective age groups. LE deficit is defined as the counterfactual LE from a Lee-Carter mortality forecast based on death rates for the fourth quarter of the years 2015 to 2019 minus observed LE.</p> <p><strong>54-tab_arriaga.csv</strong></p> <p>Table 1: Months of life expectancy (LE) changes and deficits (labelled ES) since the start of the pandemic attributed to age-specific mortality changes (labelled AT). LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.</p>
Nutritional table to estimate the availability of nutrients in households from the Mexican National Survey of Household Income and Expenditures (ENIGH) 2008-2020
<p>The database contains the amount of six nutrients (calories, proteins, vitamin A and C, iron, and zinc) per 100 grams/mililiters for each of the food categories used in the Mexican National Survey of Household Income and Expenditures 2008-2020.</p>
Yellow-feathered broilers Testes Manuscript Figures and Table 1_Supplamentary Materials
<p>The compressed Zip file contains Table 1 of the manuscript to serve as Supplementary Materials for Article publication in addition to all manuscript Figures.</p> <p>Figures presenting Graphs of the Testes and Seminiferous tubules Morphometry have been added.</p>
Insolation values for the climate of the last 10 million years (Tables)
<p>Tables published in Berger A. and M. F. Loutre (1991), Insolation values for the climate of the last 10 million years, Quaternary Science Reviews, (10) 297 - 317 doi:10.1016/0277-3791(91)90033-Q and which constitute what is known in the community as the "BER90" solution. </p> <p>Orginal abstract: New values for the astronomical parameters of the Earth's orbit and rotation (eccentricity, obhqulty and precession) are proposed for paleochmatle research related to the Late Miocene, the Pliocene and the Quaternary They have been obtained from a numerical solution of the Lagrangian system of the planetary point masses and from an analytical solution of the Polsson equations of the Earth-Moon system. The analytical expansion developed in this paper allows the direct determination of the main frequencies with their phase and amplitude. Numerical and analytical comparisons with the former astronomical solution BER78 are performed so that the accuracy and the interval of time over which the new solution is valid can be estimated. The corresponding insolation values have also been computed and compared to the former ones. This analysis leads to the conclusion that the new values are expected to be reliable over the last 5 Ma In the time domain and at least over the last 10 Ma in the frequency domain. <br> </p>
A database of shake-table tests conducted on unreinforced masonry buildings
<p>To date, databases focusing on unreinforced masonry have primarily been developed exclusively at the element level, specifically for stone and brick masonry walls. However, despite the significance of understanding the seismic behavior of complete buildings, a database of experimental results at this structural level had not been developed to date. This repository provides an open-access database containing the results of 69 shake-table experiments on unreinforced masonry structures that have been tested over previous decades. The database is organized in a consistent format, allowing the engineering and research communities to access detailed information and results of each test conveniently.</p><p>This project seeks to play an important role in enhancing modeling techniques, model validation, and reducing uncertainties, thereby benefiting earthquake engineering researchers and practitioners and promoting improvements in design and retrofitting standards, particularly for unreinforced masonry buildings, by facilitating the search, exchange, and reuse of experimental data from shake-table tests.</p><p> </p><p>Please cite as: Haindl, M., Beyer, K., & Smith, Ian F. C. (2023). "A database of shake-table tests conducted on unreinforced masonry buildings". Submitted to Earthquake Spectra</p>
A Translation Table to Convert ERA5 Parameter IDs used in DKRZ's data pool into GRIB Codes
<p>In DKRZ's data pool on the HPC Levante, ERA5 parameters are referred to as 3-digit parameter ID.</p> <p>This 3-digit parameter ID ("PARAM") is derived from the WMO GRIB parameter table.</p> <p>PARAM <=256 : 3-digit parameter ID of GRIB table 128</p> <p>256 < PARAM <= 512 : 256 + last 3-digits of parameter ID of GRIB table 228</p> <p>For example, PARAM=259 corresponds to "friction velocity" which has the parameter ID 003 in GRIB table 228.</p> <p>In the Excel table ERA5ParamsDKRZ_YYYYMMDD.xlsx, all ERA5 parameters are listed and explained.</p>
Turkiye, Regional Domestic Input Output Tables, 2022, 26 Region, 62 X 62 Matris
<p>Regional data is crucial for conducting inter-regional economic analyses, understanding productivity, wages, and minimum wage dynamics, assessing the impact of unforeseen events like natural disasters, comparing sectors, and evaluating development levels. Supply-use tables, employment, and demographic statistics are the most commonly used data sources for these analyses.</p> <p>To address these needs and for my own economic analyses, I have created regional supply-use tables at the province level. Additionally, I have generated domestic input-output tables using the NUTS 2 regional classification for conducting regional and inter-regional analyses in Turkiye.</p> <h2>Scope of the Research</h2> <p>This research presents domestic input-output tables for 62 industries in 26 NUTS 2 regions of Turkiye for the year 2022.</p> <h2>Methodology</h2> <p>Regional accounts are published at the A10 level for every province in Turkey. In the Input-Output tables, which consist of 62 sectors, the input-output distribution has been conducted on a sector basis to derive the total at the A10 level. The estimations at the provincial level are automatically derived from regional accounts data.</p> <p>The NUTS 2 level regional input-output data have been published and disseminated with an A62 matrix. Additionally, province-level input-output data have been published and disseminated with an A62 matrix through the application. After summations the regional input output tables have been estimated.</p> <h2><strong>Application</strong></h2> <p>The link to the relevant application is attached:</p> <h3><a href="https://turkiye.streamlit.app/" target="_blank" rel="noopener ugc nofollow">https://turkiye.streamlit.app/</a></h3> <p> </p>
Earthquake Connection Indicator (ECI) table for the locations of the cult of Poseidon with earthquake-related epithets in the ancient Aegean Sea region
<p>The rationale behind this table is to categorize the spatial attachment of the evidence of the earthquake-related epithets of Poseidon to earthquake proxies in the region of the ancient Aegean Sea.</p> <h2>Attributes</h2> <h3><strong>Location</strong></h3> <p>States the location of a findspot of the evidence of an earthquake-related epithet of Poseidon.</p> <h3><strong>Epithet</strong></h3> <p>Identifies a specific earthquake-related epithet of Poseidon at a particular location.</p> <h3><strong>Earthquake_connection_indicator</strong></h3> <p>Each location of the cult of Poseidon with an earthquake-related epithet is attributed with a number from 1 to 3 based on its spatial ties to earthquake proxies. Earthquake proxies consist of active fault lines (Ganas, A., I. A. Oikonomou, and C. Tsimi. 2013. ‘NOAfaults: A Digital Database for Active Faults in Greece’. Bulletin of the Geological Society of Greece 47 (2): 518–30. https://doi.org/10.12681/bgsg.11079), and locations of ancient earthquake reports (Guidoboni, Emanuela, Graziano Ferrari, Gabriele Tarabusi, Giulia Sgattoni, Alberto Comastri, Dante Mariotti, Cecilia Ciuccarelli, Maria Giovanna Bianchi, and Gianluca Valensise. 2019. ‘CFTI5Med, the New Release of the Catalogue of Strong Earthquakes in Italy and in the Mediterranean Area’. Scientific Data 6 (1). https://doi.org/10.1038/s41597-019-0091-9; National Geophysical Data Center. 1972. ‘Global Significant Earthquake Database’. NOAA National Centers for Environmental Information. https://doi.org/10.7289/V5TD9V7K).</p> <p>ECI 1: A location with an attested cult of Poseidon with an earthquake-related epithet located more than 5 kilometers from the nearest active fault or an ancient earthquake report.</p> <p>ECI 2: A location with an attested cult of Poseidon with an earthquake-related epithet located within a 5-kilometer radius from an active fault line.</p> <p>ECI 3: A location with an attested cult of Poseidon with an earthquake-related epithet located either a) within a 5-kilometer radius from an active fault line with an ancient earthquake report anywhere along the particular fault; or b) within a 5-kilometer radius from an ancient earthquake report itself.</p> <h3><strong>Lat</strong></h3> <p>Latitude</p> <h3><strong>Long</strong></h3> <p>Longitude</p> <h3><strong>DB MAP testimony #</strong></h3> <p>ID number of a testimony of Poseidon with an earthquake-related epithet based on Bonnet C. (dir.), ERC Mapping Ancient Polytheisms 741182 (DB MAP), Toulouse 2017-2023: <a href="https://base-map-polytheisms.huma-num.fr/">https://base-map-polytheisms.huma-num.fr</a>. DOI: <a href="https://doi.org/10.34847/nkl.1e19sne6">https://doi.org/10.34847/nkl.1e19sne6.</a></p> <h3><strong>PHI ID</strong></h3> <p>IDs of inscriptions mentioning Poseidon with an earthquake-related epithet from the Searchable Greek Inscriptions (PHI, https://inscriptions.packhum.org/) as listed in the Greek Inscriptions in Space and Time dataset (GIST, Kaše, Vojtěch, Petra Heřmánková, and Adéla Sobotková. 2023. ‘GIST’. Zenodo. https://doi.org/10.5281/zenodo.10139110.). To search the ID at PHI, put the number at the end of the URL in the following format https://epigraphy.packhum.org/text/32602.</p> <h3><strong>Thely</strong></h3> <p>Indicates whether the evidence is listed in the book Thély, Ludovic. 2016. Les Grecs face aux catastrophes naturelles: savoirs, histoire, mémoire. Bibliothèque des Écoles françaises d’Athènes et de Rome : BEFAR. Athènes, Paris: École française ; diffusion De Boccard.</p>
READ ABP Table datasets
<p>Datasets used in the publication : Comparing Machine Learning Approaches for Table Recognition in Historical Register Books, Hervé Déjean, Jean-Luc Meunier, Stéphane Clinchant, Eva Maria Lang and Florian Kleber, 13TH IAPR INTERNATIONAL WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS DAS 2018; Vienna, Austria</p> <p>CHANGES:</p> <p>07/05/2018: second version: dataset150 images were missing</p> <p>dataset111<br> img/ images<br> xml/ READ pagexml with BIESO annotation</p> <p>dataset150<br> img/ images<br> GT_xml: READ pagexml with BIESO annotation<br> WK_xml: workflow version: pagexml corresponding to the workflow outputs (textlines are automatically recognised, columns as well)<br> ROWREF: GT for the row regions</p> <p>Tagset (attribute of the TextLine element)</p> <p>Type: deprecated</p> <p>DU_row:<br> B: first element of cell<br> I: inside a cell<br> E: last element of a cell<br> S: single element of a cell<br> O: outside the table</p>
NQRS raw data to 10.1103/PhysRevX.8.021076 TABLE VII
<p>These are the raw data files to the NQR measurements presented in https://link.aps.org/doi/10.1103/PhysRevX.8.021076. T1 and T2 measurements were performed using the sequences explained in APPENDIX B. Fitting models (B1), (B2) and (B3) have been applied.</p> <p>The best-fit parameters for T1, T2 and FWHM can be found in APPENDIX C, TABLE VI.</p> <p>The names of the files follow the labelling: sampleNr_transitionNr_temperture_sequence type.txt</p> <p>the organization of the txt-files is as follows:</p> <p><strong>header: </strong>file source, number of frequency-points (Starting Point, End Point) and number of records (Starting 2D Record, Ending Record) = number of parameter sweeps (e.g. inversion time, or echo time)</p> <p><strong>Data set</strong>: records stacke on top of each other:</p> <p><strong> real part [a.u.] / imaginary part [a.u.] / frequency shift [kHz]</strong></p> <p>the frequency is given in difference from the central frequency (Obeserve Freq.), which can be found in the footer</p> <p><strong>footer</strong>: contains information on the used sequence and its paramteres. eg.g the "Obeserve Freq" of the spectrometer during the sequence, (excitiation and detection frequency)</p> <p>also: filter bandwidth, dwell time, delay time, pulse length ....</p> <p>the paramter "step size" of the sweep parameter (echo time or inversion time, respectively) is available on request: christian.goesweiner@tugraz.at</p> <p> </p> <p> </p>
CO2 emissions, water table and temperature time series from an undrained tropical peatland
<p>Supplement to: Hoyt, A. M., Gandois, L. , Eri, J. , Kai, F. M., Harvey, C. F. and Cobb, A. R. (2019), CO2 emissions from an undrained tropical peatland: Interacting influences of temperature, shading and water table depth. <em>Global Change Biology</em>. https://doi.org/10.1111/gcb.14702</p>
Support Data Assets for Figures and Tables in gmd-12-3449-2019 (Geosci. Model Dev.)
<p>Support datasets for "VISIR-1.b: ocean surface gravity waves and currents for energy-efficient navigation", by G. Mannarini and L. Carelli (Geosci. Model Dev. paper, https://doi.org/10.5194/gmd-12-3449-2019):</p><ul><li><strong> graphs_gmd-2018-292.tar.gz</strong></li></ul><p>Graphs for running VISIR computations </p><ul><li><strong>figTab_DATA_v3.tar.gz:</strong></li></ul><p>Source data for Figures and Tables</p>
USGS Table AHG Parameters And Supplementary Data
<p>Password Key: 69262qRead ; For more information please email: <strong>sha17hab.afshari@gmail.com</strong> / <strong>safshar00@citymail.cuny.edu</strong> </p> <p>Simplified hydraulic geometry relationships representing the average conditions over longer reaches could reduce the need for detailed field surveys and minimize the computational burden while studying river channel flow dynamics.</p> <p> Natural streams are characterized by changes in cross-section geometry and geophysical properties (e.g., bed-roughness, channel slope, channel planform, sediment load, etc.) along their reaches. Variations in the shape of the channel bed geometry are affected by several interacting features including the effect of different flow regimes, channel slope, sediment load, etc. Simplifying the river bed geometries will reduce the burden of assembling the required data and computational burden. “At-A-Station” Hydraulic Geometry (or AHG) relations are power-law functions that relate key hydraulic variables (i.e., velocity, depth, width, and flow area) to discharge at a river monitoring station (Dingman 2007; Dingman and Afshari 2018).</p> <p> The AHG relations have been introduced and discussed among researchers, engineers, and geomorphologists since the '50s based upon a limited number of observations made over a few flow monitoring stations across the United States. Afshari et. al., 2017 introduced a data filtering procedure that was trained and tested over both synthetic and realistic data followed by being applied over ~4000 U.S. Geological Survey’s river monitoring stations to compute AHG parameters based upon robust hydraulic vs. discharge measures. Estimated AHG parameters are combined with basic statistics (mean, minimum, maximum, and standard deviation) of key morphological and geophysical features at all USGS river monitoring sites, e.g. stream (Stahler) order, channel pattern (channel sinuosity), channel bed-slope, and channel lateral [or overbank] slope. The fundamental hydraulics, geographical, and geophysical data sources (websites) applied for making the "USGS Table AHG Parameters And Supplementary Data" table are</p> <ul> <li>USGS National Water Information System (<a href="https://waterdata.usgs.gov/nwis/sw">USGS-NWIS</a>)</li> <li>USGS Staged Product Directory (<a href="https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/1/ArcGrid/">The National Map</a>)</li> <li>National Hydrography Dataset Plus V2 (<a href="http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.php">Horizon System Corporation</a>)</li> </ul> <p> In doing so, potential interrelation among independent and dependent variables will be highlighted. Accordingly, given some assumptions, it is verified how well channel morphology and hydraulic components are intertwined and combined with AHG parameters and how categorizing river monitoring stations according to these characteristics will be practical and useful for further studies.</p> <p><strong>References:</strong></p> <ol> <li>Afshari, S., B.M. Fekete, S.L. Dingman, N. Devineni, D.M. Bjerklie, and R.M. Khanbilvardi. 2017. "Statistical filtering of river survey and streamflow data for improving At-A-Station hydraulic geometry relations." J. Hydrol. 547: 443–454. doi:10.1016/j.jhydrol.2017.01.038 </li> <li>Dingman, S.L., and S. Afshari. 2018. "Field verification of analytical at-a-station hydraulic- geometry relations." J. Hydrol. 564: 859-872. doi:10.1016/j.jhydrol.2018.07.020</li> <li>Dingman, S.L. 2007. "Analytical derivation of at-a-station hydraulic geometry relations." J. Hydrol. 334: 17–27</li> </ol>
USGS Table AHG Parameters And Supplementary Data MannN
<p>For more information please send me an email to either: <strong>sha17hab.afshari@gmail.com</strong> or <strong>safshar00@citymail.cuny.edu</strong></p> <p>Simplified hydraulic geometry relationships representing the average conditions over longer reaches could reduce the need for detailed field surveys and minimize the computational burden while studying or carrying out numerical analyses and of the flow dynamics.</p> <p> Natural streams are characterized by changes in cross-section geometry, slope, and geophysical properties (bed-roughness, channel slope, etc.) along with their reaches. Variations in the shape and size of the channel bed geometry result from several interacting features of the river system including the effect of different flow regimes, slope, sediment load, etc. Simplifying the river bed geometries could reduce the burden of assembling the required data, so implementing less detailed routing procedures could lower the computational burden. “At-A-Station” Hydraulic Geometry (or AHG) relations are power-law functions which relate river key the hydraulics (i.e., velocity, depth, width, and flow area) to discharge at a river monitoring station (Dingman 2007; Dingman and Afshari 2018).</p> <p> The AHG relations have been introduced and discussed among researchers, engineers, and geomorphologist since the '50s based upon a limited number of observations made over a few flow monitoring stations across the United States. Afshari et. al., 2017 introduced a data filtering procedure which was trained and tested over both synthetic and realistic data followed by being applied over ~4000 U.S. Geological Survey’s river monitoring stations to compute AHG parameters based upon robust hydraulic vs. discharge measures. Given “refined” dataset, estimated AHG parameters are combined with basic statistics (mean, minimum, maximum, and standard deviation) of key morphological and geophysical features at all USGS river monitoring sites, e.g. stream (Stahler) order, channel pattern (channel sinuosity), channel bed-slope, and channel lateral [or overbank] slope. The fundamental hydraulics, geographical, and geophysical data sources (websites) applied for making the "USGS Table AHG Parameters And Supplementary Data" table are</p> <ul> <li>USGS National Water Information System (<a href="https://waterdata.usgs.gov/nwis/sw">USGS-NWIS</a>)</li> <li>USGS Staged Product Directory (<a href="https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/1/ArcGrid/">The National Map</a>)</li> <li>National Hydrography Dataset Plus V2 (<a href="http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.php">Horizon System Corporation</a>)</li> </ul> <p> Doing so, potential interrelation among independent and dependent variables will be highlighted. Accordingly, given some assumptions, it is verified how well channel morphology and hydraulic components are intertwined and combined with AHG parameters and how categorizing river monitoring stations according to these characteristics will be practical and useful for further studies.</p> <p><strong>References:</strong></p> <ol> <li>Afshari, S. 2019. USGS Table AHG Parameters And Supplementary Data (Version v1.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2563440</li> <li>Afshari, S., B.M. Fekete, S.L. Dingman, N. Devineni, D.M. Bjerklie, and R.M. Khanbilvardi. 2017. "Statistical filtering of river survey and streamflow data for improving At-A-Station hydraulic geometry relations." J. Hydrol. 547: 443–454. doi:10.1016/j.jhydrol.2017.01.038 </li> <li>Dingman, S.L., and S. Afshari. 2018. "Field verification of analytical at-a-station hydraulic- geometry relations." J. Hydrol. 564: 859-872. doi:10.1016/j.jhydrol.2018.07.020</li> <li>Dingman, S.L. 2007. "Analytical derivation of at-a-station hydraulic geometry relations." J. Hydrol. 334: 17–27</li> </ol>
Lookup tables for H2SO4-H2O binary and H2SO4-H2O-NH3 ternary homogeneous and ion-mediated nucleation
<p>2019/10/10</p> <p>This directory contains fortran code and lookup tables for calculating<br> nucleation rate for the following four nucleation mechanims:<br> 1. H2SO4-H2O binary homogenous nucleation (BHN),<br> 2. H2SO4-H2O-NH3 ternary homogeneous nucleation (THN),<br> 3. H2SO4-H2O-Ions binary ion-mediated nucleation (BIMN), and<br> 4. H2SO4-H2O-NH3-Ions ternary ion-mediated nucleation (TIMN)</p> <p>References:</p> <p>1. Yu, F., Nadykto, A. B., Herb, J., Luo, G., Nazarenko, K. M., and<br> Uvarova, L. A.: H2SO4-H2O-NH3 ternary ion-mediated nucleation (BHN):<br> Kinetic-based model and comparison with CLOUD measurements, Atmos.<br> Chem. Phys.,18, 17451-17474,<br> https://doi.org/10.5194/acp-18-17451-2018, 2018.<br> 2. Yu, F., Nadykto, A. B., Herb, and J., Luo, H2SO4-H2O binary and<br> H2SO4-H2O-NH3 ternary homogeneous and ion-mediated nucleation: Lookup tables,<br> in review, GMD, 2019.</p> <p><br> Files included in the package:</p> <p>YBHN/: BHN code and lookup table<br> YTHN/: THN code and lookup table<br> YBIMN/: BIMN code and lookup table<br> YTIMN/: TIMN code and lookup table</p> <p>nucl_XXX_mod.f (XXX = BHN, THN, BIMN, TIMN): Stand alone module for XXX rate calculation.</p> <p>mainXXX.f: Test program to demonstrate how the lookup table is used.</p> <p>inputXXX.dat: Example input data for testing the code and lookup table.<br> outputXXX.dat: Example output after running the test code</p> <p>Use "f95 *.f" to compile and get executable program a.out</p> <p>To run the test program:</p> <p>>a.out</p> <p>For your reference, output of running the program on my workstation has been saved to outputXXX.dat.</p> <p>I hope that the comments provided in the subroutines make<br> the code self-explanatory. Contact me if you have questions.</p> <p>Enjoy and have fun!</p> <p>Fangqun Yu<br> SUNY-Albany<br> (fyu@albany.edu)</p>
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