cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p9o4dj1g
title: Data Release for the 2002 National Seismic Hazard Model for the Conterminous U.S.

description: Seismicity catalogs, gridded seismic hazard curve data, gridded ground motion data, and mapped gridded ground motion values are available for the 2002 National Seismic Hazard Model for the Conterminous U.S. Seismicity catalogs are available for the western U.S. (in Mw) and central and eastern U.S. (in mb). Probabilistic seismic hazard data and maps of the conterminous U.S. for peak ground acceleration (PGA) and 0.1, 0.2, 0.3, 0.5, 1.0 and 2.0 second spectral acceleration at probability levels of 2 percent in 50 years (annual probability of 0.000404) and 10 percent in 50 years (annual probability of 0.0021), assuming firm rock soil conditions at 760 m/s, are available. Hazard was calculated on a 0.1 degree by 0.1 degree grid, defined by a bounding box encompassing the conterminous U.S. (-125 to -65 degrees longitude west, 24.6 to 50 degrees latitude north). Development of the 2002 National Seismic Hazard Model for the Conterminous U.S. is documented in the USGS Open-File Report 02-420 (https://pubs.usgs.gov/of/2002/ofr-02-420/). This dataset is considered a legacy dataset. The original dataset was uploaded to the USGS website at the time of publication of the seismic hazard model (2002) but was later moved over the the USGS ScienceBase Catalog (2019). The original dataset was assumed to be complete and accurate, but may contain inconsistencies when compared to more recent, actively maintained datasets.
DOI: 10.21258/1664185
title: Inventario forestale nazionale svizzero - Tabella n. 898201
DOI: 10.5066/f757194g
title: Hydrograph-separation results for 225 streams in the Chesapeake Bay watershed derived by using PART, HYSEP (Fixed, Local minimum, Slide), BFI, and a Recursive Digital Filter with streamflow data ranging from 1913 through 2016

description: This U.S. Geological Survey (USGS) data release contains daily-mean streamflow and estimated-daily base flow for 225 stream gages in the Chesapeake Bay watershed ranging from 1913 to 2016 (beginning and end dates may vary). There is a table containing hydrograph-separation results by six methods for 225 sites (Hydrograph_separation_results_for_225_streams_in_the_Chesapeake_Bay_watershed) and a summary table with hydrograph-separation results for each site and method (Hydrograph_separation_summary_for_225_streams_in_the_Chesapeake_Bay_watershed). Quantitative estimates of base flow are necessary to address questions of the vulnerability and response of aquatic ecosystems to natural and human-induced change in environmental conditions. Base flow is generally not measured directly, but is estimated from observations of streamflow and/or stream water chemistry. Base flow was estimated using PART (Rutledge, 1998), HYSEP (Fixed, Local minimum, and Slide; Sloto and Crouse, 1996), BFI (Wahl and Wahl, 1988 and Wahl and Wahl, 1995), and a Recursive Digital Filter (Eckhardt, 2005 and Collishonn and Fan, 2013) in selected watersheds throughout the Chesapeake Bay watershed. The references to the above citations are in the Supplemental Information section of this metadata record. These data support the following publication: Raffensperger, J.P., Baker, A.C., Blomquist, J.D., and Hopple, J.A., 2017, Optimal hydrograph separation using a recursive digital filter constrained by chemical mass balance, with application to selected Chesapeake Bay watersheds: U.S. Geological Survey Scientific Investigations Report 2017-5034, 51 p., https://doi.org/10.3133/sir20175034.
DOI: 10.5066/f7sx6c4p
title: Uinta Basin Ground and Surface Water Quality Records

description: This database includes water quality records compiled at the request of the Bureau of Land Management for the Uinta Basin in Utah and Colorado. All data contained within this database are publically available via the United States Geological Survey's National Water Information System (NWIS) database (available at https://waterdata.usgs.gov/nwis). Data collected and provided by the states of Utah and Colorado were also analyzed, however, this data had also been submitted to, and was available in, NWIS by the time this database was compiled. This database includes all available water-quality data for the Uinta Basin. For groundwater samples this includes data up to October 24, 2014; for surface water samples this includes data up to July 27, 2017.
DOI: 10.5066/f7x63m7s
title: Water quality data for Beaver Creek above Victoria Creek and West Twin Creek, Alaska, 2009-2017

description: This dataset includes discrete water quality data and sensor data from 2009-2017 for Beaver Creek above Victoria Creek and West Twin Creek in Alaska.
DOI: 10.5281/zenodo.4340411
title: ATE_ABSITA@EVALITA2020 Task

description: ***************************** TASK DESCRIPTION ********************************* In our challenge, we would like to propose three different annotation tasks regarding Aspect Term Extraction (ATE), Aspect Based Sentiment Analysis (ABSA), and sentence Sentiment Analysis (SA). Aspect Term Extraction (ATE) is the task of identifying an "aspect" in a text without knowing a priori the list that contains it. According to the literature definition, a term/phrase is considered as an aspect when it co-occurs with “opinion words” that indicate a sentiment polarity on it. More details and examples are available at: http://www.di.uniba.it/~swap/ate_absita/examples.html Aspect-based Sentiment Analysis (ABSA) is an evolution of Sentiment Analysis that aims at capturing the aspect-level opinions expressed in natural language texts. In the Aspect-based Sentiment Analysis (ABSA) task, the polarity of each expressed aspect is recognized. Sentiment Analysis (or Opinion Mining) is the task of identifying what the user thinks about a particular piece of text. In particular, it often takes the form of an annotation task with the purpose of annotating a portion of text with a positive, negative, or neutral label. In our Sentiment Analysis (SA) task, the polarity of the review is provided. In particular, we decided to use the score left by the user at the item as value of polarity. It is defined as an integer number into the range 1:5. *************************** ATE ABSITA - EVALITA 2020 ************************** Contacts:<br> website: http://www.di.uniba.it/~swap/ate_absita/index.html<br> email: ate.absita.evalita2020@gmail.com<br> email: marco.polignano@uniba.it PLEASE CITE: @InProceedings{ateabsita2020,<br> author = {Lorenzo de Mattei and Graziella de Martino and Andrea Iovine and Alessio Miaschi and Marco Polignano and Giulia Rambelli},<br> title = {{ATE\_ABSITA@EVALITA2020: Overview of the Aspect Term Extraction and Aspect-based Sentiment Analysis Task}},<br> booktitle = {{Proceedings of the 7th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA 2020)}},<br> editor = {Basile, Valerio and Croce, Danilo and Di Maro, Maria and Passaro, Lucia C.},<br> year = {2020},<br> publisher = {CEUR.org},<br> address = {Online}<br> }
description: {"references": ["De Mattei, Lorenzo, Graziella De Martino, Andrea Iovine, Alessio Miaschi, Marco Polignano, and Giulia Rambelli. \"ATE_ABSITA@ EVALITA2020: Overview of the Aspect Term Extraction and Aspect-based Sentiment Analysis Task.\" Proceedings of the 7th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA 2020), Online. CEUR. org (2020)."]}
DOI: 10.5281/zenodo.4340410
title: ATE_ABSITA@EVALITA2020 Task

description: ***************************** TASK DESCRIPTION ********************************* In our challenge, we would like to propose three different annotation tasks regarding Aspect Term Extraction (ATE), Aspect Based Sentiment Analysis (ABSA), and sentence Sentiment Analysis (SA). Aspect Term Extraction (ATE) is the task of identifying an "aspect" in a text without knowing a priori the list that contains it. According to the literature definition, a term/phrase is considered as an aspect when it co-occurs with “opinion words” that indicate a sentiment polarity on it. More details and examples are available at: http://www.di.uniba.it/~swap/ate_absita/examples.html Aspect-based Sentiment Analysis (ABSA) is an evolution of Sentiment Analysis that aims at capturing the aspect-level opinions expressed in natural language texts. In the Aspect-based Sentiment Analysis (ABSA) task, the polarity of each expressed aspect is recognized. Sentiment Analysis (or Opinion Mining) is the task of identifying what the user thinks about a particular piece of text. In particular, it often takes the form of an annotation task with the purpose of annotating a portion of text with a positive, negative, or neutral label. In our Sentiment Analysis (SA) task, the polarity of the review is provided. In particular, we decided to use the score left by the user at the item as value of polarity. It is defined as an integer number into the range 1:5. *************************** ATE ABSITA - EVALITA 2020 ************************** Contacts:<br> website: http://www.di.uniba.it/~swap/ate_absita/index.html<br> email: ate.absita.evalita2020@gmail.com<br> email: marco.polignano@uniba.it PLEASE CITE: @InProceedings{ateabsita2020,<br> author = {Lorenzo de Mattei and Graziella de Martino and Andrea Iovine and Alessio Miaschi and Marco Polignano and Giulia Rambelli},<br> title = {{ATE\_ABSITA@EVALITA2020: Overview of the Aspect Term Extraction and Aspect-based Sentiment Analysis Task}},<br> booktitle = {{Proceedings of the 7th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA 2020)}},<br> editor = {Basile, Valerio and Croce, Danilo and Di Maro, Maria and Passaro, Lucia C.},<br> year = {2020},<br> publisher = {CEUR.org},<br> address = {Online}<br> }
description: {"references": ["De Mattei, Lorenzo, Graziella De Martino, Andrea Iovine, Alessio Miaschi, Marco Polignano, and Giulia Rambelli. \"ATE_ABSITA@ EVALITA2020: Overview of the Aspect Term Extraction and Aspect-based Sentiment Analysis Task.\" Proceedings of the 7th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA 2020), Online. CEUR. org (2020)."]}
DOI: 10.21258/1654475
title: Inventario forestale nazionale svizzero - Tabella n. 898152
DOI: 10.21258/1662369
title: Inventario forestale nazionale svizzero - Tabella n. 902202
DOI: 10.5066/p943tft7
title: Three-dimensional grid of properties used to analyze induced seismicity recorded from 1991 to 2012 at Paradox Valley, Colorado

description: Large numerical grid used for finite element and finite volume analysis of induced seismicity in Paradox Basin. The grid construction used the boundaries of three sub-horizontal surfaces; the topography, and the top and bottom of the production formation is determined from drilling and seismic reflection profiling, yielding the surface topography and thickness variations in these data throughout the domain. The cell dimensions in the north and east directions are 200 m and the horizontal gridlines are oriented north-south and east-west. There are 245 elements in the east west direction, 241 elements in the north south direction, and 31 elements in the vertical, extending from the surface to 15 km depth. The grid dimensions are approximately 25 km on a side in the horizontal dimensions. All grid dimensions are in kilometers, with the vertical dimension given as elevation. At the well (Figure 3), elevations for the 31 nodes in the vertical direction conform to the permeability boundaries given in the Paradox Valley Unit well data from the US Bureau of Reclamation. Elsewhere the vertical structure at the well is compressed or stretched to fit the horizons determined from a combination of seismic tomography and data from numerous wells in and around the Paradox valley. These horizons are accurate to about a meter. Where the production horizon limestone pinches out, Pre-Cambrian material values are prescribed. All boundaries are permeable to flow of pore fluid into and out of the domain in response to pumping, but the bottom and sides of the domain are set up to approximate infinite boundaries during numerical processing.
DOI: 10.21258/1645576
title: Inventario forestale nazionale svizzero - Tabella n. 891781
DOI: 10.5066/p95tq72u
title: Louisiana Barrier Island Comprehensive Monitoring Program - 2015 habitat map, West Chenier Region

description: The Barrier Island Comprehensive Monitoring (BICM) program was developed by Louisiana's Coastal Protection and Restoration Authority (CPRA) and is implemented as a component of the System Wide Assessment and Monitoring Program (SWAMP). The program uses both historical data and contemporary data collections to assess and monitor changes in the aerial and subaqueous extent of islands, habitat types, sediment texture and geotechnical properties, environmental processes, and vegetation composition. Examples of BICM datasets include still and video aerial photography for documenting shoreline changes, shoreline positions, habitat mapping, land change analyses, light detection and ranging (lidar) surveys for topographic elevations, single-beam and swath bathymetry, and sediment grab samples. For more information about the BICM program, see Kindinger and others (2013). The U.S. Geological Survey, Wetland and Aquatic Research Center provides support to the BICM program through the development of habitat map products using aerial imagery and lidar elevation data and assessing change in habitats over time. These data provide a snapshot of barrier island habitats and can be combined with other past and/or future maps to monitor these valuable natural resources over time. The current effort of this habitat mapping program includes developing habitat maps for 2008 and 2015/16 for the following BICM regions: 1) West Chenier; 2) East Chenier; 3) Acadiana Bays (only Marsh Island); 4) Early Lafourche Delta; 5) Late Lafourche Delta; 6) Modern Delta (only Chaland Headland and Shell Island); and 7) Chandeleur Islands. Additionally, a habitat change analysis will be conducted comparing reaches mapped in 2008 and 2015/2016. The BICM program has developed two habitat classification schemes which include a detailed 15-class habitat scheme and a general eight-class habitat scheme. The detailed scheme was developed specifically for this habitat mapping effort and builds off the general scheme used in previous BICM habitat mapping efforts (Fearnley and others, 2009). The additional classes developed in the detailed scheme are primarily used to further delineate various dune habitats, separate marsh and mangrove, and distinguish between beach and unvegetated barrier flat habitats. To ensure comparability between this effort and previous BICM map products, we have crosswalked the detailed classes to general habitat classes previously used by Fearnley and others (2009).
DOI: 10.21258/1635639
title: Inventario forestale nazionale svizzero - Tabella n. 897965
DOI: 10.21258/1635868
title: Inventario forestale nazionale svizzero - Tabella n. 899029
DOI: 10.5066/p9dmifmw
title: High resolution earthquake catalogs from the 2018 Kilauea eruption sequence

description: The 2018 Kilauea eruption and caldera collapse generated intense cycles of seismicity tied to repeated large seismic (Mw ~5) collapse events associated with magma withdrawal from beneath the summit. To gain insight into the underlying dynamics and aid eruption response, we applied waveform-based earthquake detection and double-difference location as the eruption unfolded. Here, we augment these rapid results by grouping events based on patterns of correlation-derived phase polarities across the network. From April 29 to August 6, bracketing the eruption, we used ~2800 events cataloged by the Hawaiian Volcano Observatory to detect and precisely locate 44,000+ earthquakes. Resulting hypocentroids resolve complex, yet coherent structures, concentrated at shallow depths east of Halema'uma'u crater, beneath the eventual eastern perimeter of surface collapse. Based on a preponderance of dilatational P-wave first motions and similarities with previously inferred dike structures, we hypothesize that failure was dominated by coupled shear and crack closure.
DOI: 10.21258/1632168
title: Inventario forestale nazionale svizzero - Tabella n. 896907
DOI: 10.21258/1640810
title: Inventario forestale nazionale svizzero - Tabella n. 899305
DOI: 10.21258/1672058
title: Inventario forestale nazionale svizzero - Tabella n. 891784
DOI: 10.21258/1649067
title: Inventario forestale nazionale svizzero - Tabella n. 892046
DOI: 10.21258/1670188
title: Inventario forestale nazionale svizzero - Tabella n. 892050
DOI: 10.21258/1631278
title: Inventario forestale nazionale svizzero - Tabella n. 899060
DOI: 10.21258/1662016
title: Inventario forestale nazionale svizzero - Tabella n. 897792
DOI: 10.21258/1640526
title: Inventario forestale nazionale svizzero - Tabella n. 892957
DOI: 10.21227/74v3-0j16
title: Supplementary materials for "Fourier Series based Analytic Model of a Resonant MEMS Mirror for General Voltage Inputs"

description: Description:
DOI: 10.21258/1667744
title: Inventario forestale nazionale svizzero - Tabella n. 898586
DOI: 10.21258/1642873
title: Inventario forestale nazionale svizzero - Tabella n. 897262
DOI: 10.21258/1642457
title: Inventario forestale nazionale svizzero - Tabella n. 899214
DOI: 10.21258/1662331
title: Inventario forestale nazionale svizzero - Tabella n. 897490
DOI: 10.5066/p9nfr2tq
title: 04087088 - Underwood Creek at Wauwatosa, WI - 2019/07/17 GPS Survey

description: The dataset contains GPS survey data from the second of two surveys conducted after a high flow event on August 20, 2018, at the USGS streamgage 04087088 - Underwood Creek at Wauwatosa, WI. This survey, on July 17, 2019, documents cross section geometry. The survey was performed using Trimble R10 RTK GPS unit (SN: 5326439561) and Trimble S7 Robotic Total Station (SN: 37430018).
DOI: 10.5517/ccdc.csd.cc1zlrq1
title: CCDC 1835284: Experimental Crystal Structure Determination

description: Related Article: P. Biswas, K. Sarkar, P. Dastidar|2020|Macromolecular Bioscience (Online)|20|2000044|doi:10.1002/mabi.202000044
DOI: 10.5517/ccdc.csd.cc1zlrr2
title: CCDC 1835285: Experimental Crystal Structure Determination

description: Related Article: P. Biswas, K. Sarkar, P. Dastidar|2020|Macromolecular Bioscience (Online)|20|2000044|doi:10.1002/mabi.202000044
DOI: 10.5517/ccdc.csd.cc1zlrp0
title: CCDC 1835283: Experimental Crystal Structure Determination

description: Related Article: P. Biswas, K. Sarkar, P. Dastidar|2020|Macromolecular Bioscience (Online)|20|2000044|doi:10.1002/mabi.202000044
DOI: 10.5517/ccdc.csd.cc24871q
title: CCDC 1974112: Experimental Crystal Structure Determination

description: Related Article: Toshiyuki Sasaki, Shunichi Sakamoto, Satoshi Takamizawa|2020|Cryst.Growth Des.|20|8079|doi:10.1021/acs.cgd.0c01350
DOI: 10.5517/ccdc.csd.cc24872r
title: CCDC 1974113: Experimental Crystal Structure Determination

description: Related Article: Toshiyuki Sasaki, Shunichi Sakamoto, Satoshi Takamizawa|2020|Cryst.Growth Des.|20|8079|doi:10.1021/acs.cgd.0c01350
DOI: 10.5517/ccdc.csd.cc24873s
title: CCDC 1974114: Experimental Crystal Structure Determination

description: Related Article: Toshiyuki Sasaki, Shunichi Sakamoto, Satoshi Takamizawa|2020|Cryst.Growth Des.|20|8079|doi:10.1021/acs.cgd.0c01350
DOI: 10.5066/p9q8rsf5
title: Lidar-derived Data Layers for South Carolina StreamStats, 2007-2013

description: In cooperation with the South Carolina Department of Transportation, the U.S. Geological Survey developed a StreamStats web application (https://water.usgs.gov/osw/streamstats/) that provides analytical tools useful for water-resources planning and management (Ries and others, 2017). This data set presents the lidar-derived flow direction, flow accumulation, streamline, and hydro-enforced digital elevation model raster data used for analysis in StreamStats. Rasters are included for each of the following hydrologic unit codes (HUCs): 03040101, 03040102, 03040103, 03040104, 03040105, 03040201, 03040202, 03040203, 03040204, 03040205, 03040206, 03040207, 03040208, 03050101, 03050102, 03050103, 03050104, 03050105, 03050106, 03050107, 03050108, 03050109, 03050110, 03050111, 03050112, 03050201, 03050202, 03050203, 03050204, 03050205, 03050206, 03050207, 03050208, 03050209, 03050210, 03060101, 03060102, 03060103, 03060104, 03060105, 03060106, 03060107, 03060108, 03060109, and 03060110. The raster data is organized into four main zip folders, which are called &quot;Flow_Accumulation_Grids,&quot; &quot;Flow_Direction_Grids,&quot; &quot;Hydro_Enforced_DEM_Grids,&quot; and &quot;Streamline_Grids.&quot; Reference: Ries, K.G., III, Newson J.K., Smith, M.J., Guthrie, J.D., Steeves, P.A., Haluska, T.L., Kolb, K.R., Thompson, R.F., Santoro, R.D., and Vraga, H.W., 2017, StreamStats, version 4: U.S. Geological Survey Fact Sheet 2017-3046, 4 p., https://doi.org/10.3133/fs20173046.
DOI: 10.5517/ccdc.csd.cc25n4ss
title: CCDC 2015365: Experimental Crystal Structure Determination

description: Related Article: Andrés Zavaleta, Aleksandr O. Lykhin, Jorge H. S. K. Monteiro, Shoto Uchida, Thomas W. Bell, Ana de Bettencourt-Dias, Sergey A. Varganov, Judith Gallucci|2020|J.Am.Chem.Soc.|142|20306|doi:10.1021/jacs.0c08182
DOI: 10.5517/ccdc.csd.cc25n4tt
title: CCDC 2015366: Experimental Crystal Structure Determination

description: Related Article: Andrés Zavaleta, Aleksandr O. Lykhin, Jorge H. S. K. Monteiro, Shoto Uchida, Thomas W. Bell, Ana de Bettencourt-Dias, Sergey A. Varganov, Judith Gallucci|2020|J.Am.Chem.Soc.|142|20306|doi:10.1021/jacs.0c08182
DOI: 10.5517/ccdc.csd.cc25n4vv
title: CCDC 2015367: Experimental Crystal Structure Determination

description: Related Article: Andrés Zavaleta, Aleksandr O. Lykhin, Jorge H. S. K. Monteiro, Shoto Uchida, Thomas W. Bell, Ana de Bettencourt-Dias, Sergey A. Varganov, Judith Gallucci|2020|J.Am.Chem.Soc.|142|20306|doi:10.1021/jacs.0c08182
DOI: 10.5517/ccdc.csd.cc25q042
title: CCDC 2017143: Experimental Crystal Structure Determination

description: Related Article: Sitthichok Kasemthaveechok, Laura Abella, Marion Jean, Marie Cordier, Thierry Roisnel, Nicolas Vanthuyne, Thierry Guizouarn, Olivier Cador, Jochen Autschbach, Jeanne Crassous, Ludovic Favereau|2020|J.Am.Chem.Soc.|142|20409|doi:10.1021/jacs.0c08948
DOI: 10.5517/ccdc.csd.cc25qzmj
title: CCDC 2018088: Experimental Crystal Structure Determination

description: Related Article: Abolfazl Ghaderian, Alicja Franke, Marcos Gil-Sepulcre, Jordi Benet-Buchholz, Antoni Llobet, Ivana Ivanović-Burmazović, Carolina Gimbert-Suriñach|2020|Dalton Trans.|49|17375|doi:10.1039/D0DT03548C
DOI: 10.5517/ccdc.csd.cc25qznk
title: CCDC 2018089: Experimental Crystal Structure Determination

description: Related Article: Abolfazl Ghaderian, Alicja Franke, Marcos Gil-Sepulcre, Jordi Benet-Buchholz, Antoni Llobet, Ivana Ivanović-Burmazović, Carolina Gimbert-Suriñach|2020|Dalton Trans.|49|17375|doi:10.1039/D0DT03548C
DOI: 10.5066/p9oy8wt2
title: Data Release for the 2010 Haiti Seismic Hazard Model

description: Gridded seismic hazard curve data, gridded ground motion data, and mapped gridded ground motion values are available for the 2010 Haiti Seismic Hazard Model. Probabilistic seismic hazard data and maps are available for peak ground acceleration (PGA) and 0.2, and 1.0 second spectral acceleration at probability levels of 2 percent in 50 years and 10 percent in 50 years, assuming firm rock soil conditions at 760 m/s. Development of the 2010 Haiti Seismic Hazard Model is documented at https://pubs.usgs.gov/of/2010/1067/ and https://doi.org/10.1193/1.3631016. This dataset is considered a legacy dataset. The original dataset was uploaded to the USGS website at the time of publication of the seismic hazard model (2010) but was later moved over the the USGS ScienceBase Catalog (2020). The original dataset was assumed to be complete and accurate, but may contain inconsistencies when compared to more recent, actively maintained datasets.