cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7469545
title: finals_2022_305

description: Log and boot files of game 305
DOI: 10.5281/zenodo.7469544
title: finals_2022_305

description: Log and boot files of game 305
DOI: 10.5281/zenodo.7469549
title: finals_2022_306

description: Log and boot files of game 306
DOI: 10.5281/zenodo.7469551
title: finals_2022_307

description: Log and boot files of game 307
DOI: 10.5281/zenodo.7469548
title: finals_2022_306

description: Log and boot files of game 306
DOI: 10.5281/zenodo.7469550
title: finals_2022_307

description: Log and boot files of game 307
DOI: 10.5281/zenodo.7469555
title: finals_2022_308

description: Log and boot files of game 308
DOI: 10.5281/zenodo.7469554
title: finals_2022_308

description: Log and boot files of game 308
DOI: 10.5281/zenodo.7469561
title: finals_2022_309

description: Log and boot files of game 309
DOI: 10.5281/zenodo.7469560
title: finals_2022_309

description: Log and boot files of game 309
DOI: 10.5281/zenodo.7469565
title: finals_2022_310

description: Log and boot files of game 310
DOI: 10.5281/zenodo.7469564
title: finals_2022_310

description: Log and boot files of game 310
DOI: 10.5281/zenodo.7469573
title: finals_2022_311

description: Log and boot files of game 311
DOI: 10.5281/zenodo.7469572
title: finals_2022_311

description: Log and boot files of game 311
DOI: 10.5281/zenodo.7469581
title: finals_2022_312

description: Log and boot files of game 312
DOI: 10.5281/zenodo.7469580
title: finals_2022_312

description: Log and boot files of game 312
DOI: 10.5281/zenodo.7469583
title: finals_2022_313

description: Log and boot files of game 313
DOI: 10.5281/zenodo.7469582
title: finals_2022_313

description: Log and boot files of game 313
DOI: 10.5281/zenodo.7469585
title: finals_2022_314

description: Log and boot files of game 314
DOI: 10.5281/zenodo.7469584
title: finals_2022_314

description: Log and boot files of game 314
DOI: 10.5281/zenodo.7469589
title: finals_2022_315

description: Log and boot files of game 315
DOI: 10.5281/zenodo.7469588
title: finals_2022_315

description: Log and boot files of game 315
DOI: 10.5281/zenodo.7469591
title: finals_2022_316

description: Log and boot files of game 316
DOI: 10.5281/zenodo.7469590
title: finals_2022_316

description: Log and boot files of game 316
DOI: 10.5281/zenodo.7469593
title: finals_2022_317

description: Log and boot files of game 317
DOI: 10.5281/zenodo.7469592
title: finals_2022_317

description: Log and boot files of game 317
DOI: 10.5281/zenodo.7469595
title: finals_2022_318

description: Log and boot files of game 318
DOI: 10.5281/zenodo.7469594
title: finals_2022_318

description: Log and boot files of game 318
DOI: 10.5281/zenodo.7469597
title: finals_2022_319

description: Log and boot files of game 319
DOI: 10.5281/zenodo.7469596
title: finals_2022_319

description: Log and boot files of game 319
DOI: 10.5281/zenodo.7469600
title: finals_2022_320

description: Log and boot files of game 320
DOI: 10.5281/zenodo.7469599
title: finals_2022_320

description: Log and boot files of game 320
DOI: 10.5281/zenodo.7469606
title: finals_2022_321

description: Log and boot files of game 321
DOI: 10.5281/zenodo.7469605
title: finals_2022_321

description: Log and boot files of game 321
DOI: 10.5281/zenodo.7469610
title: finals_2022_322

description: Log and boot files of game 322
DOI: 10.5281/zenodo.7469609
title: finals_2022_322

description: Log and boot files of game 322
DOI: 10.5281/zenodo.7469614
title: finals_2022_323

description: Log and boot files of game 323
DOI: 10.5281/zenodo.7469613
title: finals_2022_323

description: Log and boot files of game 323
DOI: 10.5281/zenodo.7469620
title: finals_2022_324

description: Log and boot files of game 324
DOI: 10.5281/zenodo.7469619
title: finals_2022_324

description: Log and boot files of game 324
DOI: 10.5281/zenodo.7469622
title: finals_2022_325

description: Log and boot files of game 325
DOI: 10.5281/zenodo.7469621
title: finals_2022_325

description: Log and boot files of game 325
DOI: 10.5281/zenodo.7469628
title: finals_2022_326

description: Log and boot files of game 326
DOI: 10.5281/zenodo.7469627
title: finals_2022_326

description: Log and boot files of game 326
DOI: 10.5281/zenodo.7469634
title: finals_2022_327

description: Log and boot files of game 327
DOI: 10.5281/zenodo.7469633
title: finals_2022_327

description: Log and boot files of game 327
DOI: 10.5281/zenodo.7469638
title: finals_2022_328

description: Log and boot files of game 328
DOI: 10.5281/zenodo.7469637
title: finals_2022_328

description: Log and boot files of game 328
DOI: 10.5281/zenodo.7469642
title: finals_2022_329

description: Log and boot files of game 329
DOI: 10.5281/zenodo.7469641
title: finals_2022_329

description: Log and boot files of game 329
DOI: 10.5281/zenodo.7469644
title: finals_2022_330

description: Log and boot files of game 330
DOI: 10.5281/zenodo.7469643
title: finals_2022_330

description: Log and boot files of game 330
DOI: 10.5281/zenodo.7469648
title: finals_2022_331

description: Log and boot files of game 331
DOI: 10.5281/zenodo.7469647
title: finals_2022_331

description: Log and boot files of game 331
DOI: 10.5281/zenodo.7469650
title: finals_2022_332

description: Log and boot files of game 332
DOI: 10.5281/zenodo.7469649
title: finals_2022_332

description: Log and boot files of game 332
DOI: 10.5281/zenodo.7469652
title: finals_2022_333

description: Log and boot files of game 333
DOI: 10.5281/zenodo.7469651
title: finals_2022_333

description: Log and boot files of game 333
DOI: 10.5281/zenodo.7469659
title: finals_2022_334

description: Log and boot files of game 334
DOI: 10.5281/zenodo.7469658
title: finals_2022_334

description: Log and boot files of game 334
DOI: 10.5281/zenodo.7469661
title: finals_2022_335

description: Log and boot files of game 335
DOI: 10.5281/zenodo.7469660
title: finals_2022_335

description: Log and boot files of game 335
DOI: 10.5281/zenodo.7469665
title: finals_2022_336

description: Log and boot files of game 336
DOI: 10.5281/zenodo.7469664
title: finals_2022_336

description: Log and boot files of game 336
DOI: 10.5281/zenodo.7469669
title: finals_2022_337

description: Log and boot files of game 337
DOI: 10.5281/zenodo.7469668
title: finals_2022_337

description: Log and boot files of game 337
DOI: 10.5281/zenodo.7469673
title: finals_2022_338

description: Log and boot files of game 338
DOI: 10.5281/zenodo.7469677
title: finals_2022_339

description: Log and boot files of game 339
DOI: 10.5281/zenodo.7469672
title: finals_2022_338

description: Log and boot files of game 338
DOI: 10.5281/zenodo.7469676
title: finals_2022_339

description: Log and boot files of game 339
DOI: 10.5281/zenodo.7469680
title: finals_2022_340

description: Log and boot files of game 340
DOI: 10.5281/zenodo.7469679
title: finals_2022_340

description: Log and boot files of game 340
DOI: 10.6073/pasta/e9898c78448cbbe544057e689d3c06df
title: IISD Experimental Lakes Area: Bathymetry Data Package, 1968-2022

description: The IISD Experimental Lakes Area (IISD-ELA) bathymetry data package provides bathymetric data on IISD-ELA lakes in a variety of formats and degrees of processing. The data package has been organized into four parts: tabular, geospatial, maps, and additional metadata. Tabular data include cumulative and interval values for area and volume at specific depth ranges, summary statistics (perimeter, surface area, total volume, mean depth, and maximum depth), and metadata for the lakes (such as water level on date of survey and methods used to collect and process the data). Geospatial data are suitable for map-making and geospatial analysis. The geospatial folder includes raw coordinate data (CSV) and processed geospatial outputs: contour lines (geodatabase and geopackage), lake polygons (geodatabase and geopackage), and raster DEMs (geodatabase and TIFF). Maps are provided in PDF format in black and white or colour. Where current maps are not available, historical maps have been provided, which are black and white scans. Additional metadata files include the Info Sheet PDF, which provides details for interpreting column names and understanding surveying and processing methods. A materials overview CSV table is provided, outlining which data types are available for each lake. A lake polygon metadata CSV table specifies which satellite imagery providers and dates were used to refine lake polygon outlines. The data package is ongoing - updated data will be provided as more lakes are surveyed and data processed. If current data do not exist for the lake you are interested in, please get in touch with us - we may be able to add a survey of that lake to our bathymetry survey schedule.
DOI: 10.5281/zenodo.7468556
title: finals_2022_93

description: Log and boot files of game 93
DOI: 10.5281/zenodo.7468555
title: finals_2022_93

description: Log and boot files of game 93
DOI: 10.5281/zenodo.7468605
title: finals_2022_105

description: Log and boot files of game 105
DOI: 10.5281/zenodo.7468606
title: finals_2022_105

description: Log and boot files of game 105
DOI: 10.25318/9810043901-eng
title: Employment income statistics by visible minority, highest level of education, immigrant status and income year: Canada, provinces and territories, census metropolitan areas and census agglomerations with parts

description: Employment income (in 2019 and 2020) of visible minority groups by educational characteristics, for cities.
DOI: 10.5281/zenodo.7259266
title: Raw Data of Tortugas Mountain Observatory 0.6m Goodrich Observations of LCROSS

description: This archive contains the raw data from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the Goodrich instrument on the Tortugas Mountain Observatory 0.6m telescope. Full details are available in Chanover, N. J. et al. Results from the NMSU-NASA Marshall Space Flight Center LCROSS observational campaign. <em>J. Geophys. Res. (Planets)</em> <strong>116</strong>, E08003 (2011). https://doi.org/10.1029/2010JE003761
DOI: 10.5281/zenodo.7259267
title: Raw Data of Tortugas Mountain Observatory 0.6m Goodrich Observations of LCROSS

description: This archive contains the raw data from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the Goodrich instrument on the Tortugas Mountain Observatory 0.6m telescope. Full details are available in Chanover, N. J. et al. Results from the NMSU-NASA Marshall Space Flight Center LCROSS observational campaign. <em>J. Geophys. Res. (Planets)</em> <strong>116</strong>, E08003 (2011). https://doi.org/10.1029/2010JE003761
DOI: 10.25318/9810043901-fra
title: Statistiques du revenu d’emploi, selon la minorité visible, le plus haut niveau de scolarité, le statut d’immigrant et l’année de revenu : Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement y compris les parties

description: Revenu d’emploi (en 2019 et en 2020) des groupes de minorités visibles, selon les caractéristiques relatives à la scolarité, pour les villes.
DOI: 10.5281/zenodo.7469846
title: Data for "Flow adaptation to velocity changes in transitional clay suspension flows"

description: This dataset details experimental results of unsteady clay-laden suspension open-channel flows, focussing on the flow adaptation to changes in velocity within these transitional clay flows. <br>
DOI: 10.5281/zenodo.7469845
title: Data for "Flow adaptation to velocity changes in transitional clay suspension flows"

description: This dataset details experimental results of unsteady clay-laden suspension open-channel flows, focussing on the flow adaptation to changes in velocity within these transitional clay flows. <br>
DOI: 10.5281/zenodo.7250590
title: PCA Filtering of Apache Point Observatory 3.5m Agile Observations of LCROSS

description: This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the Agile instrument on the Apache Point Observatory 3.5m telescope. We use principal component analysis (PCA) filtering both to improve the coregistration of the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal. Full details are available in the published journal article: Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. https://doi.org/10.3390/rs15010037 This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.
DOI: 10.5281/zenodo.7250591
title: PCA Filtering of Apache Point Observatory 3.5m Agile Observations of LCROSS

description: This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the Agile instrument on the Apache Point Observatory 3.5m telescope. We use principal component analysis (PCA) filtering both to improve the coregistration of the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal. Full details are available in the published journal article: Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. https://doi.org/10.3390/rs15010037 This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.
DOI: 10.5281/zenodo.7469296
title: finals_2022_248

description: Log and boot files of game 248
DOI: 10.5281/zenodo.7469300
title: finals_2022_249

description: Log and boot files of game 249
DOI: 10.5281/zenodo.7469295
title: finals_2022_248

description: Log and boot files of game 248
DOI: 10.5281/zenodo.7469299
title: finals_2022_249

description: Log and boot files of game 249
DOI: 10.15472/hb5xgb
title: Lista de fauna y flora presente en el humedal La Moya de Enrique, departamento del Tolima

description: Los humedales sirven para mitigar los impactos generados por el ciclo hidrológico de una región y, paralelamente, proveen de hábitat a distintos organismos, incluyendo aquellas especies que recurren a la migración como estrategia adaptativa. Proveen de hábitat, alimento, refugio, y áreas de crianza y reproducción a un elevado número de especies de peces, aves, anfibios, reptiles, mamíferos e invertebrados. Son reconocidos por su alto nivel de endemismos, en particular de peces e invertebrados, por su fauna altamente especializada y por ser refugio de una gran diversidad de especies de aves migratorias. Los humedales tienen también un papel ecológico muy importante en el control de la erosión, la sedimentación y las inundaciones; en el abastecimiento y depuración del agua, y en el mantenimiento de pesquerías. En la actualidad estos sistemas han reducido su extensión considerablemente debido al drenado y relleno de sus áreas para diferentes usos (Aguilar, 2003).
description: En el departamento del Tolima se registran como los humedales más importantes 300 lagunas de cordillera, de origen glaciar, localizadas en la cordillera central en áreas de los Parques Nacionales Naturales y numerosas lagunas y sistemas de humedales en las zonas bajas. A pesar de esta variedad de humedales en el departamento del Tolima solo se han realizado evaluaciones iniciales de los humedales ubicados en el Parque Natural Nacional Los Nevados y en su área amortiguadora.
description: Teniendo en cuenta lo anterior y consciente de la importancia de los humedales, de la fauna y flora que los caracteriza, la Corporación Autónoma del Tolima CORTOLIMA y el Grupo de Investigación en Zoología (GIZ) de la Universidad del Tolima, han considerado muy relevante desarrollar el proyecto de estudio de diez humedales ubicados en las zonas bajas y altas del departamento del Tolima cuyo objetivo es la caracterización de la fauna y flora presente en ellos y generar la línea base para plantear el Plan de Manejo para su conservación.
description: En un esfuerzo de monitorear y documentar la biodiversidad presente en los humedales del Tolima, se muestreó la fauna y flora presente en el humedal La Moya de Enrique, localizado en el municipio de Ambalema. En total, se listan 133 taxones, distribuidos en dos reinos: Animalia (66.66%) y Plantae (33.33%). Un 85.85% está clasificado a nivel de especie y 14.14% a género.
DOI: 10.5281/zenodo.7258562
title: PCA Filtering of Magdalena Ridge Observatory 2.4m PHOTGJON Observations of LCROSS

description: This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the PHOTGJON instrument on the Magdalena Ridge Observatory 2.4m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal. Full details are available in the published journal article: Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. https://doi.org/10.3390/rs15010037 This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.
DOI: 10.5281/zenodo.7258563
title: PCA Filtering of Magdalena Ridge Observatory 2.4m PHOTGJON Observations of LCROSS

description: This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the PHOTGJON instrument on the Magdalena Ridge Observatory 2.4m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal. Full details are available in the published journal article: Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. https://doi.org/10.3390/rs15010037 This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.
DOI: 10.5281/zenodo.7258606
title: PCA Filtering of Magdalena Ridge Observatory 2.4m PHOTDOC Observations of LCROSS

description: This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the PHOTDOC instrument on the Magdalena Ridge Observatory 2.4m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal. Full details are available in the published journal article: Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. https://doi.org/10.3390/rs15010037 This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.
DOI: 10.5281/zenodo.7258607
title: PCA Filtering of Magdalena Ridge Observatory 2.4m PHOTDOC Observations of LCROSS

description: This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the PHOTDOC instrument on the Magdalena Ridge Observatory 2.4m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal. Full details are available in the published journal article: Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. https://doi.org/10.3390/rs15010037 This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.
DOI: 10.25318/9810052401-eng
title: Languages used at work by languages used at home, immigrant status and period of immigration: Canada, provinces and territories, census metropolitan areas and census agglomerations with parts

description: Data on language used most often at work by other language(s) used regularly at work, language spoken most often at home, all language(s) spoken at home, immigrant status and period of immigration and labour force status for the population aged 15 years and over who worked since 2020, in private households of Canada, provinces and territories, census metropolitan areas and census agglomerations with parts.