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openother-ncJun 2022View details →

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Chilean coast

The ensemble provides future projections of key marine variables under climate change for the Chilean coast. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and three different variables (potential temperature, dissolved oxygen, and pH) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul>   Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the area around the Yucatán Peninsula, see “Related identifiers”. A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Bay of Biscay

The ensemble provides future projections of key marine variables under climate change for the Bay of Biscay region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling  was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>. Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.   We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the North Sea

The ensemble provides future projections of key marine variables under climate change for the North Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.   Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.   We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Mediterranean Sea

The ensemble provides future projections of key marine variables under climate change for the Mediterranean region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.   Analogue datasets are provided in separate zenodo entries for the regions of the North Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Baltic Sea

The ensemble provides future projections of key marine variables under climate change for the Baltci Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>. Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

Leaf samples of three common plant species collected in seven LandKlif quadrants

Leaf samples of Acer pseudoplatanus, Dactylis glomerata and Potentilla reptans were collected in seven LandKlif quadrants along a climate gradient in summer 2020. In each quadrant, leaves were sampled in two habitats (forest and open landscape). In each habitat, three leaves from seven individuals of each species were collected. Specific leaf area (SLA) and leaf dry matter content (LDMC) of each leaf sample were determined in the lab. In addition, nitrogen content was measured at the level of individuals. This dataset contains the mean SLA and LDMC of the leaves sampled from each individual, as well as information on the site where they were collected. LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif).  Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.

Non-linear three-mode coupling of gravity modes in rotating slowly pulsating B stars: Stationary solutions and modeling potential

This repository contains the material available online that accompanies <a href="https://arxiv.org/abs/2311.02972" target="_blank" rel="noopener">Van Beeck et al. (2024)</a> (ArXiv link).  It contains zipped archives that contain inlists and final data products for the MESA stellar evolution code\(^1\) (version 15140), the GYRE stellar pulsation/oscillation code\(^2\) (version 6.0.1) and the AESolver stellar oscillation mode coupling code\(^3\). In the technical information section below you may find a description of the contents of this repository. The abstract of <a href="https://arxiv.org/abs/2311.02972" target="_blank" rel="noopener">Van Beeck et al. (2024)</a> is also available below.   Footnotes : \(^1\): see <a href="https://docs.mesastar.org/en/r15140/" target="_blank" rel="noopener">https://docs.mesastar.org/en/r15140/</a> for additional details about the MESA stellar evolution code. \(^2\): see <a href="https://gyre.readthedocs.io/en/v6.0.1/">https://gyre.readthedocs.io/en/v6.0.1/</a> for additional details about the GYRE stellar pulsation/oscillation code. \(^3\): the AESolver code can be downloaded from its Github repository: <a href="https://github.com/JVB11/AESolver" target="_blank" rel="noopener">https://github.com/JVB11/AESolver</a>; its documentation may be consulted at <a href="https://jvb11.github.io/AESolver/" target="_blank" rel="noopener">https://jvb11.github.io/AESolver/</a>.

Growth of wild cucumber (Echinocystis lobata) in three variants of support (5, 20, 50 cm) and shoot geometry

The experiments included three variants of support: 5 cm, 20 cm, and 50 cm. The data was collected in the years 2020-2023. After developing their first pair of mature leaves, when the plants were about 20-35 cm in length, the plants were photographed. The shutter was released every 15 minutes. The resulting images were combined into a 10 fps video. The following were used for the analysis: 13 recordings of the 5 cm variant and 12 recordings of the 20 and 50 cm variants. Next, an analysis was performed based on the resulting video using Tracker for kinetic analysis of video objects. The recordings were used to measure the plant growth parameters. The tape measure and point mass tools were used to determine the length of the shoots and to change the position of the apex relative to the areas of the X- and Y-axis photographed over time. To compensate for this, a trend line was drawn (polynomial of the 2nd degree, due to the very good fit, R2 = 0,975–0,995) and new plant length parameters were calculated.  To enhance the research, cross-sections of the shoots of ten plants were scanned. Samples were taken every 5 cm from the base of the plant. Using ImageJ the following parameters were measured: shoot cross-sectional area, tissue area, perimeter, and circularity.

Ecological, flowering phenology, morphological and seed production of three sympatric dioecious Chamaedorea palms from Costa Rica

The data in the file was used to estimate the factors shaping seed production in three sympatric dioecious Chamaedorea palms in Costa Rica during the 2011-2012 season. The file contains the following fields: <ol> <li>Species. The name of the species: C. costaricana, C. macrospadix and C. tepejilote</li> <li>ID. Identifier for each studied individual female plant.</li> <li>infl. Identifier for each sampled inflorescence from each sampled female.</li> <li>census.date: flowering date of each inflorescence.</li> <li>days.since.oct14: number of days since the first Chamaedorea inflorescence flowered.</li> <li>days.since.1st.flr: number of days since the first Chamaedorea inflorescence of each species flowered.</li> <li>sync.costa: flowering overlap with C. costaricana males.</li> <li>sync.macro: flowering overlap with C. macrospadix males.</li> <li>sync.tepe: flowering overlap with C. tepejilote males.</li> <li>neartest.female: distance to the nearest synchronously flowering conspecific female.</li> <li>male.5m: number of synchronously flowering conspecific male individuals in a 5m radius</li> <li>male.10m: number of synchronously flowering conspecific male individuals in a 10m radius</li> <li>female.5m.edco: number of synchronously flowering conspecific female individuals in a 5m radius, after applying Ripley's (1977) edge correction.</li> <li>female.10m.edco: number of synchronously flowering conspecific female individuals in a 10m radius, after applying Ripley's (1977) edge correction.</li> <li>male.5m.edco: number of synchronously flowering conspecific  male individuals in a 5m radius, after applying Ripley's (1977) edge correction.</li> <li>male.10m.edco: number of synchronously flowering conspecific male individuals in a 10m radius, after applying Ripley's (1977) edge correction.</li> <li>no.stems: specific for C. costaricana, number of stems per individual.</li> <li>height: height of the flowering stem in cm.</li> <li>leaves: number of leaves of the flowering stem</li> <li>leaflets: number of leaflets of the youngest leaf of the flowering stem</li> <li>leaf.rachis: length in cm of the youngest leaf of the flowering stem</li> <li>floral.rachis: length in cm of the inflorescence's rachis</li> <li>peduncle: length in cm of the inflorescence's peduncle</li> <li>no.spikes: number of spikes of the inflorescence</li> <li>no.flowers: number of flowers per inflorescence</li> <li>no.fruits: number of single-seeded fruits per inflorescence</li> </ol>

Kallaama: A Transcribed Speech Dataset about Agriculture in the Three Most Widely Spoken Languages in Senegal

This data is transcribed speech data, in Wolof, Pulaar and Sereer. The recordings are about agriculture. The recorded consist of farmers, agricultural advisers, and agri-food business managers. Type of recordings comprise interactive radio programmes, focus groups, voice messages, push messages and interviews. Therefore, spontaneous speech is prevailing. Quality of audio may vary depending on the type of programme. Content description : <ul> <li>speech_dataset_wol.tar.gz: Wolof (ISO Code 639-2: wol) speech dataset contains 55 hours of transcribed speech, including almost 13 hours of validated content check by an expert. It also contains a XSAMPA lexicon (49,132 phonetised entries) and a text corpus (1,140,508 words).</li> <li>speech_dataset_fuc.tar.gz: Pulaar (ISO Code 639-2: fuc) speech dataset contains nearly 32 hours of transcribed speech, including around 11 hours of validated content check by an expert. It also contains a text corpus (742,024 words).</li> <li>speech_dataset_srr.tar.gz: Sereer (ISO Code 639-2: srr) speech dataset contains 38 hours of transcribed speech, including nearly 11 hours of validated content check by an expert. In total, these resources provide 125 hours of transcribed speech in the 3 most widely spoken languages in Senegal, including 35 hours of checked transcriptions.</li> </ul> This work is a result of the Kallaama project, funded by Lacuna Fund for 1 year, in 2023.  See the <a title="Kallaama speech dataset" href="https://github.com/gauthelo/kallaama-speech-dataset" target="_blank" rel="noopener">GitHub repository</a> for more details about the dataset.

opencc-by-4.0Jul 2024View details →

Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan - Supporting Movie Files

This is a collection of movies related to the manuscript "Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan" by McLaughlin et al to describe kidney organization using 3D light sheet fluorescence microscopy.  The preprint manuscript associated with these movies is  Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan Liam McLaughlin, Bo Zhang, Siddharth Sharma, Amanda L. Knoten, Madhurima Kaushal, Jeffrey M. Purkerson, Heidy Huyck, Gloria S. Pryhuber, Joseph P. Gaut, Sanjay Jain bioRxiv 2024.07.29.605633; doi: <a href="https://doi.org/10.1101/2024.07.29.605633">https://doi.org/10.1101/2024.07.29.605633</a> Movie Legends Movie 1: 3D view of the entire slice showing key structures. 3D light sheet fluorescence microscopy 5x movie of reference adult sample SK3, demonstrating glomeruli, collecting ducts, nerves, and blood vessels. 0:00s — Raw signal. 0:10s — Segmentations. Annotations are in the movie. Movie 2: Relationship of nerves with glomeruli and juxtaglomerular apparatus. The movie depicts innervation of glomeruli in 2D optical sections, containing glomeruli, Tuj1(labels TUBB3)-stained nerves, and CGRP-stained sensory nerves. 0:22s — Innervation of the JGA. 0:33 s— Innervation of the Macula Densa. 0:47s — Innervation of the outer boundary of the Bowman’s Capsules. Movie 3: Neuro-nephron connectivity. The movie explores innervation between different structures of the same nephron, and between nephrons in both 3D and 2D optical sections, containing glomeruli, Tuj1 (TUBB3)-stained nerves, CGRP-stained sensory nerves, proximal (convoluted) tubule, thick ascending limb, distal convoluted tubule, and collecting duct. 0:00-1:53min — 3D relationships. 0:38s — Innervation of glomerulus JGA. 1:02min — Post-JGA innervation of medullary ray structures. 1:54min-end — 2D relationships. 2:38min — Interglomerular/internephron innervation. Movie 4: Neurovascular – nephron patterns in the medulla. The movie shows innervation pattern within the medulla. 0:00s—5x adult medullary innervation pattern in 3D; 0:34s—in 2D also showing Vasa Recta and Collecting Duct; 0:48s—in 3D at 20x resolution. 1:04min—20x adult medullary innervation of proximal tubule, thick ascending limb, and Vasa Recta in 3D; 1:31min—view if the previous in 2D. 2:11min—5x adult medullary innervation pattern in 3D; 2:40min—in 2D also showing Vasa Recta and Collecting Duct; 2:54min—in 3D at 20x resolution; 3:15—in 2D at 20x resolution. Movie 5: Network motifs. Exploring 3D neuroglomerular networks at 5x and 20x resolution. 0:00s — Raw 5x signal from young adult sample SK2. 0:16s— Segmented 5x SK2 with 20x coregistrations. 0:26 — Exploring SK2 20x FOV. 0:38 — 20x network featuring hourglass motif. 1:19 5x “Type I” network in SK2. 1:41 — Segmented 5x adult SK3 sample featuring a “Type 2” network containing a lattice motif. 2:23 — Exploring SK3 20x FOV, featuring a network with a lattice motif. Movie 6: LSFM movie of pediatric kidney 3D lightsheet 5x image of neonatal sample SK414, containing glomeruli, collecting ducts, nerves, and blood vessels. 0:00s — Raw signal. 0:22s — Segmentations. 1:34min — Overlayed segmentations. Movie 7: Neuronephron connectivity time course Exploring neuronephro-networks across a life time course in 1mm3 20x images. 0:00 — Raw neonatal. 0:17sec — Segmented neonatal. 0:47 sec— Raw infant. 0:54 — Segmented infant with network. 1:13min — Raw young adult. 1:23min — Segmented young adult with network. 1:33min — Raw adult. 1:43min — Segmented adult featuring network with keychain motif. 1:49min — Raw aged. 1:59min — Segmented aged with network featuring pyramid motif.   Movie 8: Mother Glomeruli Evaluating distributions and innervation of mother glomeruli in neuroglomerular networks. 0:00 - Large 20x 3D Network sample SK1 FOV8. 0:13 - Sample SK1 5x Network in 2D. 0:36 - Mother glomerulus neural quantifications SK1. 0:41 Large 20x 3D Network sample SK3 FOV12. 0:56. Large 20x 2D Network sample SK3 FOV12. 1:21 - Mother glomerulus neural quantifications SK3.   Movie 9: Segmentations Demonstrating accuracy of segmentations that combine supervised ML with manual validation in Sample SK2 FOV5. 0:00 - AQP2 labelled Collecting Duct and NPHS1 labelled Glomerulus. 0:26 - Tuj1 labelled nerve.   metadata_analyzed_images: Metadata for images that were analyzed. Includes metadata .txt files for all stitched, downsampled samples, as well as .csv metadata for certain raw .czi files (pre-processing).

opencc-by-4.0Jan 2024View details →

A geospatial dataset of lichen key attributes in the Earth's three poles

To develop the geospatial dataset, we initially defined two lichen attributes: color type and growth form.  These attributes were chosen due to their significant correlation with lichen physiological and biochemical characteristics, as well as their association with reflection spectra. Each record of this geospatial dataset consists of information such as scientific name, longitude, latitude, ecoregion name, biome name, color type, growth form, and the occurranceID belongs to the GBIF original dataset. This dataset serves as a foundational resource for extensive investigations into the intricate interplay between lichen physiology and the environment, addressing a significant knowledge gap in the field. Furthermore, our dataset holds the potential to address challenges associated with remote sensing monitoring of lichens, a longstanding issue in vegetation remote sensing. Precise in situ observation records, as provided by our dataset, can facilitate the development of remote sensing techniques tailored for lichen monitoring. "Program" is the code used in the process of establishing the geospatial dataset of lichen key attributes in the Earth’s three poles.

opencc-by-4.0Dec 2021View details →

From the Horse's Mouth: The Words We Use to Teach Diverse Student Groups Across Three Continents

Word frequency pairs for courses A, B, C from:  Brett A. Becker, Daniel Gallagher, Paul Denny, James Prather, Colleen Gostomski, Kelli Norris, and Garrett Powell. 2022. From the Horse’s Mouth: The Words We Use to Teach Diverse Student Groups Across Three Continents. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2022), March 3–5, 2022, Providence, RI, USA. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3478431.3499392 When referring to this dataset, please cite the above article. That contains the DOI of this dataset. Please do not cite this dataset directly without citing the article.

opencc-by-4.0Jan 2022View details →

The Three Rs: Resolving Respiration Robotically in Shelf Seas

Ocean gliders were deployed to conduct 'virtual mooring’ profiles at a study site in the seasonally stratified central Celtic Sea (station CCS, 49° 24’ N, 8° 36’ W) (see Fig. 1) during spring 2015 (6th April to- 28th April, decimal day 95 to 117) and summer 2015 (15th July and- 2nd August, decimal day 195 to 213). The integrated approach adopted in this study, combining ship based and glider measurements enabled estimates of spatial gradients while also minimising tidal aliasing that would likely be introduced by long spatial transects with the glider. A Slocum (Teledyne Webb Research, Falmouth, USA) Ocean Microstructure Glider (OMG, see Palmer et al., 2015 for full details) was equipped with a MicroRider microstructure package (Rockland Scientific International) to measure turbulentthe microstructure of velocity shear, a Seabird SBE42 CTD sensor to measure temperature, salinity and pressure, and an Aanderaa 4831 oxygen optode to measure O2 (precision 0.2 µmol kg-1). Measurements were taken within 5 m of the bed and 2 m of the surface on most dives, with each yo-yo profile taking approximately 20 minutes. Glider salinity data was corrected for thermal inertia following Palmer et al. (2015). The glider AA4831 optode is known to experience severe lag across strong oxygen gradients, and therefore oxygen data was corrected where possible for optode membrane lag following Bittig et al. (2014). Where optode lag across the oxycline was too great and so not correctable using this method, it was omitted and oxygen data from coinciding CTDs was used. In comparison to other oxygen optodes, the AA4831 has been documented by various scientific studies as being an extremely stable optode with low detectable drift ( <0.5% yr-1) and high precision of <0.2 µmol kg-1 (Kortzinger et al., 2004; Nicholson et al., 2008; Johnson et al., 2010; Champenois & Borges, 2012). Optode drift was calculated in this study by comparing discrete Winkler-analysed samples taken at deployment and recovery of the gliders, identifying a downward drift of 0.001% d-1, in close agreement with quoted manufacturer values. Glider sensors (temperature, salinity and ) were calibrated against nearby ship CTD profiles (CTD calibrated 1 month prior to cruise, SBE 43 precision = 2% of  saturation) and discrete water samples collected within 3 hours and 2 km of glider deployment and recovery times and glider position, respectively, as part of the Shelf Sea Biogeochemistry programme (RRS Discovery, DY029 and DY033). Error estimates for the total change in  (µmol kg-1) were calculated as the sum of the optode precision (0.2 µmol kg-1) and drift over the entire respective deployments (<0.1 µmol kg-1). Currents, tides, salinity and temperature were monitored throughout the glider deployments by a mooring at the CCS study site, which was equipped with an acoustic current profiler (ADCP), salinometer and thermistors that provided near-continuous data (Wihsgott et al., 2019; Ruiz-Castello et al., 2019).

opencc-by-4.0Feb 2022View details →

Three Datasets Cloned from GNNBenchmarkingDatasets

This is only a cloned version for three datasets from GNNBenchmarkingDatasets in pytorch geometric library.

opencc-by-4.0Feb 2022View details →

Simulation Data for "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip"

Simulation data from Jiang et al. (2022), "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip," Journal of Geophysical Research: Solid Earth. The archive includes simulation data for 3D SEAS benchmarks BP4-QD and BP5-QD that are analyzed in our paper (descriptions in NOTES.txt)  BP4-QD Benchmark Simulations: 1000 m:  jiang.5, lambert.8, barbot.3, barbot.2, dliu.2, li.4 500 m:  jiang.3, lambert.3, barbot.5, barbot.7, ozawa BP5-QD Benchmark Simulations: 2000 m:  jiang.6, lambert.8,  liu.4, cattania.5,          dli.7, barbot.3, dliu.10, li.3 1000 m:  jiang.2, lambert.7,  liu.5, cattania.3, ozawa,   dli.5, barbot,   dliu.6,  li.2 500 m:  jiang.4, lambert.9,  liu.6, cattania.4, ozawa.2, dli.6, barbot.2, dliu.8 250 m:  lambert.10, liu.7 BP5-QD with Off-Fault Data: 1000 m:  lambert.7, dli.5, barbot,   dliu.6, li.2 500 m:  lambert.9, dli.6, barbot.2, dliu.8 Tables 2–4 in our paper summarizes details of numerical codes and selected simulations. The benchmark descriptions and the full suite of simulation data are available at SEAS online platform https://strike.scec.org/cvws/seas/.

opencc-by-4.0Mar 2022View details →

First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS

This dataset is relative to the paper entitled: "First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS" publishing in journal Diversity (MPDI). Abstract: Zooplankton is a fundamental group in all aquatic ecosystems located the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations of populations with environmental change, especially reacting to heating and water acidification. At sea copepod crustaceans account for app. 70% in abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study we have used FAIR-inspired legacy data (dating back to the ‘80s) collected in the Ross Sea by the Italian National Antarctic Program in GBIF.org. Together with other open-access GIS data sources and tools it allows generating, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework we used machine learning with three types of algorithms (TreeNet, RandomForest and Ensemble) to analyze the presence and absence of copepods at different areas and depth classes in function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow for the first time to map-predict the food chain in quantitative terms showing the relative index of occurrence (RIO) and identified the presence for each copepod species analyzed in the Ross Sea. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting Antarctic copepod presence, also providing useful data to orient future sampling and management of wildlife and conservation.

opencc-by-4.0Mar 2022View details →

Supplemental Material to "Consistent quantification of precipitate shapes and sizes in two and three dimensions using central moments"

Supplemental material to manuscript "Consistent quantification of precipitate shapes and sizes in two and three dimensions using central moments" published in IMMJ "Integrating Materials and Manufacturing Innovation" 2022

opencc-by-4.0Apr 2022View details →

Analysis of the interacting residues between wild type SARS-CoV-2 spike protein and natural ligand hACE2, as well as three engineered alternative ligands

The analysis of residue interactions between the SARS-CoV-2 spike protein and its natural (hACE2 1) and engineered binders P17 Fab 2, Ty1 VHH 3 and LCB1 peptide 4 reveals that glutamine, serine and especially tyrosine residues on the ligand side are more frequent and influence spike binding efficiency, and that spike residues Glu484, Phe486, Tyr489 and Gln493 are more recurrent targets for interactions with ligands. The list of residues establishing contacts between the wild type structure of the SARS-CoV-2 spike protein and the binders defined above are described in Table 1. In Figure 1, the frequency and type of amino acids that interact with each spike residue is illustrated.