cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.600659
title: Signal, Uncertainty, And Conflict In Phylogenomic Data For A Diverse Lineage Of Microbial Eukaryotes (Diatoms, Bacillariophyta)

description: This data depository contains analysis results from Parks, Wickett, and Alverson 2017 (Signal, Uncertainty, and Conflict in Phylogenomic Data for a Diverse Lineage of Microbial Eukaryotes (Diatoms, Bacillariophyta) (Mol. Biol. Evol. doi:10.1093/molbev/msx268)), and is made freely available to the research community.

The file and subfolders here are as follows:

gene_alignments<br>
- contains compressed (tarred and gzipped) folders with all gene alignments at 0.2, 0.5 and 0.8 alignment column occupancy cutoffs. In each folder, there are also text files listing which gene alignments fall under which taxon occupancy subsetting strategy (i.e., 10-20% taxon occupancy, 40-60% taxon occupancy, 80-100% taxon occupancy, etc).

gene_trees<br>
- contains all (compressed) gene trees (bootstrapped versions, 100 bootstrap pseudo-replicates)) used in Astral analyses for each alignment column occupancy cutoff (0.2, 0.5, 0.8); nodes with less than 33% bootstrap support are collapsed.

hmms.mafft_aligned<br>
- contains (compressed) hmm specifications for each major diatom morphotype (radial and polar centrics, araphid and raphid pennates) from the 0.2 alignment column occupancy subset of the data. A summary of the sampling scheme and the hmm results/counts are also available in HMM_sampling.docx.

mmetsp_nuclear_transcriptome_assemblies<br>
- these are the compressed nuclear transcriptome assemblies that were done in-house (i.e., mostly MMETSP samples). Assemblies do not include organellar or rDNA loci.

species_trees<br>
- contains (compressed) species trees for all phylogenetic strategies and alignment column occupancy cutoff/data subset strategies.

Suppl_1.MMETSP_basic_summaries.xlsx<br>
- this an identical file to Parks, Wickett and Alverson 2017 supplementary file 1. This file contains taxon, strain and SRA information for all assembled taxa, and a variety of assembly metric information.
description: Funded in part by:
NSF DEB-1353131
NSF DEB-1353152
NSF DEB-1239992
NSF DEB-1342873
Simons Foundation
DOI: 10.14457/bru.res.2018.1
title: title
DOI: 10.14457/bru.the.2017.1
title: ???
DOI: 10.5285/630a847b-b40c-6b0a-e053-6c86abc03974
title: DY029 Phytoplankton and microzooplankton species abundance.

description: This dataset contains phytoplankton and microzooplankton taxa-specific abundance data that were enumerated from profile samples collected in the upper euphotic zone during the Natural Environment Research Council (NERC) Shelf Sea Biogeochemistry (SSB) pelagic cruise DY029 (April 2015) in the Celtic Sea. Samples were preserved in acid Lugol's iodine and analysed using light microscopy by Claire Widdicombe following the British Standard (BS EN 15204: 2006) 'Water quality — Guidance standard on the enumeration of phytoplankton using inverted microscopy (Utermohl technique)'. Where possible all phyto- and microzooplantkon cells &gt;15um were identified to species level and grouped according to six functional groups: DIATOMS, AUTOTROPHIC DINOFLAGELLATES, HETEROTROPHIC DINOFLAGELLATES, CILIATES, FLAGELLATES AND MISCELLANEOUS. Mean cell measurements were used to calculate cell biovolume, assuming appropriate geometric shapes (Olenina et al. 2006) and converted to carbon (pgC cell<sup>-1</sup>) using the equations of Menden-Deuer and Lessard (2000). The author does not have any concerns over the quality of the data set and values are consistent with similar data reported in the wider literature. The data were collected as part of the SSB research programme in the CaNDyFloSS: Carbon and Nutrient Dynamics and Fluxes over Shelf Systems research programme component. SSB was co-funded by the NERC and the Department for Environment, Food and Rural Affairs (Defra). The programme took a holistic approach to the cycling of nutrients and carbon and the controls on primary and secondary production in UK and European shelf seas, to increase understanding of these processes and their role in wider biogeochemical cycles. The British Oceanographic Data Centre (BODC) created the metadata entry and is responsible for holding master copies of the data.
DOI: 10.5061/dryad.2qc10/1
title: Percentage frequency of principal phytolith morphotypes

description: Percentage frequency of principal phytolith morphotypes
DOI: 10.5061/dryad.2qc10/2
title: Supplementary Table 1

description: List of studied grass taxa along with their taxonomic position (After Soreng et al. 2015), collection areas, habitats, and principal phytolith assemblages.
DOI: 10.5061/dryad.2qc10/3
title: Table 2

description: Morphotypes and sub-morphotypes of studied phytoliths with hand drawn illustrations. (Modified after Twiss et al. 1969, Sase and Kondo 1974, Brown 1984, Kondo and Sase 1986, Piperno 1988, Mulholland 1989, Pearsall 1989, Pearsall and Piperno 1990, Mulholland and Rapp 1992, Twiss 1992, Fredlund and Tieszen 1994, Kondo et al. 1994, Piperno and Pearsall 1998, Pearsall 2000, Lu and Liu 2003a, Lu and Liu 2003b, Stromberg 2003, Gallego and Distel 2004, Blinnikov 2005, Madella et al.  2005, Honaine et al. 2006, Piperno 2006, Fahmy 2008, Barboni and Bremond 2009, Mercader et al. 2010, Ghosh et al. 2011, Gu et al. 2013, Biswas et al. 2016, Naskar and Bera 2016, Neumann et al. 2017)
DOI: 10.5061/dryad.2qc10/5
title: Fig 3

description: Selected polylobate sub-morphotypes retrieved from the studied grasses. Light microscopic images (1-13) and scanning electron microscopic images (14-22). (Tri-regular-small - 1. a. Oplismenus compositus, 1.b. Paspalum conjugatum, 1.c. Echinochloa picia, 14.a Brachiaria reptans, 14.b Echinochloa crus-pavonis), (Tri-irregular-small - 2.a Setaria pallide-fusca, 2.a Zea mays), (Tetra-regular-small - 3.a Oplismenus burmanni, 3.b Setaria verticillata), (Tetra-irregular-small - 4. Paspalum scrobiculatum, 15.a Setaria pallide-fusca, 15.b Oplismenus compositus), (Penta-irregular-small - 5.a Oplismenus burmanni, 5.b Apluda mutica), (Hexa-irregular-small – 6, 16.a Themeda arundinacea, 16.b – 16.c Apluda mutica),   (Hepta-irregular small - 7.a and 7.c Apluda mutica,  7.b - 7.d Themeda arundinacea), (Octa-irregular small 8.a - 8.b, 17. Themeda arundinacea, (Tri-regular-medium - 9.Porteresia coarctata, 18.a Echinochloa stagnina, 18.b Digitaria setigera), (Tri-irregular-medium - 19.Setaria viridis), (Tetra-regular-medium - 20. Panicum flavidium), (Tetra-irregular-medium - 10. Paspalum distichum), (Penta-irregular-medium 11. Dichanthium annulatum, 22. Dichanthium caricosum), (Hexa-regular-medium - 12. Oplismenus burmanni), (Tri-regular-large - 13. Digitaria abscendens, 21. Echinochloa frumentacea).
DOI: 10.5061/dryad.2qc10/6
title: Fig 4

description: Selected saddle sub-morphotypes retrieved from the studied grasses. Light microscopic images (1-9) and scanning electron microscopic images (10-18). (Short thin saddle - 1.a Eragrostis coarctata, 1.b Chloris barbata, 1.c Myriostachya wightiana, 1.d Aeluropus lagopoides, 10.a Sporobolus spicatus, 10.b Chloris montana), (Short broad saddle - 2.a Eragrostis atrovirens, 2.b Eragrostis gangetica, 2.c Eleusine indica, 2.d Desmostachya bipinata, 11.a Dactyloctenium aegyptium, 11.b Cynodon dactylon), (Plateau thin saddle - 3.a Thyrsostachys siamensis, 3.b Chloris montana, 12. Sporobolus tremulus), (Plateau broad saddle - 4.a Bambusa wamin, 4.b Sporobolus diander, 13. Phragmites karka), (Long saddle gr.I – 5.a Bambusa vulgaris var. vittata, 5.b Thyrsostachys siamensis, 14. Bambusa ventricosa), (Long saddle gr. II – 6.a Bambusa tulda, 6.b Dendrocalamus giganteus, 15. Dinochloa maclellandii), (Long saddle gr. III – 7.a Bambusa oliveriana, 7.b Bambusa ventricosa, 16. Dendrocalamus giganteus), (Collapsed thin saddle - 8.a Bambusa ventricosa, 8.b Thyrsostachys siamensis, 8.c Bambusa vulgaris var. vittata, 17.a Bambusa tulda, 17.b Bambusa oliveriana), (Collapsed thin saddle 9.a Bambusa oliveriana, 9.b Bambusa wamin, 9.c Dendrocalamus giganteus, 18. Bambusa oliveriana)
DOI: 10.5061/dryad.2qc10/7
title: Fig 5

description: Selected rondel/tower sub-morphotypes retrieved from the studied grasses. Light microscopic images (1-11) and scanning electron microscopic images (12-19). (Flat tower – 1.a Sporobolus spicatus, 1.b Paspalum vaginatum, 12.a Aeluropus lagopoides, 12.b Zoysia matrella), (Two-horned rondel – 2.a-e, 13.a-e), (Three-horned rondel – 3.a-c Porteresia coarctata,  3.d-e Paspalum vaginatum, 3.f  Eragrostis gangetica, 3.g Dichanthium caricosum -14.a Paspalum conjugatum, 14.b Porteresia coarctata), (Four-horned rondel – 4.a-g Porteresia coarctata), (Pyramidal rondel – 5. Coix lacryma-jobi), (Ellipsoid rondel – 6.a Digitaria bicornis, 6.b Sporobolus wallichii, 6.c Porteresia coarctata ), (Keeled rondel – 7.a Eragrostis unioloides, 7.b Eragrostis nutans, 15.a Paspalum distichum, 15.b Chrysopogon aciculatus 15.c Leersia hexandra), (Spool/horned tower – 8.a Paspalum vaginatum, 8.b Porteresia coarctata, 18.a Eragrostis viscosa, 18.b Leptochloa chinensis, 18.c Aeluropus lagopoides ), (Oval rondel – 9.a Thysanolaena maxima, 9.b Arundo donax, 9.c Eragrostis viscosa, 16 Sporobolus diander), (Wavy top rondel – 10 Aeluropus lagopoides, 17 Oryza rufipogon), (Complex tower – 11.a Bambusa ventricosa, 11.b Desmostachya bipinata, 11.c Chloris montana, 19. Bambusa wamin)
DOI: 10.5061/dryad.2qc10/8
title: Fig 6

description: Selected sub-morphotypes of cross, trapeziform, and pooidae class groups, retrieved from the studied grasses. Light microscopic images (1-13) and scanning electron microscopic images (14-22). (Small cross with thin lobes – 2.a Leersia hexandra, 2.b Dichanthium caricosum, 2.c Paspalum distichum, 2.d Porteresia coarctata, 15.a Echinochloa crusgalli, 15.b Panicum repens, (Small cross with thick lobes – 1.a Echinochloa crus-pavonis, 1.b Brachiaria mutica, 1.c Panicum sp., 14.a Pennisetum typhoides, 14.b Setaria glauca, (Medium cross with thick lobes – 3.a Echinochloa stagnina, 3.b Brachiaria subquadripara, 3.c. Saccharum officinarum 16. Echinochloa colonum), (Medium cross with thin lobes – 4.a Paspalum distichum, 4.b Brachiaria distachya, 4.c Paspalidium geminatum, 17. Imperata cylindrica), (Large cross with thick lobes – 5.a Coix lacryma-jobi, 5.b Paspalidium sp., 18. Coix gigantea), (Large cross with thin lobes – 6.a Brachiaria milliformis, 6.b Porteresia coarctata, 19.a Echinochloa frumentacea, 19.b Paspalum distichum), (Trapeziform rectangular short cell – 7.a Pennisetum pedicellatum, 7.b Bambusa tulda, 7.c Desmostachya bipinata, 20.a Eragrostis tenuifolia, 20.b Heteropogon contortus), (Trapeziform square short cell –8.a Vetiveria zizanioides, 8.b Sporobolus diander, 8.c Paspalum scrobiculatum), (Trapeziform sinuate 9.a, 9.b Triticum aestivum, (Trapeziform polylobate –10a, 21 Triticum aestivum, 10.b Brachiaria subquadripara, 10.c Aeluropus lagopoides), (Circular/Round – 11.a Echinochloa picia, 11.b Paspalum longifolium, 11.c Sporobolus diander, 11.d Zoysia matrella, 21. Eragrostis coarctata), (Crescent moon – 12.a Sporobolus spicatus, 12.b Eleusine indica, 12.c Sporobolus diander, (13. Biconvex - Aeluropus lagopoides)
DOI: 10.5061/dryad.2qc10/9
title: Fig 7

description: Selected sub-morphotypes of elongate, bulliform, and ‘others’ groups, retrieved from the studied grasses. Light microscopic images (1-13) and scanning electron microscopic images (14-24). (Elongate psilate/smooth long cells – 1.a Echinochloa stagnina, 1.b Panicum paludosum, 1.c Setaria palmifolia, 14. Eragrostis japonica), (Elongate papilate/sinuate long cells 2.a Digitaria bicornis, 2.b Phragmites karka, 2.c Echinochloa crusgalli 15. Setaria barbata), (Elongate echinate long cells – 3.a Brachiaria milliformis, 3.b Panicum paludosum, 3.c Setaria viridis, 16.a Dendrocalamus giganteus, 16.b Bambusa wamin), (Elongate dendritic long cells – 4. Panicum repens, 17. Apluda mutica), (Elongate crenate  long cell – 5. Triticum aestivum), (Elongate angular long cells – 6.a Digitaria setigera, 6.b Panicum repens, 6.c Cymbopogon citratus, 18. Bambusa oliveriana), (Parallepipedal bulliform – 7.a Brachiaria sp., 7.b Imperata cylindrica, 23. Leptochloa chinensis), (Cuniform bulliform 8.a Oryza rufipogon, 8.b Bambusa vulgaris var. vittata, 8.c Dinochloa maclellandii, 24.a Imperata cylindrica, 24.b Pennisetum typhoides, 24.c Oryza sativa), (Stomate – 9.a Cynodon dactylon, 9.b Paspalum longifolium, 9.c Eragrostis atrovirens, 21. Eleusine indica), (Silicified epidermal tissue – 10 Eragrostis japonica), (Prickle – 11.a Digitaria ciliaris, 11.b Eragrostis atrovirens, 19. Leptochloa chinensis), (Hair – 12. Digitaria abludens, 20. Dinochloa maclellandii), (Broken bilobate – 13.a Saccharum spontaneum, 13.b Aristida adscensionis, 22. Saccharum spontaneum)
DOI: 10.3886/e101126v1-4833
title: Uniform Crime Reporting Program Data: Arrests by Age, Sex, and Race, 2016

description: These data provide information on the number of arrests reported to the Federal Bureau of Investigation's (FBI) Uniform Crime Reporting (UCR) Program each month by police agencies in the United States. Although not as well known as the "Crimes Known to the Police" data drawn from the Uniform Crime Report's Return A form, the arrest reports by age, sex, and race provide valuable data on 43 offenses including violent, drug, gambling, and larceny crimes. The data received  were structured as a hierarchical file containing (per reporting police agency) an agency header record, and 1 to 12 monthly header reports, and 1 to 43 detail offense records containing the counts of arrests by age, sex, and race for a particular offense. The data has been restructured from the original data to a rectangular format.<br><br>The codebook is for the data from 2015, but applies to this data. Minor changes were made in the column names as well as some aggregate columns (e.g. TOTAL column for all arrests) has been added.
DOI: 10.5281/zenodo.1154913
title: Paleo Hydrodynamics Data Assimilation product (PHYDA)

description: See the associated publication: Steiger, N. J., Smerdon, J. E., Cook, E. R., and Cook, B. I., A reconstruction of global hydroclimate and dynamical variables over the Common Era, <em>Scientific Data</em>, 5:180086, doi: 10.1038/sdata.2018.86.

This database provides the first truly global reconstructions of hydroclimate along with associated climate dynamical variables over the past two thousand years. The reconstructions were made with an improved data assimilation reconstruction approach that optimally combines 2,978 paleoclimate proxy-data time series with the physical constraints of an atmosphere--ocean climate model. Three separate global reconstructions were created from the years 1 to 2000 CE, targeting annual means (defined as April to the next calendar year March), the boreal growing season of June, July, and August (JJA), and the austral growing season of December, January, and February (DJF); each of these reconstructions is contained in a separate NetCDF file. The reconstructions include three global variables gridded at about 2 degree resolution: surface temperature at 2 m, the Palmer drought severity index (PDSI), and the standardized precipitation evapotranspiration index (SPEI). Dynamical variables also included for each reconstruction: the global mean temperature, the North Atlantic sea surface temperature index which is the non-detrended and non-smoothed version of the Atlantic multidecadal oscillation (AMO), the monthly Nino SST indices (Nino 1+2, 3, 3.4, 4), the monthly equatorial Pacific zonal SST gradient, and the location of the intertropical convergence zone (ITCZ) in 11 longitudinal zones.

<strong>A 100 member ensemble (randomly drawn from the original reconstruction ensemble) is available externally due to Zenodo file size restrictions: http://clifford.ldeo.columbia.edu/nsteiger/recon_output/phyda_ens/</strong>

Please cite the associated publication if you use this data set. Contact the author Nathan Steiger (http://www.ldeo.columbia.edu/~nsteiger/contact.html) for more information about the details of the reconstructions.
description: Version 2 update: Fixed incorrect percentile values for index variables.
DOI: 10.5281/zenodo.1154914
title: Paleo Hydrodynamics Data Assimilation Product (Phyda)

description: This database provides the first truly global reconstructions of hydroclimate along with associated climate dynamical variables over the past two thousand years. The reconstructions were made with an improved data assimilation reconstruction approach that optimally combines 2,978 paleoclimate proxy-data time series with the physical constraints of an atmosphere--ocean climate model. Three separate global reconstructions were created from the years 1 to 2000 CE, targeting annual means (defined as April to the next calendar year March), the boreal growing season of June, July, and August (JJA), and the austral growing season of December, January, and February (DJF); each of these reconstructions is contained in a separate NetCDF file. The reconstructions include three global variables gridded at about 2 degree resolution: surface temperature at 2 m, the Palmer drought severity index (PDSI), and the standardized precipitation evapotranspiration index (SPEI). Dynamical variables also included for each reconstruction: the global mean temperature, the North Atlantic sea surface temperature index which is the non-detrended and non-smoothed version of the Atlantic multidecadal oscillation (AMO), the monthly Nino SST indices (Nino 1+2, 3, 3.4, 4), the monthly equatorial Pacific zonal SST gradient, and the location of the intertropical convergence zone (ITCZ) in 11 longitudinal zones.

Contact the author Nathan Steiger (http://www.ldeo.columbia.edu/~nsteiger/contact.html) for more information about the details of the reconstructions and for forthcoming citation information for the publication associated with this dataset.
DOI: 10.17034/3ae75912-840b-4680-86e4-4e64ac4ba29a
title: Adams Method Prepared Metal Oxide Catalysts for Solar-Driven Water Splitting
DOI: 10.15129/1ed57e7a-8fcb-4b99-b89b-88f19e987183
title: Data for "Modular, Step-Efficient Palladium-Catalyzed Cross-Coupling Strategy To Access C6-Heteroaryl 2‑Aminopurine Ribonucleosides"

description: Dataset consists of NMR, HRMS and IR data for Modular, Step-Efficient Palladium-Catalyzed Cross-Coupling Strategy to Access C6-Heteroaryl 2‑Aminopurine Ribonucleosides. The data is sorted into its compound number corresponding to their number in the paper. All compounds have the following data: 
NMR data in its original file may be opened in Topspin or other NMR data programme.
DOI: 10.3886/e100167v16-4831
title: National Surveys on Energy and Environment [United States]

description: The National Surveys on Energy and Environment (NSEE), a core activity in CLOSUP's Energy and Environmental Policy Initiative, reflects a formal partnership between the Muhlenberg Institute of Public Opinion at Muhlenberg College and the Center for Local, State, and Urban Policy at the University of Michigan's Gerald R. Ford School of Public Policy. NSEE surveys include twice per year national opinion surveys on issues directly related to climate change and energy policy, as well as other surveys conducted on a range of topics such as hydraulic fracturing ("fracking"), the Great Lakes, and wider issues of energy and environment. NSEE is co-directed by professor Barry Rabe at the University of Michigan, and professor Christopher Borick at Muhlenberg College. For more information on the collaboration between the University of Michigan and Muhlenberg College, please see the recent article from Muhlenberg Magazine. For more information about the NSEE, contact CLOSUP staff at 734-647-4091 or closup@umich.edu. From 2008-2012 the survey was called the “National Survey of American Public Opinion on Climate Change” (NSAPOOC); starting in 2013 the survey was renamed to the “National Surveys on Energy and Environment” (NSEE). <br><br>The NSEE is committed to transparency in all facets of our work, including timely release and posting of data from each survey wave. A grant from the Office of the Provost at the University of Michigan has allowed us to provide online access to earlier waves of the NSEE, including frequency tables, survey instruments, and datasets. Users can see a list of topics covered by the NSEE, and search for questions by text, variable name, or variable category on CLOSUP's website. <br><br>Although the datasets are listed by survey wave, the NSEE is a valuable source of longitudinal public-opinion data on climate change and energy policy. Many questions have been asked over multiple waves, including questions about belief in global warming that have been asked in every wave of the NSEE. Consult the NSEE Crosswalk to see which questions have been asked in prior and subsequent waves of the NSEE. To facilitate longitudinal analysis, the NSEE datasets use a longitudinal variable naming scheme to facilitate longitudinal analysis. Variable names include two parts: a subject category for the question, and a description of the contents of the question. When a question has been asked with the same text and response options over multiple waves, the same variable name will be used in each dataset. For more information on the longitudinal naming scheme users should consult the codebooks for the datasets. <br><br><br>
DOI: 10.6073/pasta/0872f51911a807ca4a39f4948810c298
title: SBC LTER: Reference: Daily and monthly averages of modeled significant wave height (Hs) and peak wave period (Tp) in the Santa Barbara Coastal area from the Coastal Data Information Program - Monitoring and Prediction System (CDIP MOP)

description: Data are mean significant wave height and wave period (averaged daily
  and monthly) for 17 CDIP MOP model-output sites on the mainland coast
  of Santa Barbara County, which are nearest to the nine mainland SBC
  LTER reefs. Data were obtained from the Coastal Data Information
  Program (CDIP) MOP system (MOnitoring and Prediction). The sampling
  period is usually 30 mintes of each hour, and data are reported hourly
  by CDIP. These data represent aggregates of a subset of the publically
  available CDIP product "MOP Alongshore Sea and Swell
  Predictions". (http://cdip.ucsb.edu). This dataset is planned to
  be ongoing. All times are UTC.

      
  From http://cdip.ucsb.edu: The Coastal Data Information Program (CDIP)
  is a research group at Scripps Institution of Oceanography that
  monitors coastal waves and nearshore sand levels on regional scales.
  CDIP maintains a network of optimally-placed, directional wave buoys
  from San Diego to Eureka. The buoy measurements are used to initialize
  a high spatial resolution (100m x 100m) linear spectral wave
  propagation model. The resulting hourly hindcasts and nowcasts of CA
  coastal wave conditions have a level of accuracy that is not possible
  with more traditional wind-wave generation models that are initialized
  with modeled wind fields.
DOI: 10.21227/h2gq0x
title: Real-time Body Tracking
DOI: 10.5281/zenodo.1155473
title: Spanish 3B Words Word2Vec Embeddings

description: Ready to use gensim Word2Vec embedding models for the Spanish language. Models are created using a window of +/- 5 words, discarding those words with less than 5 instances and creating a vector of 400 dimensions for each word. The text used to create the embeddings has been recovered from news, Wikipedia, the Spanish BOE, web crawling and open literary sources.  The used text has a total of 3.257.329.900 words and 18.852.481.207 characters.

We support two types of models: Gensim full models (complete_model.zip) and KeyedVectors (keyed_vectors.zip). You can check the differences between them in the following URL: https://radimrehurek.com/gensim/models/keyedvectors.html

To load the full model use: model = Word2Vec.load("complete.model")<br>
To load the KeyedVectors use: word_vectors = KeyedVectors.load('complete.kv', mmap='r')

More info about the models can be found in: https://github.com/aitoralmeida/spanish_word2vec