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361 results for “January”
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWLNETS (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWLNETS</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWL (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
Challenges to freedom of speech and journalists in Ukraine in times of war – Non-representative online expert survey of Ukrainian journalists (January 2023)
The expert survey of journalists was conducted from 18 to 27 January 2023 using a self-completion questionnaire in Google Forms. The survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation on the request of the Human Rights Centre ZMINA with the support of Freedom House Ukraine. A total of 132 people participated in the survey. The respondents were selected using the method of voluntary selection and snowballing to the point of saturation. The sample represents only the opinion of the respondents, but it also allows us to talk about certain trends and common assessments of certain phenomena and processes in the journalistic field. The survey includes questions about freedom of speech and self-censorship in the media environment during the Russian-Ukrainian war. The data collection contains original survey data. The Excel file (.xlsx) is the original file with the respondents' answers in Ukrainian, provided by the Ilko Kucheriv Democratic Initiatives Foundation. The documentation includes the questions and answer options of the original questionnaire in Ukrainian and English. Additionally, the data collection contains the "Summary" file, which is an analytical report prepared by the Ilko Kucheriv Democratic Initiatives Foundation and the Human Rights Centre ZMINA. The report uses data from an expert survey of journalists in 2019 and 2023, and the results of focus groups in 2022.
Preliminary Coastal Grain Size Portal (C-GRASP) dataset. Version 1, January 2022
<p>Provisional database: The data you have secured from the U.S. Geological Survey (USGS) database identified as <em>Preliminary Coastal Grain Size Portal (C-GRASP) dataset. Version 1, January 2022</em> have not received USGS approval and as such are provisional and subject to revision. The data are released on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use.</p> <p>Version 1 (January 2022) of the the Coastal Grain Size Portal (C-GRASP) database. This is a preliminary internal deliverable for the National Oceanography Partnership Program (NOPP) Task 1 / USGS Gesch team and project partners only.</p> <p>The primary purpose of this Provisional data release is to provide National Oceanography Partnership Program (NOPP) project partners with programmatic access to this preliminary version of the Coastal Grain Size Portal (C-GRASP) database for internal project use. These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p> <p>This preliminary data release contains various files that list grain size information collated from secondary data already in the public domain, in the form of public datasets, or in published literature.</p> <p>Where possible, we have indicated the source, location, and sampling methods used to obtain these data. Where not possible to establish these facts, those fields have been left empty.</p> <p>More information on our methods, data sources, and data processing and analysis codes are found on our <a href="https://github.com/C-GRASP">github page </a></p> <p>The dataset consists of one zipped file, Source_Files.zip, and 4 comma separated value (csv) files</p> <ol> <li>dataset_10kmcoast.csv- This is all data that is found to be within 10km of the Natural Earth coastline polyline</li> <li>Data_EstimatedOnshore.csv- This is all the data from dataset_10kmcoast.csv that lies within the Natural Earth United States Polygon</li> <li>Data_VerifiedOnshore.csv- This is all data that was able to be verified onshore from either sampling method, note, or location type data</li> <li>Data_Post2012_VerifiedOnshore.csv- This is all the data from Data_VerifiedOnshore.csv that is after 2012</li> </ol> <p>The files each have the following fields (no data is blank):</p> <p>'ID': row ID integer</p> <p>'Sample_ID': identifier to raw data source</p> <p>'Sample_Type_Code': code of sample id</p> <p>'Project': raw datasource project identifier</p> <p>'dataset': raw dataset major identifier</p> <p>'Date': date, where specified, and to whatever precision that is specified</p> <p>'Location_Type': where specified, code indicating type of location information</p> <p>'latitude': latitude in decimal degrees</p> <p>'longitude': longitude in decimal degrees</p> <p>'Contact': where specified, raw data originator</p> <p>'num_orig_dists': number of unique grain size distributions</p> <p>'Measured_Distributions': number iof measured grain size distributions</p> <p>'Grainsize': grain size is sometimes reported without specification</p> <p>'Mean', mean grain size in mm</p> <p>'Median', median grain size in mm</p> <p>'Wentworth', wentworth name (one of ['Clay', 'CoarseSand', 'CoarseSilt', 'Cobble', 'FineSand', 'FineSilt', 'Granule', 'MediumSand', 'MediumSilt', 'Pebble', 'VeryCoarseSand', 'VeryFineSand', 'VeryFineSilt'])</p> <p>'Kurtosis', kurtosis value (non-dim)</p> <p>'Kurtosis_Class', kurtosis category</p> <p>'Skewness', skewness value (non-dim)</p> <p>'Skewness_Class', skewness category</p> <p>'Std', standard deviation of grain sizes </p> <p>'Sorting', sorting category</p> <p>'d5', grain size distribution 5th percentile</p> <p>'d10', grain size distribution 10th percentile</p> <p>'d16', grain size distribution 16th percentile</p> <p>'d25', grain size distribution 25th percentile</p> <p>'d30', grain size distribution 30th percentile</p> <p>'d50', grain size distribution 50th percentile</p> <p>'d65', grain size distribution 65th percentile</p> <p>'d75', grain size distribution 75th percentile</p> <p>'d84',grain size distribution 84th percentile</p> <p>'d90', grain size distribution 90th percentile</p> <p>'d95', grain size distribution 95th percentile</p> <p>'Notes': notes - these can be informative and substantial, do not disregard</p> <p> </p> <p>Source_Files.zip contains 11 comma separated value files, namely bicms.csv boem.csv clark.csv dbseabed.csv ecstdb.csv mass.csv mcfall.csv rossi.csv sandsnap.csv sbell.csv ussb.csv, which contain raw datasets that have been collated and extracted from their native formats into csv format</p>
RINEX files from low-cost GNSS receivers in Wrocław, Poland; January - March, 2021
<p>Daily RINEX files with multi-GNSS (GPS, GLONASS, Galileo) observations at 30 sec. interval obtained with low-cost GNSS receiver u-blox ZED-F9P and u-blox patch antennas (except BX02 - ArduSimple survey antenna). Time period (depending on stations): 27.02.2021 - 28.03.2021.</p>
Data set: Average daily minimum temperature in January and February in Corsica
<p>Raster providing the average of the daily minimum temperature in Celsius degrees over January and February in Corsica from 1995 to 2003 with a 0.016667x0.0166671 resolution in latitude and longitude.</p> <p>Construction: This raster was constructed from the freely available database (PVGIS © European Communities, 2001-2008) providing, in particular, monthly averages of the daily minimum temperature reconstructed over a grid with 1$\times$1km spatial resolution (Huld et al., 2006). These monthly averages correspond to the period 1995-2003 and were used by Abboud et al. (2019, 2020) to model Xylella fastidious dynamics in South Corsica.</p> <p>Load the raster in the R statistical software (v4.1.2):<br> library(raster)<br> ADMT=raster("average-daily-minimum-temperature_Corsica_Abboud-et-al_Forecasting.grd")<br> print(ADMT)<br> plot(ADMT)</p> <p>Summary information:<br> class : RasterLayer <br> dimensions : 108, 78, 8424 (nrow, ncol, ncell)<br> resolution : 0.016667, 0.016667 (x, y)<br> extent : 8.400708, 9.700734, 41.30018, 43.10021 (xmin, xmax, ymin, ymax)<br> crs : +proj=longlat +datum=WGS84 +no_defs <br> source : average-daily-minimum-temperature_Corsica_Abboud-et-al_Forecasting.grd <br> names : layer <br> values : -0.6748945, 6.75789 (min, max)</p> <p>References:<br> - Abboud, C., Bonnefon, O., Parent, E., and Soubeyrand, S. (2019). Dating and localizing an invasion from post-introduction data and a coupled reaction–diffusion–absorption model. Journal of Mathematical Biology 79, 765–789.<br> - Abboud, C., Parent, E., Bonnefon, O., and Soubeyrand, S. (2022). Forecasting pathogen dynamics with Bayesian model-averaging: Application to Xylella fastidiosa. Preprint.<br> - Huld, T. A., Suri, M., Dunlop, E. D., and Micale, F. (2006). Estimating average daytime and daily temperature profiles within Europe. Environmental Modelling & Software 21, 1650–1661.</p>
IMS sulphate aerosol in the stratospheric plume of the January 2022 Tong aeruption
<p>This animation is made using the IMS sulphate aerosol optical depth product (see https://www?doi.org/10.5281/zenodo.7102472) for all day and night orbits of each day between 13 January and 30 April 2022. The indicated times are those of the intersection of the orbits with the equator. The upper chart of each view is a daily composite of the day orbits and the lower chart is a daily composite of the night orbits. When two orbit swaths overlap, the crossing time of the overlapped orbit is indicated in red. Missing orbits are blanked out. Several days are entirely missing between 8 and 14 March.</p>
SunspotsYoloDataset: annotated solar images captured with smart telescopes (January 2023 - May 2024)
<p><strong>SunspotsYoloDataset</strong> is a set of 1690+380+128 high-resolution RGB astronomical images captured with smart telescopes with specific solar filters and annotated with the positions of sunspots that are effectively in the images. Two instruments were used for several months from Luxembourg and France between January 2023 and May 2024: a Stellina smart telescope (<a href="https://vaonis.com/stellina">https://vaonis.com/stellina</a>) and a Vespera smart telescope (<a href="https://vaonis.com/vespera">https://vaonis.com/vespera</a>).</p> <p><strong>SunspotsYoloDataset</strong> can be used to train YOLO detection models on solar images, enabling the prediction of unexpected events such as Borealis Aurora with astronomical equipment accessible to the public.</p> <p><strong>SunspotsYoloDataset</strong> is formatted with the YOLO standard, i.e., with separated files for images and annotations, usable by state-of-the-art training tools and graphical software like MakeSense (<a href="https://www.makesense.ai">https://www.makesense.ai</a>). More precisely, there is a ZIP file containing RGB images in JPEG format (minimal compression), and text files containing the positions of sunspots. Each RGB image has a resolution of 640 × 640 pixels.</p> <p>For more details about the dataset, please contact the author: olivier.parisot@list.lu .</p> <p>For more information about Luxembourg of Science and Technology (LIST), please consult: <a href="https://www.list.lu">https://www.list.lu</a> .</p> <p> </p>
Temperature Profiles from the Eastern Tropical Pacific (0-300m) from January-February 2023
<p>Vertical temperature profiles taken as part of a research expedition to Clipperton Atoll. Water column temperature profiles were measured down to 300m depth by deploying a <em>RBRduet<sup>3</sup> T.D.</em> sensor<sup> </sup>(Range -5°C to 35°C; Initial accuracy ±0.002°C; Resolution <0.00005°C; time constant <1s). Data (downcast and upcast) is averaged by depth into 1 meter bins, and the standard deviation and number of measurements in each bin are included as columns in the data.</p> <p> </p> <p>This data is supplemented locally for the shallow waters of Clipperton Atoll with 21 water column profiles measured using a Mares Puck Pro dive computer (Range: -10 °C to +50 °C; Resolution: 1°C; Accuracy: ± 2 °C) worn by one of the expedition divers.</p>
[DATA_SCIENCE] Interviews PomBase Users, January-February 2016
<p>Here you find the transcripts of interviews collected by Sabina Leonelli as part of the ERC project "The Epistemology of Data-Intensive Science". You also find the information sheet provided to interviewees, which gives you the context for this project. Further information and related publications can be found at www.datastudies.eu. One paper that specifically makes use of these interviews was published by Sabina Leonelli in the journal Philosophy of Science in 2018, under the title "Data in Time: Time-Scales of Data Use in the Life Sciences." The transcripts document yeast researchers' attitudes to data curation and the use of databases in their field. Researchers have consented to have these transcripts made available as Open Data. Other interviewees did not give consent, so those transcripts are held securely by the research team in Exeter.</p>
"Bom Batuque, Ilê Aiyê" percussion break at Ilê Aiyê's Beleza Negra, January 28, 2023. Senzala do Barro Preto. Salvador, Brazil.
<p>The clip shows the percussion break that took place during "Bom Batuque, Ilê Aiyê" as performed by Ilê Aiyê's annual Beleza Negra on January 28, 2023, led by Mestre Mário Pam. Video by author, Cody Case, with permission from Ilê Aiyê who possesses all videos. This fieldwork footage was funded by a Fulbright-Hays DDRA fellowship and received IRB and Brazil Ethics committee approval to record videos of public performances, including bloco authorization provided by founder and president Antônio Carlos dos Santos for research purposes. </p>
EOL Dynamic Hierarchy Trunk (trunk): EOL Dynamic Hierarchy Trunk January 2021
This is the trunk for the EOL reference hierarchy. It determines the relationships among the higher taxa and adds a few taxa that are not covered by other resources. The EOL DH trunk is maintained in [TTT](<p></p>http://ttt.biodinfo.org/) developed by Colin (Congtian Lin) and Jiangning Wang from Biodiversity Informatics Group of the Institute of Zoology, Chinese Academy of Sciences. ##References Adl, S. M., et al. 2019. Revisions to the classification, nomenclature, and diversity of eukaryotes. Journal of Eukaryotic Microbiology 66, 4–119. <p></p>https://doi.org/10.1111/jeu.12691 Aguiar, A.P., Deans, A.R., Engel, M.S., Forshage, M., Huber, J.T., Jennings, J.T., Johnson, N.F., Lelej, A.S., Longino, J.T., Lohrmann, V., Mikó, I., Ohl, M., Rasmussen, C., Taeger, A., Yu, D.S.K., 2013. Order Hymenoptera . In : Zhang, Z.-Q. (Ed.) Animal Biodiversity: An Outline of Higher-level Classification and Survey of Taxonomic Richness (Addenda 2013). Zootaxa 3703, 51–62. <p></p>https://doi.org/10.11646/zootaxa.3703.1.12 Aspöck, U., Haring, E., Aspöck, H., 2012. The phylogeny of the Neuropterida: long lasting and current controversies and challenges (Insecta: Endopterygota). Arthropod Systematics & Phylogeny 70, 119–129. Benton, M., 2014. Vertebrate Palaeontology. John Wiley & Sons. Betancur-R, R., Wiley, E.O., Arratia, G., Acero, A., Bailly, N., Miya, M., Lecointre, G., Ortí, G., 2017. Phylogenetic classification of bony fishes. BMC Evolutionary Biology 17, 162. <p></p>https://doi.org/10.1186/s12862-017-0958-3 Bleidorn, Christoph. 2019. Recent Progress in Reconstructing Lophotrochozoan (Spiralian) Phylogeny." Organisms Diversity & Evolution 19, no. 4 (December 1, 2019): 557–66. <p></p>https://doi.org/10.1007/s13127-019-00412-4. Bouchard, P., Bousquet, Y., Davies, A., Alonso-Zarazaga, M., Lawrence, J., Lyal, C., Newton, A., Reid, C., Schmitt, M., Slipinski, A., Smith, A., 2011. Family-Group Names In Coleoptera (Insecta). ZooKeys 88, 1–972. <p></p>https://doi.org/10.3897/zookeys.88.807 Cannon, Johanna Taylor, Bruno Cossermelli Vellutini, Julian Smith, Fredrik Ronquist, Ulf Jondelius, and Andreas Hejnol. 2016. Xenacoelomorpha Is the Sister Group to Nephrozoa. Nature 530(7588):89–93. <p></p>https://doi.org/10.1038/nature16520. Davis, R.B., Baldauf, S.L., Mayhew, P.J., 2010. The origins of species richness in the Hymenoptera: insights from a family-level supertree. BMC Evolutionary Biology 10, 109. <p></p>https://doi.org/10.1186/1471-2148-10-109 Dunlop, J. A., Penney, D. & Jekel, D. 2015. A summary list of fossil spiders and their relatives. In World Spider Catalog. Natural History Museum Bern, online at <p></p>http://wsc.nmbe.ch Dunn, C.W., Giribet, G., Edgecombe, G.D., Hejnol, A., 2014. Animal Phylogeny and Its Evolutionary Implications. Annu. Rev. Ecol. Evol. Syst. 45, 371–395. <p></p>https://doi.org/10.1146/annurev-ecolsys-120213-091627 Foottit, R. G., Adler, P. H., eds. 2017. Insect Biodiversity: Science and Society, Volume 1 & 2. 2nd Edition. Wiley-Blackwell. Fritz, U., Havaš, P., 2013. Order Testudines: 2013 update. In : Zhang, Z.-Q. (Ed.) Animal Biodiversity: An Outline of Higher-level Classification and Survey of Taxonomic Richness (Addenda 2013). Zootaxa 3703, 12–14. <p></p>https://doi.org/10.11646/zootaxa.3703.1.4 Giribet, Gonzalo. 2016. New Animal Phylogeny: Future Challenges for Animal Phylogeny in the Age of Phylogenomics. Organisms Diversity & Evolution 16 (2):419–26. <p></p>https://doi.org/10.1007/s13127-015-0236-4. Giribet, Gonzalo, and Gregory D. Edgecombe. 2020. The Invertebrate Tree of Life. Princeton, United States: Princeton University Press, 2020. Guy, L., Ettema, T.J.G., 2011. The archaeal __TACK__ superphylum and the origin of eukaryotes. Trends in Microbiology 19, 580–587. <p></p>https://doi.org/10.1016/j.tim.2011.09.002 Hinchliff, C.E., Smith, S.A., Allman, J.F., Burleigh, J.G., Chaudhary, R., Coghill, L.M., Crandall, K.A., Deng, J., Drew, B.T., Gazis, R., Gude, K., Hibbett, D.S., Katz, L.A., Laughinghouse, H.D., McTavish, E.J., Midford, P.E., Owen, C.L., Ree, R.H., Rees, J.A., Soltis, D.E., Williams, T., Cranston, K.A., 2015. Synthesis of phylogeny and taxonomy into a comprehensive tree of life. PNAS 112, 12764–12769. <p></p>https://doi.org/10.1073/pnas.1423041112 Holzenthal, R.W., Morse, J.C., Kjer, K.M., 2011. Order Trichoptera Kirby, 1813. In: Zhang, Z.-Q. (Ed.) Animal biodiversity: An outline of higher-level classification and survey of taxonomic richness. Zootaxa 3148, 209. <p></p>https://doi.org/10.11646/zootaxa.3148.1.40 Hormiga, G., Griswold, C.E., 2014. Systematics, Phylogeny, and Evolution of Orb-Weaving Spiders. Annu. Rev. Entomol. 59, 487–512. <p></p>https://doi.org/10.1146/annurev-ento-011613-162046 James, S.W., Davidson, S.K., 2012. Molecular phylogeny of earthworms (Annelida:Crassiclitellata) based on 28S, 18S and 16S gene sequences. Invertebrate Systematics 26, 213. <p></p>https://doi.org/10.1071/IS11012 Jarvis, E.D., Mirarab, S., Aberer, A.J., Li, B., Houde, P., Li, C., Ho, S.Y.W., Faircloth, B.C., Nabholz, B., Howard, J.T., Suh, A., Weber, C.C., Fonseca, R.R. da, Li, J., Zhang, F., Li, H., Zhou, L., Narula, N., Liu, L., Ganapathy, G., Boussau, B., Bayzid, M.S., Zavidovych, V., Subramanian, S., Gabaldón, T., Capella-Gutiérrez, S., Huerta-Cepas, J., Rekepalli, B., Munch, K., Schierup, M., Lindow, B., Warren, W.C., Ray, D., Green, R.E., Bruford, M.W., Zhan, X., Dixon, A., Li, S., Li, N., Huang, Y., Derryberry, E.P., Bertelsen, M.F., Sheldon, F.H., Brumfield, R.T., Mello, C.V., Lovell, P.V., Wirthlin, M., Schneider, M.P.C., Prosdocimi, F., Samaniego, J.A., Velazquez, A.M.V., Alfaro-Núñez, A., Campos, P.F., Petersen, B., Sicheritz-Ponten, T., Pas, A., Bailey, T., Scofield, P., Bunce, M., Lambert, D.M., Zhou, Q., Perelman, P., Driskell, A.C., Shapiro, B., Xiong, Z., Zeng, Y., Liu, S., Li, Z., Liu, B., Wu, K., Xiao, J., Yinqi, X., Zheng, Q., Zhang, Y., Yang, H., Wang, J., Smeds, L., Rheindt, F.E., Braun, M., Fjeldsa, J., Orlando, L., Barker, F.K., Jønsson, K.A., Johnson, W., Koepfli, K.-P., O__Brien, S., Haussler, D., Ryder, O.A., Rahbek, C., Willerslev, E., Graves, G.R., Glenn, T.C., McCormack, J., Burt, D., Ellegren, H., Alström, P., Edwards, S.V., Stamatakis, A., Mindell, D.P., Cracraft, J., Braun, E.L., Warnow, T., Jun, W., Gilbert, M.T.P., Zhang, G., 2014. Whole-genome analyses resolve early branches in the tree of life of modern birds. Science 346, 1320–1331. <p></p>https://doi.org/10.1126/science.1253451 Kathirithamby, J., Engel, M.S., 2014. A Revised Key to the Living and Fossil Families of Strepsiptera, with the Description of a New Family, Cretostylopidae. Journal of the Kansas Entomological Society 87, 385–388. <p></p>https://doi.org/10.2317/JKES140407.1 Kjer, K.M., Simon, C., Yavorskaya, M., Beutel, R.G., 2016. Progress, pitfalls and parallel universes: a history of insect phylogenetics. Journal of The Royal Society Interface 13, 20160363. <p></p>https://doi.org/10.1098/rsif.2016.0363 Klopfstein, S., Vilhelmsen, L., Heraty, J.M., Sharkey, M., Ronquist, F., 2013. The Hymenopteran Tree of Life: Evidence from Protein-Coding Genes and Objectively Aligned Ribosomal Data. PLoS ONE 8, e69344. <p></p>https://doi.org/10.1371/journal.pone.0069344 Kocot, Kevin M., Torsten H. Struck, Julia Merkel, Damien S. Waits, Christiane Todt, Pamela M. Brannock, David A. Weese, et al. 2016. Phylogenomics of Lophotrochozoa with Consideration of Systematic Error. Systematic Biology, syw079. <p></p>https://doi.org/10.1093/sysbio/syw079. Laumer, Christopher E., Rosa Fernández, Sarah Lemer, David Combosch, Kevin M. Kocot, Ana Riesgo, Sónia C. S. Andrade, Wolfgang Sterrer, Martin V. Sørensen, and Gonzalo Giribet. 2019. Revisiting Metazoan Phylogeny with Genomic Sampling of All Phyla. Proceedings of the Royal Society B: Biological Sciences 286 (1906): 20190831. <p></p>https://doi.org/10.1098/rspb.2019.0831. Laumer, Christopher E., Nicolas Bekkouche, Alexandra Kerbl, Freya Goetz, Ricardo C. Neves, Martin V. Sørensen, Reinhardt M. Kristensen, et al. 2015. Spiralian Phylogeny Informs the Evolution of Microscopic Lineages. Current Biology 25(15): 2000–2006. <p></p>https://doi.org/10.1016/j.cub.2015.06.068. Leschen, R.A.B., Beutel, R.G., 2014. Morphology and Systematics: Phytophaga. Walter de Gruyter. Li, H., Shao, R., Song, N., Song, F., Jiang, P., Li, Z., Cai, W., 2015. Higher-level phylogeny of paraneopteran insects inferred from mitochondrial genome sequences. Scientific Reports 5. <p></p>https://doi.org/10.1038/srep08527 Lozano-Fernandez, J., Tanner, A.R., Giacomelli, M., Carton, R., Vinther, J., Edgecombe, G.D., Pisani, D., 2019. Increasing species sampling in chelicerate genomic-scale datasets provides support for monophyly of Acari and Arachnida. Nature Communications 10, 2295. <p></p>https://doi.org/10.1038/s41467-019-10244-7 Malm, T., Nyman, T., 2015. Phylogeny of the symphytan grade of Hymenoptera: new pieces into the old jigsaw(fly) puzzle. Cladistics 31, 1–17. <p></p>https://doi.org/10.1111/cla.12069 Marlétaz, Ferdinand, Katja T. C. A. Peijnenburg, Taichiro Goto, Noriyuki Satoh, and Daniel S. Rokhsar. 2019. A New Spiralian Phylogeny Places the Enigmatic Arrow Worms among Gnathiferans. Current Biology 29(2):312-318.e3. <p></p>https://doi.org/10.1016/j.cub.2018.11.042. Nakano, T., Ramlah, Z., Hikida, T., 2012. Phylogenetic position of gastrostomobdellid leeches (Hirudinida, Arhynchobdellida, Erpobdelliformes) and a new family for the genus Orobdella. Zoologica Scripta 41, 177–185. <p></p>https://doi.org/10.1111/j.1463-6409.2011.00506.x Naylor, G.J.P., Caira, J.N., Jensen, K.R.E., Rosana, K.M., Straube, N., Lakner, C., 2012. Elasmobranch Phylogeny: A Mitochondrial Estimate Based on 595 Species. In J.C. Carrier, J.A. Musick and M.R. Heithaus (editors), The Biology of Sharks and Their Relatives. 31-56. CRC Press, Taylor & Francis Group. Nesbitt, S.J., 2011. The Early Evolution of Archosaurs: Relationships and the Origin of Major Clades. Bulletin of the American Museum of Natural History, 2011(352):1-292. <p></p>https://doi.org/10.1206/352.1 Nesnidal, Maximilian P., Martin Helmkampf, Achim Meyer, Alexander Witek, Iris Bruchhaus, Ingo Ebersberger, Thomas Hankeln, Bernhard Lieb, Torsten H. Struck, and Bernhard Hausdorf. 2013. New Phylogenomic Data Support the Monophyly of Lophophorata and an Ectoproct-Phoronid Clade and Indicate That Polyzoa and Kryptrochozoa Are Caused by Systematic Bias. BMC Evolutionary Biology 13(1): 253. <p></p>https://doi.org/10.1186/1471-2148-13-253. Oaks, J.R., 2011. A Time-Calibrated Species Tree of Crocodylia Reveals a Recent Radiation of the True Crocodiles. Evolution 65, 3285–3297. <p></p>https://doi.org/10.1111/j.1558-5646.2011.01373.x Okamura, B., Gruhl, A., Reft, A.J., 2015. Cnidarian Origins of the Myxozoa, in: Okamura, B., Gruhl, A., Bartholomew, J.L. (Eds.), Myxozoan Evolution, Ecology and Development. Springer International Publishing, Cham, pp. 45–68. <p></p>https://doi.org/10.1007/978-3-319-14753-6_3 Pyron, R.A., Burbrink, F.T., Wiens, J.J., 2013. A phylogeny and revised classification of Squamata, including 4161 species of lizards and snakes. BMC Evolutionary Biology 13, 93. <p></p>https://doi.org/10.1186/1471-2148-13-93 Robertson, J.A., Ślipiński, A., Moulton, M., Shockley, F.W., Giorgi, A., Lord, N.P., Mckenna, D.D., Tomaszewska, W., Forrester, J., Miller, K.B., Whiting, M.F., Mchugh, J.V., 2015. Phylogeny and classification of Cucujoidea and the recognition of a new superfamily Coccinelloidea (Coleoptera: Cucujiformia): Systematics of Cucujoidea and Coccinelloidea. Systematic Entomology 40, 745–778. <p></p>https://doi.org/10.1111/syen.12138 Rouse, Greg W., Nerida G. Wilson, Jose I. Carvajal, and Robert C. Vrijenhoek. 2016. New Deep-Sea Species of Xenoturbella and the Position of Xenacoelomorpha. Nature 530(7588):94–97. <p></p>https://doi.org/10.1038/nature16545. Ruhfel, B.R., Gitzendanner, M.A., Soltis, P.S., Soltis, D.E., Burleigh, J.G., 2014. From algae to angiosperms–inferring the phylogeny of green plants (Viridiplantae) from 360 plastid genomes. BMC Evolutionary Biology 14, 23. <p></p>https://doi.org/10.1186/1471-2148-14-23 Schiffer, Philipp H., Helen E. Robertson, and Maximilian J. Telford. 2018. Orthonectids Are Highly Degenerate Annelid Worms. Current Biology 28(12):1970-1974.e3. <p></p>https://doi.org/10.1016/j.cub.2018.04.088. The Angiosperm Phylogeny Group, 2016. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG IV. Bot. J. Linn. Soc. 181, 1–20. <p></p>https://doi.org/10.1111/boj.12385 Van Nieukerken, E.J., Kaila, L., Kitching, I.J., Kristensen, N.P., Lees, D.C., Minet, J., Mitter, C., Mutanen, M., Regier, J.C., Simonsen, T.J., Wahlberg, N., Yen, S.-H., Zahiri, R., Adamski, D., Baixeras, J., Bartsch, D., Bengtsson, B.Å., Brown, J.W., Bucheli, S.R., Davis, D.R., Prins, J.D., Prins, W.D., Epstein, M.E., Gentili-Poole, P., Gielis, C., Hättenschwiler, P., Hausmann, A., Holloway, J.D., Kallies, A., Karsholt, O., Kawahara, A.Y., Koster, S.J.C., Kozlov, M.V., Lafontaine, J.D., Lamas, G., Landry, J.-F., Lee, S., Nuss, M., Park, K.-T., Penz, C., Rota, J., Schintlmeister, A., Schmidt, B.C., Sohn, J.-C., Solis, M.A., Tarmann, G.M., Warren, A.D., Weller, S., Yakovlev, R.V., Zolotuhin, V.V., Zwick, A., 2011. Order Lepidoptera Linnaeus, 1758. In: Zhang, Z.-Q. (Ed.) Animal biodiversity: An outline of higher-level classification and survey of taxonomic richness. Zootaxa 3148, 212. <p></p>https://doi.org/10.11646/zootaxa.3148.1.41 Vea, I.M., Grimaldi, D.A., 2015. Diverse New Scale Insects (Hemiptera: Coccoidea) in Amber from the Cretaceous and Eocene with a Phylogenetic Framework for Fossil Coccoidea. novi 2015, 1–15. <p></p>https://doi.org/10.1206/3823.1 Vélez-Zuazo, X., Agnarsson, I., 2011. Shark tales: A molecular species-level phylogeny of sharks (Selachimorpha, Chondrichthyes). Molecular Phylogenetics and Evolution 58, 207–217. <p></p>https://doi.org/10.1016/j.ympev.2010.11.018 Weigert, A., Bleidorn, C., 2016. Current status of annelid phylogeny. Org Divers Evol 16, 345–362. <p></p>https://doi.org/10.1007/s13127-016-0265-7 Weirauch, C., Schuh, R.T., 2011. Systematics and Evolution of Heteroptera: 25 Years of Progress. Annu. Rev. Entomol. 56, 487–510. <p></p>https://doi.org/10.1146/annurev-ento-120709-144833 Wiegmann, B.M., Trautwein, M.D., Winkler, I.S., Barr, N.B., Kim, J.-W., Lambkin, C., Bertone, M.A., Cassel, B.K., Bayless, K.M., Heimberg, A.M., Wheeler, B.M., Peterson, K.J., Pape, T., Sinclair, B.J., Skevington, J.H., Blagoderov, V., Caravas, J., Kutty, S.N., Schmidt-Ott, U., Kampmeier, G.E., Thompson, F.C., Grimaldi, D.A., Beckenbach, A.T., Courtney, G.W., Friedrich, M., Meier, R., Yeates, D.K., 2011. Episodic radiations in the fly tree of life. Proceedings of the National Academy of Sciences 108, 5690–5695. <p></p>https://doi.org/10.1073/pnas.1012675108 Winterton, S.L., Hardy, N.B., Wiegmann, B.M., 2010. On wings of lace: phylogeny and Bayesian divergence time estimates of Neuropterida (Insecta) based on morphological and molecular data. Systematic Entomology 35, 349–378. <p></p>https://doi.org/10.1111/j.1365-3113.2010.00521.x Yuri, T., Kimball, R.T., Harshman, J., Bowie, R.C.K., Braun, M.J., Chojnowski, J.L., Han, K.-L., Hackett, S.J., Huddleston, C.J., Moore, W.S., Reddy, S., Sheldon, F.H., Steadman, D.W., Witt, C.C., Braun, E.L., 2013. Parsimony and Model-Based Analyses of Indels in Avian Nuclear Genes Reveal Congruent and Incongruent Phylogenetic Signals. Biology 2, 419–444. <p></p>https://doi.org/10.3390/biology2010419 Zverkov, Oleg A., Kirill V. Mikhailov, Sergey V. Isaev, Leonid Y. Rusin, Olga V. Popova, Maria D. Logacheva, Alexey A. Penin, et al. 2019. Dicyemida and Orthonectida: Two Stories of Body Plan Simplification. Frontiers in Genetics 10. <p></p>https://doi.org/10.3389/fgene.2019.00443.<p></p>The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
EOL Dynamic Hierarchy, digest form, January 2019: Dynamic hierarchy digest January 2019
__Digest__ of the EOL dynamic hierarchy prepared by querying the online traits database. Unlike the full DH files, this file provides only the bare minimum information for each taxon: page id, parent page id, canonical name.<p></p>Zip file containing a README and a three-column CSV (pages.csv) with header row
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output with snow cover January 16 2020 0000 UTC to January 17 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 16 and January 17 2020 with snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured ideal condition for cold-air pool formation followed by a disturbance around midnight. There is also an upload with simulation output for the same night, but without snow cover (Rauchöcker et al., 2024a). Also available in a different dataset are data from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period.</p> <h3><strong>WRF Simulation Output</strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 16 2020 and run until 12:00 UTC January 17 2020.</p> <p>Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the two simulation with snow cover, where <em>jan126_sms.zip</em> contains the files relating to the simulations with the SMS-3DTKE scheme and <em>jan16.zip</em> those for the simulation with the MYNN 2.5-order PBL parameterization. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the files <em>namelist.input</em> and <em>namelist_sms.input</em> that were used for the simulations. </p> <p>Standard WRF output can be found in <em>wrfout_40m_jan16</em> and <em>wrfout_40m_jan16_sms</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan16</em> and <em>windout_40m_jan16_sms</em>. These variables were contained in the unprocessed output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan16.nc</em> and <em>tend_40m_jan16_sms.nc</em>.</p> <h3><strong>Plotting routines</strong></h3> <p>Python scripts and environment files to reproduce most figures in Rauchoecker et al. (2024d) are included in <em>code.zip</em>. To reproduce plots involving measurement data, which is available in Rauchöcker et al. (2024c), is also needed.</p> <p>Due to conflicts between some packages, two different environment were needed. To reproduce Figure 2, install the <em>orthoplot</em> environment by running "<em>conda env create orthoplot.yml</em>" in a terminal window, activate it ("<em>conda activate orthoplot</em>") and then run <em>ortho_plot.py</em>. All other plots require the wrfstuff environent (installed by running "<em>conda env create wrfstuff.yml</em>" and activated by "<em>conda activate wrfstuff</em>") and are produced by <em>paper_plots.py</em>. Functions used to load data and plot the figures are included in <em>dataload.py</em> and <em>plotting_routines.py</em>, respectively<em>.</em></p> <p>Two variables decide which figures are plotted for which dataset: <em>dataname</em> and <em>doplot</em>. The variable <em>dataname</em> defines the path to the <em>dataset</em> that should be used to produce the figures, while the value <em>doplot</em> defines which figure to reproduce. By setting doplot="fig1", Figure 1 is reproduced, while doplot="fig7" and doplot="fig9" reproduce Figures 7 and 9, respectively. For all other values for <em>doplot</em>, Figures 4, 5, 6, 8 and 11 are reproduced. We decided not to include a script to plot Figure 3 because the data the climatology is based on is owned by GeoSphere Austria - the agency operating the permanent weather station. Further, no script for reproducing Figure 10 is included because it was not created within the framework of Python.</p> <h3><strong>Geofiles</strong></h3> <p>The files included in <em>g</em><em>eofiles.zip</em> are needed to plot Figure 1 and Figure 2, although not of particular interest on their own. Included are output files from <em>geogrid.exe</em>, whicih are necessary to plot the domains overview (Figure 1), as well as an orthophoto (<em>orthophoto.tif</em>) and high-resolution digital elevation model (<em>topo_hr.tif</em>) which are both based on data from Land Tirol.</p>
High-quality NEID Solar Observations (January 2021 - June 2022)
<p>NExScI archives all NEID Solar observations at <a href="https://neid.ipac.caltech.edu/search_solar.php">https://neid.ipac.caltech.edu/search_solar.php</a>. NEID takes data even during inclement weather, and the official <a href="https://neid.ipac.caltech.edu/docs/NEID-DRP/index.html">NEID Data Reduction Pipeline</a> does not differentiate between those taken in excellent or poor observing conditions. This CSV file contains a list of all NEID Solar observations from January 1, 2021 to June 13, 2022, identifies which are currently believed to be of high-quality, and provides diagnostic information used (so users can easily adapting the selection criterion for their needs).</p> <p><strong>Columns:</strong></p> <p>filename: Name of file as provided by NExScI</p> <p>bjd: Barycentric Julian Date</p> <p>mask: true for observations considered of high quality; false for observations suspected to be low quality; See selection criterion specified below</p> <p>pyrflux_mean: mean flux observed by pyrheliometer during exposure</p> <p>pyrflux_rms: root mean square deviation from mean of flux observed by pyrheliometer during exposure</p> <p>expmeter_mean: mean of exposure meter during exposure (summed over wavelength channels)</p> <p>expmeter_rms: root mean square deviation from mean of exposure meter during exposure (summed over wavelength channels)</p> <p>airmass: airmass of sun a time of observation</p> <p>hour_angle: hour angle of sun at time of observation</p> <p>driftfun: value of DRIFTFUN copied from FITS header of filename</p> <p>wavecal: value of WAVECAL copied from FITS header of filename</p> <p>expmeter_mean_blue: mean of exposure meter during exposure (summed over bluest third of wavelength channels)</p> <p>expmeter_mean_green: mean of exposure meter during exposure (summed over middle third of wavelength channels)</p> <p>expmeter_mean_red: mean of exposure meter during exposure (summed over reddest third of wavelength channels)</p> <p> </p> <p>Selection criteria for setting mask to true:</p> <p>1. Require E_VER from fits file matches v1.1.* (Data processed with common minor version of NEID DRP)</p> <p>2. Require driftfun == "dailymodel0" and wavecal == "LFCplusThAr" (Wavelength calibration used)</p> <p>3. Start time of exposure lies between 17:30:00 and 22:12:00 (Removes data taken while wavelength calibration is changing rapidly)</p> <p>4. Exclude dates: October 2, 2021 to October 27, 2021 (due to a cabling issue)</p> <p>5. Require airmass <= 2.25 (Exclude data taken at high airmass)</p> <p>6. Require mean_pyroflux >= 10^2.95 (Require sufficient flux reaching the pyrheliometer)</p> <p>7. Require expmeter_mean >= 1.0e5 (Require sufficient flux reaching the exposure meter)</p> <p>8. Require rms_pyroflux <= 0.0035 * mean_pyroflux (Steady atmospheric transparency)</p> <p>9. Require expmeter_rms <= 0.003 * expmeter_mean (Steady transparency & pointing)</p> <p>10. Require expmeter_mean >= 150 * pyrflux_mean (Good pointing)</p> <p>11. Exclude dates: June 6, 2021, June 16, 2021, July 7, 2021, July 18, 2021, and July 19, 2021 (Dates with poor wavelength calibrations. This list may be updated upon further analysis.)</p>
Measurements from CalCOFI cruises in the California Current System, including log of station information, weather, sea conditions as well as physical, chemical and biological measurements including including temperature, salinity, oxygen, density, sigma theta, phosphate, silicate, nitrite, nitrate, ammonia, chlorophyll a, integrated chlorophyll a, primary productivity, and integrated primary production. 1949 - January 2020
Since 1949, hydrographic and biological data of the California Current System have been collected on quarterly CalCOFI cruises. The 59+ year hydrographic time-series includes weather, temperature, salinity, oxygen and phosphate observations. In 1961, nutrient analysis expanded to include silicate, nitrate and nitrite; in 1973, chlorophyll was added; in 1984, C14 primary productivity incubations were added. These data are being provided here in collaboration with CalCOFI-SIO in order to provide an additional queriable interface to the data. The data are updated on a regular basis from the CalCOFI hydrographic database.
Leaf litter, soil, and periphyton gene expression along freshwater to marine gradients in Everglades National Park (FCE LTER), Florida, USA, January 2021 and April 2021
We collected leaf litter, periphyton, and soil along freshwater to marine gradients at SRS-2, SRS-4, SRS-6, TS/PH-2, TS/Ph-3, TS/Ph-7a, and TS/Ph-10. Samples were collected in January and April of 2021 to understand how microbial communities respond to and influence the breakdown of organic matter along freshwater to marine transects. Data collection for this project is complete. For each site and litter pair we collected a subset of 2-3 g wet mass of litter, a grab sample of soil, and a grab sample of periphyton for each site. All subsamples were preserved at -20°C until extraction, which took place up to a year after initial collection. Samples were sent to Novogene (Novogene Co. Ltd., Beijing, China) for the total RNA extraction followed by metatranscriptome sequencing. We selected n = 12 genes/gene families encoding for focal enzymes to investigate which are important to the breakdown of organic matter: Dioxygenases (associated with aerobic respiration), Sulfatases (associated with the release of sulfates from complex molecules), sulfite reductases (associated with sulfite reduction), methyl coenzyme M reductase and formylmethanofuran (associated with methanogenesis), nitrite reductases (associated with nitrite reduction), cellobiosidase, glucosidase, and xylosidase (associated with cellulose breakdown), phenol oxidase (associated with lignin breakdown), acid phosphatase (associated with phosphate acquisition in acidic environments), and alkaline phosphatase (associated with phosphate acquisition in basic environments). For each gene/family of interest, we searched all annotated transcripts for all entries corresponding to that gene/family and combined all values for a total expression. We selected n = 6 monophyletic microbial functional groups, representing sulfate reducers, sulfate oxidizers, methane oxidizers, methanogens, nitrite oxidizers, and ammonia oxidizers associated with sulfate and methane cycling. We filtered all annotated transcripts for all specie
Mammal species recorded within the Hubbard Brook Experimental Forest and vicinity (1963-2020; updated January 2021).
This dataset contains confirmed observations of mammal species at the Hubbard Brook Experimental Forest and adjacent Mirror Lake. The original list was published in Holmes, R. T. and G. E. Likens. 1999. Organisms of the Hubbard Brook Valley, New Hampshire. USDA Forest Service, Northeastern Research Station, General Tech. Report NE-257. 32 pp. The list is updated here (January 2021) to include additional species observed since the original publication, with annotated comments by R.T. Holmes, H. ter Hofstede (bats) and L. Christenson (from motion-detecting cameras, 2014-2019).
Measurements of height, diameter at breast height and basal diameter for Prestoea acuminata at the Luquillo Forest Dynamics Plot (LFDP), Puerto Rico in January 2020.
Data were collected in January 2020 in Puerto Rico, within and outside the boundaries of LFDP. For each of these palms, we measured stem height from the ground to the base of the crown (Hbc; height of the youngest internode), diameter at 130 cm above ground (D130), and basal diameter (DB; just above the top of the roots). These are the data which accompany the publication: Height-diameter allometry for a dominant palm to improve understanding of carbon and forest dynamics in forests of Puerto Rico.
Hyporheic sediment characteristics from Von Guerard Stream, Taylor Valley, McMurdo Dry Valleys, Antarctica in January 2019
In this data package, we present chlorophyll concentrations, percent loss-on-ignition organic matter, sorbed ammonium concentrations, and percent biogenic silica for hyporheic sediments collected in January 2019 from nine transects across Von Guerard Stream, Taylor Valley, Antarctica. These samples were collected to address questions about the retention and processing of particulate organic matter in the hyporheic zone of McMurdo Dry Valley streams. The nine transects were located at pools, riffles, and meanders (three of each geomorphology type) along Von Guerard Stream and extended across the stream channel to the edges of the wetted zone, ranging from 6.6 to 13.6 m in length. At each sampling location, we collected subsurface bulk sediment down to a depth of 10 cm. We analyzed the sediment samples for chlorophyll-a, phaeophytin, loss-on-ignition, ammonium sorbed to the sediment, and the UV absorbance at 254 nm of sediment extractions. This data package is associated with a complementary data package that contains diatom community assemblages for the same samples.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.