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Measurements of diurnal variations of meteorological parameters and subsurface water temperature in Lake Kinneret, Israel, during the period (Sept. 6 – 20, 2015)
<p>The datasets include in-situ 10-minute measurements of subsurface water temperature taken at a depth of 20 cm, at a site A (32.82 <sup>o</sup>N; 35.60 <sup>o</sup>E) located near the center of Lake Kinneret, during the period (Sept. 6 – 20, 2015). Lake Kinneret is located in Israel. The Campbell 107-L temperature probe was used (specifications are available online at <a href="https://www.campbellsci.asia/107-l">https://www.campbellsci.asia/107-l</a> ). The datasets also include meteorological measurements taken at the same site, such as air temperature, relative humidity, wind speed, upwelling and downwelling longwave (4.5 - 42 µm) radiation. The above meteorological measurements were taken at a height of 2 - 3 m above the lake surface. Measurements at the site A are associated with the Kinneret Limnological Laboratory, Israel Oceanographic and Limnological Research (<a href="https://www.ocean.org.il/kinneret-limnological-laboratory-center/">https://www.ocean.org.il/kinneret-limnological-laboratory-center/</a> ).</p> <p><em>Data format: xlsx file. The file includes water temperature (WT, <sup>o</sup>C), wind speed (WS, m/s), air temperature (Tair, <sup>o</sup>C), relative humidity (RH, %), upwelling longwave radiation (Upwelling LW, W/m<sup>2</sup>) and downwelling longwave radiation (Downwelling LW, W/m<sup>2</sup>).</em></p> <p>Files (140.40 KB)</p>
Рис. 8. СреЗы череЗ гонады моллюска: А – поперечный среЗ череЗ гонаду самки, Б–Д – фолликулы в гонадах самок (Б, В – Зрелые ооциты круглой формы, готовые к вымету; Г – ооциты в период активного гаметогенеЗа на стадии раннего трофоплаЗматического роста, Д – ооциты каплевидной формы в период преднерестовой стадии при ЗаверШении трофоплаЗматического роста), Е, Ж – поперечные среЗы череЗ гонаду самца, З, И – ацинусы в гонадах самцов (З – преднерестоваЯ стадиЯ, просветы в ацинусах практически отсутствуют, стенки ацинусов не раЗличимы, И – нерестоваЯ стадиЯ, имеютсЯ просветы в ацинусах). МасШтабные линейки 300 мкм (А), 200 мкм (Е), 100 мкм (Ж), 50 мкм (Б–Д, З, И). вя – вакуолиЗированное Ядро, сф – стенка фолликула, вм – вителлиноваЯ мембрана, РО – раЗвиваюЩиесЯ иЗ пелликулы ооциты, пг – ресничный проток гонады, с – сперматоциты, па – просветы в ацинусах. Fig. 8. Sections through the gonads of the mollusk: А – transverse section through the female gonad, Б–Д – ovarian acini, follicles (Б, В – mature round-shaped oocytes ready to be swept out; Г – oocytes in the period of active gametogenesis at the stage of early trophoplasmatic growth, Д – tear-shaped oocytes during the pre-spawning stage at the end of trophoplasmatic growth), Е, Ж – transverse sections through the male gonads, З, И – testicular acini (З – pre-spawning stage, with practically absent gaps in the acini and invisible the acini walls, И – spawning stage, with gaps in the acini). Scale bars 300 µm (A), 200 µm (E), 100 µm (Ж), 50 µm (Б–Д, З, И). вя – vacuolated nucleus, сф – follicle wall, вм – vitelline membrane, РО – developing oocytes arising from a pellicle, пг – ciliated gonadal duct, с – spermatocytes, па – gaps in acini. in Nodularia vladivostokensis (Bivalvia: Unionidae) from Razdolnaya River (Primorye, Russia)
Рис. 8. СреЗы череЗ гонады моллюска: А – поперечный среЗ череЗ гонаду самки, Б–Д – фолликулы в гонадах самок (Б, В – Зрелые ооциты круглой формы, готовые к вымету; Г – ооциты в период активного гаметогенеЗа на стадии раннего трофоплаЗматического роста, Д – ооциты каплевидной формы в период преднерестовой стадии при ЗаверШении трофоплаЗматического роста), Е, Ж – поперечные среЗы череЗ гонаду самца, З, И – ацинусы в гонадах самцов (З – преднерестоваЯ стадиЯ, просветы в ацинусах практически отсутствуют, стенки ацинусов не раЗличимы, И – нерестоваЯ стадиЯ, имеютсЯ просветы в ацинусах). МасШтабные линейки 300 мкм (А), 200 мкм (Е), 100 мкм (Ж), 50 мкм (Б–Д, З, И). вя – вакуолиЗированное Ядро, сф – стенка фолликула, вм – вителлиноваЯ мембрана, РО – раЗвиваюЩиесЯ иЗ пелликулы ооциты, пг – ресничный проток гонады, с – сперматоциты, па – просветы в ацинусах. Fig. 8. Sections through the gonads of the mollusk: А – transverse section through the female gonad, Б–Д – ovarian acini, follicles (Б, В – mature round-shaped oocytes ready to be swept out; Г – oocytes in the period of active gametogenesis at the stage of early trophoplasmatic growth, Д – tear-shaped oocytes during the pre-spawning stage at the end of trophoplasmatic growth), Е, Ж – transverse sections through the male gonads, З, И – testicular acini (З – pre-spawning stage, with practically absent gaps in the acini and invisible the acini walls, И – spawning stage, with gaps in the acini). Scale bars 300 µm (A), 200 µm (E), 100 µm (Ж), 50 µm (Б–Д, З, И). вя – vacuolated nucleus, сф – follicle wall, вм – vitelline membrane, РО – developing oocytes arising from a pellicle, пг – ciliated gonadal duct, с – spermatocytes, па – gaps in acini.
Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. Fig. 5. Dependence of start of spawning of the Japanese scallop and Pacific (giant) oyster in Peter the Great Bay on the sum of sea surface temperatures (March–June): 1 – beginning of spawning of the Japanese scallop; 2 – beginning of spawning of the Pacific oyster; 3 – sum of sea surface temperatures for the period from March to June. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. Fig. 5. Dependence of start of spawning of the Japanese scallop and Pacific (giant) oyster in Peter the Great Bay on the sum of sea surface temperatures (March–June): 1 – beginning of spawning of the Japanese scallop; 2 – beginning of spawning of the Pacific oyster; 3 – sum of sea surface temperatures for the period from March to June.
Рис. 1. Среднемесячная температура воды в б. Новгородская на поверхности: 1 – За период 1931–1973 гг.; 2 – За 1977 г.; 3 – За 1978 г.; 4 – За 1979 г.; 5 – За 1980 г.; 6 – За 1981 г.; 7 – температура нереста (18ºС). Fig. 1. Average monthly sea surface water temperature in Novgorodskaya Bay: 1 – for the period 1931–1973; 2 – for 1977; 3 – for 1978; 4 – for 1979; 5 – for 1980; 6 – for 1981; 7 –spawning temperature (18ºC). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 1. Среднемесячная температура воды в б. Новгородская на поверхности: 1 – За период 1931–1973 гг.; 2 – За 1977 г.; 3 – За 1978 г.; 4 – За 1979 г.; 5 – За 1980 г.; 6 – За 1981 г.; 7 – температура нереста (18ºС). Fig. 1. Average monthly sea surface water temperature in Novgorodskaya Bay: 1 – for the period 1931–1973; 2 – for 1977; 3 – for 1978; 4 – for 1979; 5 – for 1980; 6 – for 1981; 7 –spawning temperature (18ºC).
A convection-permitting and limited-area model hindcast driven by ERA5 data: BOLAM precipitation daily data for the period 1979-2019
<p>This dataset represents a hindcast of daily total precipitation for the period 1979-2019. Data were obtained using the BOLAM model fed by ERA5 data as initial and boundary conditions. For additional details, see the reference below.</p> <p>Citation = "Capecchi V, et al 'A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.' Climate Dynamics 61.3 (2023): 1411-1437";</p> <p>Creator_name = "Valerio Capecchi";</p> <p>Contact = "capecchi@lamma.toscana.it";</p> <p>Institute = "LaMMA - Laboratorio di Meteorologia e Modellistica Ambientale per lo sviluppo sostenibile";</p> <p>Geospatial bounds = "longitude: -26 to 53.121 by 0.089 degrees_east; latitude: 25.035 to 58.705 by 0.07 degrees_north (the Mediterranean Sea and nearby areas)";</p> <p>Grid spacing = "7 km";</p> <p>Grid = "890x482"</p>
ECHAM6-wiso and ECHAM5-wiso nudged simulation data for the period 1979-2018
<p>This data set contains model values from 4 simulations produced with the isotope-enabled atmosphere GCMs ECHAM5-wiso and ECHAM6-wiso for the period 1979-2018. These simulations been performed at different spatial resolutions (T63 and T127) or with different reanalyses for the nudging (ERA5 and ERA-Interim). A complete description can be found in Cauquoin, A. and Werner, M. (2021). High-resolution nudged isotope modeling with ECHAM6-wiso: Impacts of updated model physics and ERA5 reanalysis data. <em>J. Adv. Model. Earth Syst.</em>, <strong>13</strong>, e2021MS002532, <a href="https://doi.org/10.1029/2021MS002532">https://doi.org/10.1029/2021MS002532</a>.</p> <p> </p> <p>The 4 performed simulations, described in Cauquoin and Werner (JAMES, 2021), are: </p> <p>- E6_LR_ERA5: ECHAM6-wiso at T63L47 spatial resolution, nudged to ERA5.</p> <p>- E6_LR_ERAI: ECHAM6-wiso at T63L47 spatial resolution, nudged to ERA-Interim.</p> <p>- E5_LR_ERA5 : ECHAM5-wiso at T63L47 spatial resolution, nudged to ERA5.</p> <p>- E6_HR_ERA5: ECHAM6-wiso at T127L95 spatial resolution, nudged to ERA5.</p> <p> </p> <p>The files are in netcdf or excel format:</p> <p>- *.temp2_timmean.nc: annual mean 2m air temperature (°C)</p> <p>- *.d18Op_timmean.nc: annual mean d18O of precipitation (permil)</p> <p>- *.dexp_timmean.nc: annual mean d-excess of precipitation (permil)</p> <p>- E6_LR_ERA5.precip_timmean.nc and E6_LR_ERA5.precip_timmean.nc: annual mean precipitation from ECHAM6-wiso T63L47 (mm/month)</p> <p>- E6_LR_ERA5.d18Oqvi_timmean.nc and E6_LR_ERA5.d18Oqvi_timmean.nc: annual mean d18O of vertically integrated water vapor from ECHAM6-wiso T63L47 (permil)</p> <p>- E6_LR_ERA*.qtot_*_timmean.nc: u and v components of annual mean water vapor transport from ECHAM6-wiso T63L47 (kg/m/s)</p> <p>- E6_LR_ERA*_tropics_mermean.q.nc: meridional mean between 15°S and 15°N of the modeled annual mean specific humidity from ECHAM6-wiso T63L47 (kg/kg)</p> <p>- E6_LR_ERA*_tropics_mermean.d18Oq.nc: meridional mean between 15°S and 15°N of the modeled annual mean d18O of water vapor from ECHAM6-wiso T63L47 (permil)</p> <p>- echam_wiso_seasonal_signals_*.xlsx: modeled monthly mean variations of 2m air temperature, precipitation, d18O of precipitation and d-excess of precipitation according to the 4 simulations at Ankara, Belem, Halley Bay, Reykjavik, Valentia and Vienna for the period 1979-2018.</p> <p>- echam_wiso_subdaily_signals_*.xlsx: modeled (sub-)daily variations surface specific humidity, d18O of surface water vapor and d-excess of surface water vapor according to the 4 simulations at Ankara, Mase, Niwot Ridge and Summit.</p>
Data from: Monitoring microarthropods assemblages along a pH gradient in a forest soil over a 60 years' time period
<p>The goal of this study was to assess the development, over 60 years, of microarthropod communities over a pH gradient in forest soil.</p> <p>Site Description</p> <p>Hackfort is an oak coppice grove in the East-Southeast of the city of Zutphen in the province of Gelderland, the Netherlands, 52°06′09.7″ N, 6°15′56.0″ E (see Figure 1). The experimental area is about 1.5 ha and is divided in a 10 m × 10 m grid. Vegetation is dominated by common oak (<em>Quercus robur</em>), mixed with birch (<em>Betula pendula</em>), and had in 1959, an understory of wood sage plugs (<em>Teucrium scorodonia</em>), wood anemone (<em>Anemone nemorosa</em>), bracken (<em>Pteridium aquilinum</em>), and wavy-hair grass (<em>Deschampsia flexuosa</em>). In later years, the understory became more dominated by bramble species (<em>Rubus fruticosus </em>and<em> R. idaeus</em>) and common nettles (<em>Urtica dioica</em>) at the edges of the forest, due to increased N deposition from adjacent farmland. The forest is situated at the transition from western riverine deposits and eastern periglacial cover sands. The soil is a riverine deposit with a few elevation differences, making a number of gradients in clay and loam content, which results in many short-distance gradients in soil types, varying from typic haplaquolls with the largest loam contents, via psammaquentic haplorthods to humaqueptic spodic psammaquents, slightly elevated and low in loam contents.</p> <p>Microarthropod Sampling and pH Measurement</p> <p>In 1959, samples were taken at three subsequent dates: 11 September, 9 October, and 30 October. Samples in 1987 were taken on one date, 9 October, just as on 30 October 2019. Samples were taken following a standard procedure, developed at the Institute for Applied Biological Research in Nature, Wageningen, the Netherlands (later merged into the Research Institute for Nature management, Institute for Forestry and Nature Research and Alterra resp., now known as Wageningen Environmental Research); this procedure has been published by Siepel and van de Bund in 1988 (Siepel and van de Bund, 1988). Each mineral soil sample has 100 cc: a volume of 5 cm diameter and 5 cm depth plus litter on top. In 1959, two samples per date were taken on each plot, making a total of 6 samples (only pooled data are available); in 1987 and in 2019, 4 and 5 samples for each plot were taken on, respectively (data per sample available).</p> <p>Soil cores were put on a Tullgren funnel for 1 week, during which temperature was increased from 35 to 45 °C, and then, microarthropods were collected in 70% alcohol and later put into 20% lactic acid for clarification and identification (Siepel, 1990; Siepel and van de Bund, 1988). The Tullgren funnel used for extraction (Siepel, 1990) has been used ever since 1936 and efficiency has not changed as the tool and protocol was the same all over the years.</p> <p>Identification was done to the species level as much as possible using at present the keys for Oribatida(Weigmann and G., 2006), for Gamasina (Lehtinen, 1994), for Uropodina (Karg, 1989), and for Collembola (Hopkin, 2007). Material from the extractions of 1959 and 1987 was re-examined as far as possible to check the correct species identification. In the 1959 and 1987 samples, only oribatid mites were identified to the species level, whereas in 1959, all species of <em>Quadroppiidae, Oppiidae</em>, and <em>Suctobelbidae</em> were pooled. In 2019, all microarthropods were identified to the species level.</p> <p>Sorting and identification of the 1959 microarthropods was carried out by an experienced acarologist (J.G. de Gunst), in 1987, this was done by a student (C. Arnold) and completed and checked by the second author. For the 2019 samples, we decided to demonstrate the potential difference in picking out the microarthropods from the extraction fluid into the slides for identification as part of the experiment: the first author made a first series of slides including all distinguished animals (dataset 2019 a), while the second author made an extra set of slides with the animals missed by the first (dataset 2019 b). The first author did know since the beginning that the second author would check all samples after her sorting session. In this way, we intended to demonstrate the potential difference in this crucial part of the procedure by a starting and an experienced professional. In the analysis, we compare dataset (2019 a) with (2019 a + b), in order to highlight the difference between a starting and an experienced acarologist. Nomenclature adopted was updated according to current standards, following, e.g., the checklists for Oribatida (Siepel et al., 2009), for Astigmatina (Siepel et al., 2016), and for Mesostigmata (Siepel, 2018). Values of pH-KCl were measured in the core material after the extraction of the microarthropods, both in 1959, 1987, and 2019.</p> <p> </p> <p>We have four data files:</p> <p>1959 hackfort microarthropods data.csv</p> <p>1989 hackfort microarthropods data.csv</p> <p>2019 hackfort microarthropods data.csv</p> <p>pH data Hackfort 1959-2019.csv.</p> <p> </p> <p>Explanation of the variables in the datasets:</p> <p>higher taxon: Oribatida, Astigmata, Mesostigmata, Prostigmata, Collembola or Protura</p> <p>Name in De Gunst 1959: taxonomic identification by De Gunst in 1959</p> <p>Valid name: Henk Siepel re-checked these species names in 2019</p> <p>Plot: plot 1, plot 2, plot 3, plot 4, plot 5</p> <p>a: identified by Yuxi Guo</p> <p>b: re-checked by Henk Siepel from remaining soil microarthropods in slide</p> <p>pH(KCL) and pH(H2O): pH values based on indicated methods</p> <p> </p>
PM10 concentrations in the region of West Macedonia, Greece for a 10-year period
<p>Dataset of PM10 daily average concentrations for a 10-year period The data presented were collected from nine locations from 2021 to 2020 in the region of western Macedonia, Greece</p>
Chaos and irreversibility of a flexible filament in periodically--driven Stokes flow
<pre>This directory contains a jupyter notebook file, generate_plots.ipynb which generate all the plots for paper "Chaos and irreversibility of a flexible filament in periodically driven Stokes flow" (arXiv:2111.14638) by Vipin Agrawal and Dhrubaditya Mitra. Readme.md file contains the information on how to use the data. If anything turns out to be incomplete, please reach to vipinagrawal25@gmail.com.</pre>
Dataset: HRSTEM Images of Defective and Non-Defective Quasi-Periodic Materials
<p>This is the image dataset and model used to produce the results reported in the following publication: <br> Dennler, N., Foncubierta-Rodriguez, A., Neupert, T., Sousa, M. (2021). <em>Learning-based defect recognition for quasi-periodic HRSTEM images</em>. Micron, 146(July 2020), 103069. https://doi.org/10.1016/j.micron.2021.103069<br> <br> For questions, please correspond with N. Dennler (n.dennler2<strong> </strong>at<strong> </strong>herts.ac.uk) or with M. Sousa (sou at zurich.ibm.com).</p> <p><strong>hrstem_defects_dataset.zip:</strong> These are the images and labels used to develop and test the algorithm proposed in the above-mentioned publication. They correspond to high resolution scanning transmission electron microscopy images obtained for various III-V films, namely InP, GaAs, InGaAs and InAlGaAs using a JEOL ARM200F microscope. The raw images have been converted in .tif format with the GMS 3 program from Digital Micrograph. The labels have been created by a microscopy expert. Black: main crystal symmetry (non-defective). Gray: secondary crystal symmetry (symmetry defect). White: blurred (amorphous region or beam defect)<br> <br> <strong>vgg16.zip: </strong>The trained neural network model as well as a detailed description of the training/testing dataset that was used to achieve the results reported in the above-mentioned publication.</p>
Remnants of the amalgamation of the East and West Cathaysia blocks revealed by a short-period dense seismic array
<p>This is the data used to support relevant research, including the data of the teleseismic receiver function with the Gaussian factor of 10.0, which can be used to facilitate further relevant research.</p>
Performance of GFN1-xTB for periodic optimization of Metal-Organic Frameworks
<p>GFN-xTB optimised structures of CoRE 2014 and CoRE 2019 structures, with and without lattice optimisation.</p>
Dataset - Associations between author-level metrics in subsequent time periods
<p>Dataset used in the project https://github.com/carolmb/associations_between_author_level_metrics.</p> <p>Author-level metrics are calculated in sequential 5-years-windows and subsequent 3-years-windows, more details are described in https://doi.org/10.1016/j.joi.2021.101218.</p>
Further Evidence of Modified Spin-down in Sun-like Stars: Pileups in the Temperature–Period Distribution
<p>Datasets for "Further Evidence of Modified Spin-down in Sun-like Stars: Pileups in the Temperature–Period Distribution," by David et al. 2022 (in review at The Astrophysical Journal).</p> <p>The article pre-print is available on <a href="https://arxiv.org/abs/2203.08920">arXiv</a>.The code required to reproduce this work is available on <a href="https://github.com/trevordavid/rossby-ridge">GitHub</a>.</p> <p> </p>
Longer and warmer prewinter periods reduce post-winter fitness in a diapausing insect
<p>1. Diapause is considered an important adaptation for survival of winter; however, insects often enter diapause long before its onset. Thus, diapausing insects must also be able to survive these prewinter conditions which warm temperatures could make quite energetically taxing despite relative inactivity.</p> <p>2. We tested for both immediate and delayed fitness effects of prewinter conditions in diapausing Pieris napi butterfly pupae, experimentally exposing them to different prewinter treatments in a factorial design. We placed diapausing pupae at one of three temperatures (15, 20, 25 °C) for 1 to 16 weeks, followed by the same standardized winter for all individuals.</p> <p>3. We monitored survival at multiple points during the experiment, including after winter, as well as their change in mass. For a subset of individuals, we also made repeated metabolic measurements.</p> <p>4. We found substantial weight loss during prewinter warm periods, greater during longer prewinter treatments at higher temperatures. This weight loss was associated with elevated metabolic rates at higher temperatures which increased over the duration of the prewinter treatment.</p> <p>5. Although we found little prewinter mortality associated with these conditions, mortality was much greater post-winter for individuals in long, warm prewinter treatments and the dry mass of adults that did survive these conditions was lower, highlighting the need to understand chronic or delayed effects of stress on fitness.</p> <p>6. Ultimately, we found substantial fitness consequences of prewinter conditions for a diapausing insect. Given that climate change will make these prewinter periods both longer and more intense, it will be important to understand how dormant organisms tolerate or reduce the length of these dormant, inactive periods.</p>
Collection of literature and extracted features in the field of under-frequency load shedding for the period 1954-2020
<p>The number of publications in the field of UFLS research is rapidly increasing. This confirms that the topic is timely, but due to the amount of publications, it is easy to lose track of the prevailing concepts driving the latest technologies. To overcome this problem, researchers resort to subjective review articles in the literature in which individual authors attempt to systematically categorize UFLS algorithms according to their own understanding of a variety of approaches. Since this is not exactly a trivial task and is undoubtedly subject to personal interpretation, it is better to use specialized mathematical techniques for this purpose. Recently, it has been shown that the use of clustering techniques and graph theory can be a useful tool for systematic literature reviews.</p> <p>If one chooses to search for similarities between UFLS algorithms using such techniques, one must first collect the relevant literature in the field and extract important information. Therefore, this collection provides an Excel spreadsheet (.xlsx) of 381 publications in the field of UFLS protection for the period from 1954 to 2020, with the following information for each publication:</p> <ol> <li><em>authors</em>,</li> <li><em>title</em>,</li> <li><em>year</em> of publication (journal, conference proceedings, doctoral dissertation, master's thesis, bachelor's thesis, other),</li> <li>the <em>source</em> from which we obtained the publication,</li> <li><em>DOI</em> (Digital Object Identifier),</li> <li>extracted <em>general features</em>, and</li> <li>extracted <em>specific features</em>.</li> </ol> <p>Relevant literature was obtained from various open access journals, impact factor journals, repositories of various universities, and archives of various electric utilities and transmission system operators.</p> <p>If the publication in the row can be assigned to the feature specified in column I3-BJ3, the corresponding cell contains the value "1". Otherwise, i.e., if the feature specified in column I3-BJ3 cannot be assigned to the publication, the cell is empty. Detailed descriptions of the individual feature can be found in "Description_of_features.docx". Namely, two identical documents are available, one in English and one in Slovene.</p> <p>Updated versions with more recent data will be uploaded with a differing version number and doi.</p>
Fig. 1 in Morphological Differentiation In Nestlings Turdus Philomelos (Passeriformes, Turdidae) And Staging In Their Development During The Nestling Period Of Postembryogenesis
Fig. 1. Dynamics of changes in 12 morphometric traits in nestlings during the nesting stage of postembryogenesis (from 1 to 14 days), the stage of fast growth (from 1 to 8 days) and of slow growth (from 8 to 14 days).
CROSSBOW HLU3-UC1-TC1 & HLU3-UC3-TC1 AM Market prices in the demonstration period
<p>CROSSBOW RES-CC continuously participates in the CROSSBOW AM ID market. The dataset comprises some of the market results that are used in the demonstrations. Fields are:</p> <ul> <li>Time slot (hourly)</li> <li>BSP (data is aggregated by hour and BSP)</li> <li>Energy forecasted</li> <li>Energy price in ID market</li> </ul>
Data set: On the porosity-dependent permeability and conductivity of triply periodic minimal surface based porous media
<p>This file contains all processed data from the simulations and calculations.</p>
Modeling output for "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars"
<p><strong>General information:</strong></p> <p>Please contact Claire Newman (claire@aeolisresearch.com) if you have questions about this dataset.</p> <p><em><strong>Title of Dataset: </strong></em>Modeling output for "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars"</p> <p><em><strong>Author Information:</strong></em><br> Name: Claire E. Newman<br> Institution: Aeolis Research<br> Email: claire@aeolisresearch.com</p> <p>Recommended citation for this dataset: Newman C.E. (2022) "Modeling output for Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars", Dataset.</p> <p><strong>Summary taken from the paper "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars" by Kathryn M. Stack et al., accepted by JGR Planets for publication in 2022:</strong></p> <p>The orientation of Glen Torridon ridges was compared against outputs from the Mars Weather Research and Forecasting (MarsWRF) model. The MarsWRF model has been used to simulate the atmospheric circulation inside Gale crater to compare with MSL Rover Environmental Monitoring Station (REMS) wind measurements (Newman et al., 2017) and to assist in interpreting observations of dust devils (Newman et al., 2019) and aeolian changes (Baker et al., 2018, 2022). The output used in this work comes from the ‘vertical grid B’ simulations described in Newman et al. (2017), which provide the best match to observed winds and aeolian features. For Gale crater modeling, MarsWRF is run as a global model with nested higher-resolution domains that gradually increase the model’s horizontal resolution over smaller and smaller areas, finally providing output at ~400m grid spacing over the NW quadrant of the crater. The version of MarsWRF used here includes the treatment of radiative transfer in Mars’s dusty CO2 atmosphere, the seasonal CO2 cycle, subsurface-surface-atmosphere exchange of heat and momentum, and vertical mixing of heat and momentum, with surface properties (topography, roughness, albedo, etc.) based on orbital datasets and the seasonally-evolving, non-dust-storm “Mars Climate Database” dust distribution imposed (see Richardson et al., 2007 for more details).</p> <p>The model outputs minute-by-minute predictions of surface friction velocity, u*, and atmospheric density at 1.5m, ρ, for 7 sols at each of 12 periods, which are equally spaced in planetocentric solar longitude (Ls) through the martian year. For each output, the surface wind stress, τ, is found from τ=ρu_*^2. We then adjusted the contribution of each period to account for the varying number of sols Mars spends around each period over its orbit, before producing wind stress roses, which thus represent the total wind stress and direction over a non-dust-storm Mars year.</p> <p><strong>Specifically, contained in the "outputsfullcorr.nc" netCDF data file are:</strong></p> <p><strong><em>Variables:</em></strong></p> <p>PSFC: surface pressure (Pa)</p> <p>T1_5: temperature at 1.5m height above the surface (K)</p> <p>U1_5: zonal (west-to-east) wind at 1.5m height above the surface (m/s)</p> <p>V1_5: meridional (south-to-north) wind at 1.5m height above the surface (m/s)</p> <p>UST: surface friction speed (m/s)</p> <p><strong><em>Output times:</em></strong></p> <p>These variables are output from MarsWRF every minute starting at 15:10 Local True Solar Time in the first sol, with 1440 outputs per martian sol. There are 68 sols of data in total, with between 5 and 7 sols of data from each 30° Ls range, with the relative number chosen to reflect the fraction of a Mars year spent in that Ls range. In detail, the sols of the martian year used (where Ls=0 would be at the start of sol 1 and Ls=360 at the end of sol 669) are:</p> <p>Ls~0°: sols 668-669 & 01-4</p> <p>Ls~30°: sols 60-65</p> <p>Ls~60°: sols 124-130</p> <p>Ls~90°: sols 194-200</p> <p>Ls~120°: sols 254-259</p> <p>Ls~150°: sols 314-319</p> <p>Ls~180°: sols 374-378</p> <p>Ls~210°: sols 424-428</p> <p>Ls~240°: sols 474-478</p> <p>Ls~270°: sols 514-518</p> <p>Ls~300°: sols 564-568</p> <p>Ls~330°: sols 614-618</p> <p><strong><em>Output longitudes and latitudes are in file "static.nc":</em></strong></p> <p>The output is provided for a uniform grid of 19 longitudes by 15 latitudes. The longitudes and latitudes (variables XLONG and XLAT) in static.nc match those in outputsfullcorr.nc, but below are listed the longitudes and latitudes for convenience:</p> <p>Longitudes (19): 137.321, 137.3292, 137.3374, 137.3457, 137.3539, 137.3621, 137.3704, <br> 137.3786, 137.3868, 137.3951, 137.4033, 137.4115, 137.4198, 137.428, <br> 137.4362, 137.4444, 137.4527, 137.4609, 137.4691 </p> <p>Latitudes (15): -4.786012, -4.777781, -4.769551, -4.761321, -4.75309, -4.74486, -4.736629, -4.728399, -4.720168, -4.711937, -4.703707, -4.695477, -4.687246, -4.679016, -4.670785</p> <p>static.nc also provides the local topographic height used by the model at each location (variable name HGT).</p> <p><strong><em>Output data format:</em></strong></p> <p>NetCDF (Network Common Data Form) is a set of software libraries and machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data. It is also a community standard for sharing scientific data. The Unidata Program Center supports and maintains netCDF programming interfaces for <a href="https://docs.unidata.ucar.edu/netcdf-c/current/">C</a>, <a href="https://docs.unidata.ucar.edu/netcdf-cxx/current/">C++</a>, <a href="https://www.unidata.ucar.edu/software/netcdf-java/">Java</a>, and <a href="https://docs.unidata.ucar.edu/netcdf-fortran/current/">Fortran</a>. Programming interfaces are also available for Python, IDL, MATLAB, R, Ruby, and Perl.</p> <p>See: <a href="https://www.unidata.ucar.edu/software/netcdf/">https://www.unidata.ucar.edu/software/netcdf/</a></p> <p><strong>References:</strong></p> <p>Baker, M.M., Lapotre, M.G.A., Minitti, M.E., Newman, C.E., Sullivan, R., Weitz, C.M., Rubin, D.R., Vasavada, A.R., Bridges, N.T., & Lewis, K.W. (2018). The Bagnold Dunes in Southern Summer: Active Sediment Transport on Mars Observed by the Curiosity Rover. <em>Geophysical Research Letters, 45</em>(17), 8853-8863. <a href="https://doi.org/10.1029/2018GL079040">https://doi.org/10.1029/2018GL079040</a>.</p> <p>Baker, M.M., Newman, C.E., Lapotre, M.G.A., Sullivan, R., Bridges, N.T., & Lewis, K.W. (2018). Coarse Sediment Transport in the Modern Martian Environment. <em>Journal of Geophysical Research- Planets, 123</em>(6), 1380-1394. <a href="https://doi.org/10.1002/2017JE005513">https://doi.org/10.1002/2017JE005513</a>.</p> <p>Baker, M.M., Newman, C.E., Sullivan, R., Minitti, M.E., Edgett, K.S., Fey, D., Ellison, D., & Lewis, K.W. (2022). Diurnal variability in aeolian sediment transport at Gale crater, Mars, J. Geophys. Res. (Plan.), 127, e2020JE006734, https://doi.org/10.1029/2020JE006734.</p> <p>Newman, C., Gómez‐Elvira, J. G., Marín, M., Navarro, S., Torres, J., Richardson, M. I., et al. (2017). Winds measured by the Rover Environmental Monitoring Station (REMS) during the Mars Science Laboratory (MSL) rover's Bagnold Dunes Campaign and com- parison with numerical modeling using MarsWRF. <em>Icarus, 291</em>, 203–231. https://doi.org/10.1016/j.icarus.2016.12.016</p> <p>Newman, C. E., Kahanpää, H., Richardson, M. I., Martinez, G. M., Vicente‐Retortillo, A., & Lemmon, M. (2019). Convective vortex and dust devil predictions for Gale crater over 3 mars years and comparison with MSL‐REMS observations, <em>J. Geophys. Res. (Plan.</em>), 124, 3442– 3468, https://doi.org/10.1029/2019JE006082.</p> <p>Richardson, M.I., Toigo, A.D., & Newman, C.E. (2007). PlanetWRF: A general purpose, local to global numerical model for planetary atmospheric and climate dynamics<em>, J. Geophys. Res. (Plan.)</em>, 112, E09001, <a href="https://doi.org/10.1029/2006JE002825">https://doi.org/10.1029/2006JE002825</a>.</p>
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