cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15472/7rbyue
title: Ictiofauna presente en fuentes abastecedoras en La Sierpe - Osbezcac (cuenca del rio Saija, Timbiquí - Cauca) 2013

description: Desde el punto de vista faunístico, el buen funcionamiento de un ecosistema hídrico, está influenciado por varios factores, determinados por la composición y el rol que juegan las especies en el mismo, pues la presencia de especies con ciertos requerimientos ecológicos y con funciones específicas y relevantes, actúan como indicadoras de calidad del ecosistema. Funciones como la dispersión, polinización, depredación y control de poblaciones, así como el reciclaje de nutrientes, son realizadas por los distintos grupos fáunicos, los cuales se relacionan de muchas maneras con el entorno, bien sea directamente con el recurso hidrobiológico disponible o con la vegetación adyacente a la fuente hídrica y otras lo hacen de manera indirecta, formando parte importante en los distintos niveles o eslabones de las cadenas tróficas ocurrentes en dicho ecosistema. Así mismo, estas encuentran en el ambiente las condiciones físicas de hábitat para establecerse y desarrollar procesos ecológicos necesarios para la dinámica ecosistémica; de manera que aprovechan todos los elementos disponibles en el ambiente como son la vegetación, troncos caídos, raíces de árboles y hojarasca, para construir sus refugios; también encuentran la protección y sombra que le brinda dicha vegetación a las especies tanto presas, como predadoras; y el recurso agua, imprescindible en el ambiente como hábitat para especies acuícolas, la cual también es aprovechada por la fauna para fines como obtención de alimento, consumo directo y baño, y a partir de la cual se inician complejos procesos ecológicos de carácter trófico. Sin embargo, a pesar de la importancia que presentan los ecosistemas acuáticos para la fauna y para las comunidades que de ellos se benefician, existen amenazas naturales o antrópicas potenciales que ponen en riesgo la capacidad de dicho ecosistema para generar bienes y servicios a las poblaciones humanas; por lo que conocer su potencial faunístico y el papel que las especies desempeñan en la funcionalidad y mantenimiento de este ecosistema, crean la necesidad de emprender investigaciones dirigidas a generar conocimiento para fines de manejo, ordenación y planificación del mismo. Por lo tanto el presente estudio tiene como propósito analizar los procesos ecológicos de la fauna que ayuden a identificar la calidad ecológica de las fuentes abastecedoras de agua en la cuenca del río Saija.
DOI: 10.7910/dvn/3ycb56/naoyp5
title: Pseudoword Passage Reading.tab

description: :unav
DOI: 10.5285/2503df629a8a485e8582e5150876b210
title: Thunderstorm occurrence at ten sites across the UK digitsied from the UK Met Office Monthly Weather Records (1884-1993)

description: This dataset contains UK Met Office Daily Weather Reports (DWR) from ten sites from the UK Met Office Monthly Weather Records (1884-1993), using the recently-scanned UK Met Office Monthly Weather Reports (MWR). The data are presented as is, with no attempt to provide any corrections or calibration. Approximately half the stations exhibit sharp drops in thunderdays at various points between 1960 and 1990. Comparison with nearby Met Office Integrated Data Archive System (MIDAS) stations suggests the low thunderdays are the result of changes in observing practice, rather than genuine changes in thunderstorm occurrence. These potential data issues limit interpretation of the long-term trends. DWR contain extensive logs of UK thunderstorm activity, in the form of thunderday observations. To date, only a very small fraction of these data have been digitised as part of the MIDAS dataset, and exclusively after 1950.
DOI: 10.5519/qd.zxgegl34
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.21227/hag1-0p74
title: 7.5kW Wind turbine

description: SImulation resultsunder construction, will be submitted shortly
DOI: 10.5281/zenodo.3365468
title: Grain size and volume effects on granular flow behavior and deposit characteristics: implications for rock avalanche dynamics

description: Seven excel files which includes the data supporting Figure 5, Figure 6, Figure 7, Figure 8, Figure 10, Figure 12 and Figure 14 is uploaded saparately. Data Set S1. Data supporting the relationship between flow height and time in Figure 5. Data Set S2. Data supporting normalized velocity profiles and normalized shear rate profiles in Figures 6(a)-6(d). Data Set S3(a). Data supporting the relationships between mean grain size and global shear rate in Figure 7(a). Data Set S3(b). Data supporting the relationships between mean grain size and Savage number in Figure 7(a). Data Set S4(a). Data supporting the relationships between depth averaged velocity and time in Figure 8(a). Data Set S4(b). Data supporting the relationships between depth averaged velocity and time in Figure 8(b). Data Set S4(c). Data supporting the relationships between mean grain size and equivalent friction coefficient in Figure 8(c). Data Set S5(a). Data supporting Figures 10(a) and 10(b). Data Set S5(b). Data supporting the relationships between relative flow height and λ in Figure 10(c). Data Set S5(c). Data supporting the relationships between relative flow height and λ in Figure 10(d). Data Set S6. Data supporting the relationships between global shear rate and equivalent friction coefficient in Figure 12. Data Set S6. Data supporting the relationships between normalized flow height and Savage number in Figure 14.
description: {"references": ["Iverson, R. M. (1997). The physics of debris flows. Reviews of Geophysics, 35(3), 245\u2013296. https://doi.org/10.1029/97RG00426"]}
DOI: 10.25345/c58q07
title: MassIVE MSV000084197 - 16 mzXML trial Shimadzu with raw .lcd file and mzXML converted with MSConvet (peak picking level 1-2, 32-bit, no compression)
title: 16 mzXML trial Shimadzu with raw .lcd file and mzXML converted with MSConvet (peak picking level 1-2, 32-bit, no compression)
DOI: 10.5281/zenodo.3365707
title: Higher education learning

description: Data for higher education pedagogies and purposes
DOI: 10.5281/zenodo.3365708
title: Higher education learning

description: Data for higher education pedagogies and purposes
DOI: 10.21227/ye0h-kg56
title: The USyd Campus Dataset

description: Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios. For certain applications, autonomous vehicles in particular, algorithm failures can lead to potentially catastrophic consequences. To minimise this risk, it is essential to evaluate the robustness and reliability of algorithms before deploying them in real life applications. This paper presents a long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of Sydney campus and surrounds. The USyd Campus Dataset has been designed to aid in the development, validation and testing of algorithm robustness under a wide variety of conditions. It includes multiple sensor modalities and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes. The dataset also includes a semantic segmentation dataset with different illumination conditions and camera perspectives, and a ‘Road’ class image dataset which is automatically generated using lidar. To facilitate the dataset use by researchers and practitioners, we also provide a set of development tools for the analysis and visualisation of the dataset.
DOI: 10.24378/exe.1723
title: Cost-effectiveness analysis of reflex testing for Lynch syndrome in women with endometrial cancer in the UK setting (dataset)

description: This is the dataset used for the Snowsill et al. (2019) article "Cost-effectiveness analysis of reflex testing for Lynch syndrome in women with endometrial cancer in the UK setting" published in PLOS ONE.
description: This Excel workbook contains an economic model used for conducting an economic evaluation (cost-effectiveness analysis) of reflex testing for Lynch syndrome in women with endometrial cancer in the UK setting. It accompanies an article published in PLOS ONE by Snowsill et al.: Snowsill TM, Ryan NAJ, Crosbie EJ, Frayling IM, Evans DG, Hyde CJ. Cost-effectiveness analysis of reflex testing for Lynch syndrome in women with endometrial cancer in the UK setting. PLOS ONE (accepted). The workbook can be opened in Microsoft Excel. It has been opened in Excel 2013 but is expected to also function in Excel 2010. It may contain some functionality which is not compatible with Excel 2007.
description: The article associated with this dataset is located in ORE at: http://hdl.handle.net/10871/38454
DOI: 10.25919/5d509f123b856
title: Parkes observations for project PX500 semester 2019APRS_16

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX501) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first, relating to this proposal, will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.17632/9nfxyvfk9n.1
title: Data for: Deep Sulfate-Methane-Transition and sediment diagenesis in the Gulf of Alaska (IODP Site U1417)

description: Supplemental Data to Zindorf et al., Deep Sulfate-Methane-Transition and sediment diagenesis in the Gulf of Alaska (IODP Site U1417)
DOI: 10.21227/r98e-q692
title: The USyd Campus Dataset

description: Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios. For certain applications, autonomous vehicles in particular, algorithm failures can lead to potentially catastrophic consequences. To minimise this risk, it is essential to evaluate the robustness and reliability of algorithms before deploying them in real life applications. This paper presents a long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of Sydney campus and surrounds. The USyd Campus Dataset has been designed to aid in the development, validation and testing of algorithm robustness under a wide variety of conditions. It includes multiple sensor modalities and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes. The dataset also includes a semantic segmentation dataset with different illumination conditions and camera perspectives, and a ‘Road’ class image dataset which is automatically generated using lidar. To facilitate the dataset use by researchers and practitioners, we also provide a set of development tools for the analysis and visualisation of the dataset.
DOI: 10.7910/dvn/6rddqh/kbpk2t
title: simulation.tab

description: Input data for simulation.py script
DOI: 10.7910/dvn/6rddqh/qg7sgd
title: estimation.tab

description: Input data for estimation.do script
DOI: 10.7910/dvn/nnajlx/ifsy41
title: Data.tab

description: :unav
DOI: 10.7910/dvn/nnajlx/unuwcd
title: Do_file.do

description: :unav
DOI: 10.17632/3ppn393r6v.1
title: Supplementary figures: slices, histograms & PCA

description: This dataset comprises example slices, histograms and principal components analyses of all cores analyzed.
DOI: 10.17632/tr37c9cfz2.1
title: CT data of sediment cores

description: The component distributions, misclassification probabilities, mean HU, skewness and kurtosis for all cores
DOI: 10.21227/7dnv-7w81
title: The USyd Campus Dataset

description: Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios. For certain applications, autonomous vehicles in particular, algorithm failures can lead to potentially catastrophic consequences. To minimise this risk, it is essential to evaluate the robustness and reliability of algorithms before deploying them in real life applications. This paper presents a long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of Sydney campus and surrounds. The USyd Campus Dataset has been designed to aid in the development, validation and testing of algorithm robustness under a wide variety of conditions. It includes multiple sensor modalities and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes. The dataset also includes a semantic segmentation dataset with different illumination conditions and camera perspectives, and a ‘Road’ class image dataset which is automatically generated using lidar. To facilitate the dataset use by researchers and practitioners, we also provide a set of development tools for the analysis and visualisation of the dataset.
DOI: 10.7910/dvn/a3m7py/4ewgl7
title: README.txt

description: :unav
DOI: 10.7910/dvn/a3m7py/fnznwa
title: FigureA2.R

description: :unav
DOI: 10.7910/dvn/a3m7py/peq7hg
title: -controls.do

description: :unav
DOI: 10.7910/dvn/a3m7py/tqfiac
title: acs2015.dta

description: :unav
DOI: 10.7910/dvn/a3m7py/uacjnh
title: appendix.do

description: :unav
DOI: 10.7910/dvn/a3m7py/d9qonq
title: nes2016.tab

description: :unav
DOI: 10.7910/dvn/a3m7py/dvvlgs
title: TableA15.do

description: :unav
DOI: 10.7910/dvn/a3m7py/gcodjc
title: cces2016.dta

description: :unav
DOI: 10.7910/dvn/a3m7py/iti86v
title: TableA3.do

description: :unav
DOI: 10.7910/dvn/a3m7py/mliice
title: survey.tab

description: :unav
DOI: 10.7910/dvn/a3m7py/mlma4i
title: zips.tab

description: :unav
DOI: 10.7910/dvn/a3m7py/t6ldoi
title: scf2016.tab

description: :unav
DOI: 10.7910/dvn/a3m7py/w0qa72
title: cps2016.dta

description: :unav
DOI: 10.7910/dvn/a3m7py/xffzzb
title: TableA2.do

description: :unav
DOI: 10.5281/zenodo.3365933
title: Datasets as used in Beckmann et al. The Cryosphere

description: These are the data sets for all 12 glacier. The catchment area of each glacier can be found in _catchment_areas. The derived 1D glacier geometry of each glacier is found in glacier_name/glacier.nc. It contains glacier values as well as the lon,lat coordinates of the center line. In glacier_name/out/restart.nc is the stable state version after running the model with correction flux (command found in glacier_name/out/command). Restart.nc was then run for another 100 years with the same parameters to assure a quasi-stable state. Then the plume model was switched on an parameters fwd and beta where altered as described in Beckmann et al.(2019). As depicted in the Supporting Information of Beckmann et al. (2019) , the folders also contain the temperature -salinity-profiles of each glacier fjord from CTD measurements(Oceanfjord*.nc) and reanalysis data (*_ocean.nc), as well as the subglacial discharge (subglacial_discharge.nc) and surface mass balance development (MAR_MIROC5_ref_NCEP_rcp85_1900_2100_100m.nc) under RCP 8.5.
description: This work was funded by Leibniz-Gemeinschaft, WGL Pakt für Forschung SAW-2014-PIK-1.
DOI: 10.7910/dvn/a3m7py/6ifszt
title: county-income.tab

description: :unav
DOI: 10.7910/dvn/a3m7py/h7jmah
title: FigureA1.do

description: :unav
DOI: 10.7910/dvn/a3m7py/hco2bc
title: zip_tract.tab

description: :unav
DOI: 10.7910/dvn/a3m7py/i0yevv
title: geocode_zips.txt

description: :unav
DOI: 10.7910/dvn/a3m7py/otzqbs
title: paper.do

description: :unav
DOI: 10.17632/ybs776vkyc.1
title: Data for: Migration model of hydrocarbons in the slope of the superimposed foreland basin: A case study from the South Junggar, NW China

description: Migration model of hydrocarbons in the slope of the superimposed foreland basin: A case study from the South Junggar Basin, NW China
DOI: 10.21227/0m0v-2h68
title: The USyd Campus Dataset

description: Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios. For certain applications, autonomous vehicles in particular, algorithm failures can lead to potentially catastrophic consequences. To minimise this risk, it is essential to evaluate the robustness and reliability of algorithms before deploying them in real life applications. This paper presents a long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of Sydney campus and surrounds. The USyd Campus Dataset has been designed to aid in the development, validation and testing of algorithm robustness under a wide variety of conditions. It includes multiple sensor modalities and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes. The dataset also includes a semantic segmentation dataset with different illumination conditions and camera perspectives, and a ‘Road’ class image dataset which is automatically generated using lidar. To facilitate the dataset use by researchers and practitioners, we also provide a set of development tools for the analysis and visualisation of the dataset.
DOI: 10.25582/data.2021-04.2204155/2151436
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2212035/2151437
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2211842/2151438
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-05.2219921/2151439
title: Data for EMSL Project 51287 from May 2021
DOI: 10.25582/data.2021-05.2219891/2151440
title: Data for EMSL Project 51287 from May 2021
DOI: 10.25582/data.2021-04.2216221/2151441
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2211334/2151442
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-05.2221685/2151443
title: Data for EMSL Project 51287 from May 2021
DOI: 10.25582/data.2021-04.2202682/2151444
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2212159/2151445
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2204187/2151446
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2212354/2151447
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2212158/2151448
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2208975/2151449
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2208989/2151450
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2204669/2151451
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2211305/2151452
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2207642/2151453
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2211335/2151454
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2203612/2151455
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-05.2218596/2151456
title: Data for EMSL Project 51287 from May 2021
DOI: 10.25582/data.2021-04.2202623/2151457
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2203644/2151458
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-05.2218610/2151459
title: Data for EMSL Project 51287 from May 2021
DOI: 10.25582/data.2021-04.2202670/2151460
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2202794/2151461
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2212974/2151462
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-05.2218278/2151463
title: Data for EMSL Project 51287 from May 2021
DOI: 10.25582/data.2021-04.2209619/2151464
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2202676/2151465
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-05.2218697/2151466
title: Data for EMSL Project 51287 from May 2021
DOI: 10.25582/data.2021-05.2219831/2151467
title: Data for EMSL Project 51287 from May 2021
DOI: 10.25582/data.2021-04.2202599/2151468
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2216163/2151469
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2216246/2151470
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2211328/2151471
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-05.2218937/2151472
title: Data for EMSL Project 51287 from May 2021
DOI: 10.25582/data.2021-04.2213397/2151473
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-05.2221652/2151474
title: Data for EMSL Project 51287 from May 2021
DOI: 10.25582/data.2021-05.2218589/2151475
title: Data for EMSL Project 51287 from May 2021
DOI: 10.25582/data.2021-04.2202668/2151476
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2211338/2151477
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-05.2219896/2151478
title: Data for EMSL Project 51287 from May 2021
DOI: 10.25582/data.2021-04.2202655/2151479
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2212297/2151480
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2211304/2151481
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2211365/2151482
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2211352/2151483
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2211709/2151484
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2203689/2151485
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2211370/2151486
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2202684/2151487
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2211311/2151488
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2212473/2151489
title: Data for EMSL Project 51287 from April 2021
DOI: 10.25582/data.2021-04.2213474/2151490
title: Data for EMSL Project 51287 from April 2021