cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/ejbr-wd45
title: Dataset-of-HRP-38-test-system

description: Test systems are important tools and benchmark for power system research. Currently, there is a lack of standard test systems for modern transmission expansion planning (TEP) research, especially under high variable renewable energy (VRE) penetrations. This paper describes a 38-bus test system named the HRP-38 system dedicated to TEP. “HRP” stands for high renewable penetration. The objective of establishing such a system is to provide a consistent platform for different TEP methods to be tested and compared. The test system considers high VRE penetration of more than 30% energy share, which will be an important feature in many power systems in the future. Both AC and DC candidate lines are given to provide sufficient transmission planning alternatives. The complexity of the test system is well balanced considering calculation tractability and the ability to test the performance of the TEP planning model. The network topology, generation mix, and load characteristics are described in detail. Year-round time series of VRE (wind power and PV) output and load curves for each bus are provided to facilitate a detailed evaluation of the transmission grid configuration under high VRE penetration. Finally, three benchmark results with different optimization targets are provided for the HRP-38 system.
DOI: 10.17029/9337728a-3169-4ed2-bb3c-e8cb41928c42
title: Dataset for 'Female adult zebrafish (Danio rerio) show higher levels of anxiety-like behavior than males, but do not differ in learning and memory capacity'.

description: This dataset pertains to: Fontana, B.D., Cleal, M., Parker, M.O. (2019). Female adult zebrafish (Danio rerio) show higher levels of anxiety-like behavior than males, but do not differ in learning and memory capacity. European Journal of Neuroscience.
DOI: 10.5281/zenodo.3373439
title: Histology images from uniform tumor regions in TCGA Whole Slide Images

description: This is a set of 1,608,060 image patches of hematoxylin & eosin stained histological samples of various human cancers. Whole Slide Images of TCGA dataset from 32 solid cancer types were downloaded from GDC legacy database during December 1, 2016 to June 19, 2017. 9,662 diagnostic slides (the filename contains ’DXn’, where n stands for the slide number) from 7,951 patients in SVS format were then processed to annotate. For each slide, at least three representative tumor regions were selected as polygons by two trained pathologists using a Web browser-based software developed for this purpose. The pathologists selected uniform tumor regions and avoided the regions with noncancerous structures as much as possible. 926 slides were removed due to poor staining, low resolution, out of focus across a slide, no cancerous regions, or incorrect cancer types. Finally 8,736 diagnostic slides from 7175 patients were remained. Next, 10 patches with 6 magnification levels from 128 x 128 to 256 x 256 μm were randomly cropped with random angle from each annotated region using keras-OpenSlideGenerator (https://github.com/quolc/keras-OpenSlideGenerator). Each patch was selected so as not to include the region outside the annotated region. The selected region was resized to 256 x 256 pixels. Consequently, the number of patches subjected to the analysis ranged from 264,110 to 271,700. filename: [cancer_type]/[resolution]/[TCGA Barcode]/[region]-[number]-[pixel resolution in original WSI image].jpg [resolution] - 0-&gt; 0.5 μm/pixel - 1-&gt; 0.6 μm/pixel - 2-&gt; 0.7 μm/pixel - 3-&gt; 0.8 μm/pixel - 4-&gt; 0.9 μm/pixel - 5-&gt; 1.0 μm/pixel [TCGA Barcode] TCGA-XX-XXXX represents patient ID. Please see https://docs.gdc.cancer.gov/Encyclopedia/pages/TCGA_Barcode/ for detail.
DOI: 10.25919/5dafe58dd5f93
title: Parkes observations for project PX500 semester 2019OCTS_01

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: 10.5281/zenodo.3516226
title: Descriptors for Electron and Hole Charge Carriers in Metal Oxides: Calculations Dataset

description: Calculation dataset required in order to carry out the full analysis in This repository. Outputs from hybrid Density Functional Theory calculations using the Vienna Ab-initio Simulation Package (VASP) are provided. See the main repository for details on analysis steps.
DOI: 10.5281/zenodo.3516225
title: Descriptors for Electron and Hole Charge Carriers in Metal Oxides: Calculations Dataset

description: Calculation dataset required in order to carry out the full analysis in This repository. Outputs from hybrid Density Functional Theory calculations using the Vienna Ab-initio Simulation Package (VASP) are provided. See the main repository for details on analysis steps.
DOI: 10.21227/ft3z-9y17
title: Videos - Optical sectioning microscopy through single-shot lightfield protocol

description: Optical sectioning microscopy is usually performed by means of a scanning, multi-shot procedure in combination with non-uniform illumination. In this paper we change the paradigm and report a method that is based in the lightfield concept, and that provides optical sectioning for 3D microscopy images after a single-shot capture. To do this we first capture multiple orthographic perspectives of the sample by means of Fourier-domain integral microscopy (FiMic). The second stage of our protocol is the application of a novel refocusing algorithm that is able to produce optical sectioning in real time, and with no resolution worsening, in the case of sparse fluorescent samples. We provide the theoretical derivation of the algorithm, and demonstrate its utility by applying it to simulations and experimental data.
DOI: 10.21227/bkd4-2m12
title: Videos - Optical sectioning microscopy through single-shot lightfield protocol

description: Optical sectioning microscopy is usually performed by means of a scanning, multi-shot procedure in combination with non-uniform illumination. In this paper we change the paradigm and report a method that is based in the lightfield concept, and that provides optical sectioning for 3D microscopy images after a single-shot capture. To do this we first capture multiple orthographic perspectives of the sample by means of Fourier-domain integral microscopy (FiMic). The second stage of our protocol is the application of a novel refocusing algorithm that is able to produce optical sectioning in real time, and with no resolution worsening, in the case of sparse fluorescent samples. We provide the theoretical derivation of the algorithm, and demonstrate its utility by applying it to simulations and experimental data.
DOI: 10.5285/7bc176d9-88bc-4a46-93ff-5f283227bc23
title: Groundwater flow model of aquifer system, Kwale County, Kenya
DOI: 10.5519/qd.znyz4za5
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5 records)
DOI: 10.5281/zenodo.3966452
title: Equilibration simulation of a Lo/Ld phase coexistence in a PSM/DOPC/Cholesterol mixture, larger membrane

description: To be added.
DOI: 10.5281/zenodo.3966451
title: Equilibration simulation of a Lo/Ld phase coexistence in a PSM/DOPC/Cholesterol mixture, larger membrane

description: To be added.
DOI: 10.21227/dgak-c563
title: New figure from the simulation of the odometric model

description: The odometric model is simulated herein. We described the trajectory of such one odometric model, with the delta of the heading angle given as one parameter of the simulation. The iterations show that the trajectory is well in the continuity of the variations of the heading angle. Moreover the distance in X and in Y are shown for the vehicle to be driven in the trajectory of the odometric model.
DOI: 10.21227/ryt3-gg06
title: New figure from the simulation of the odometric model

description: The odometric model is simulated herein. We described the trajectory of such one odometric model, with the delta of the heading angle given as one parameter of the simulation. The iterations show that the trajectory is well in the continuity of the variations of the heading angle. Moreover the distance in X and in Y are shown for the vehicle to be driven in the trajectory of the odometric model.
DOI: 10.25919/5db02c185f645
title: Parkes observations for project P1031 semester 2019OCTS_01

description: We propose to study the pulsar emission mechanism using novel statistical methods that enable us to study the dynamics of the radio pulsar signal on short time scales (e.g. quasi-periodic sub-pulse structure, transitions between orthogonally polarized modes of emission, nulling, etc.) without needing to detect individual pulses. This pilot study will pave the way for a large-scale survey of dynamic/transient emission properties over a broad range of the pulsar population.
DOI: 10.5281/zenodo.3515119
title: 0.1 ms of asymmetric GM1:POPC/POPC (7:93/99) bilayer

description: Lollipop-like GM1. First 25 microseconds of total 100 microseconds.
DOI: 10.5281/zenodo.3515118
title: 0.1 ms of asymmetric GM1:POPC/POPC (7:93/99) bilayer

description: Lollipop-like GM1. Third 25 microseconds of total 100 microseconds.
DOI: 10.5281/zenodo.3966801
title: supplementary of UTUC-Normal

description: Accompanies a manuscript regarding the mutant clonal expansion in human morphologically normal urothelium (bladder and ureter). Contains: 1. Input, output and R code for clustering somatic mutations based on the Dirichlet process. 2. Input for the ABSOLUTE analysis (copy number alteration analysis). 3. Input for the ReCapSeg analysis (copy number alteration analysis). 4. Output figures for the ABSOLUTE analysis (copy number alteration analysis). 5. Output segmentation files for the ABSOLUTE analysis (copy number alteration analysis). 6. Output for the Sequenza analysis (copy number alteration analysis). 7. Input VCF files for the mutational signature analysis. 8. VCF files containing all SNVs. 9. VCF files containing all INDELs.
DOI: 10.5281/zenodo.3726414
title: supplementary of UTUC-Normal

description: Accompanies a manuscript regarding the mutant clonal expansion in human morphologically normal urothelium (bladder and ureter). Contains: 1. Input, output and R code for clustering somatic mutations based on the Dirichlet process. 2. Input for the ABSOLUTE analysis (copy number alteration analysis). 3. Input for the ReCapSeg analysis (copy number alteration analysis). 4. Output figures for the ABSOLUTE analysis (copy number alteration analysis). 5. Output segmentation files for the ABSOLUTE analysis (copy number alteration analysis). 6. Output for the Sequenza analysis (copy number alteration analysis). 7. Input VCF files for the mutational signature analysis. 8. VCF files containing all SNVs. 9. VCF files containing all INDELs.
DOI: 10.15468/jobeov
title: The Type Specimens of the Odonata in the Entomological Institute, Hokkaido Unversity

description: This dataset contains the digitized treatments in Plazi based on the original journal article Syoziro Asahina (1963): The Type Specimens of the Odonata in the Entomological Institute, Hokkaido Unversity. Insecta matsumurana 24: 57-65, DOI: http://doi.org/10.5281/zenodo.3516659
DOI: 10.5281/zenodo.3516925
title: Replication Data for: Determination of Intrinsic Effective Fields and Microwave Polarizations by High-Resolution Spectroscopy of Single NV Center Spins

description: Data repository for: <strong>Determination of Intrinsic Effective Fields and Microwave Polarizations by High-Resolution Spectroscopy of Single NV Center Spins</strong> <em>Data description.pdf</em> describes the uploaded data.<br> <em>Data.xlsx</em> is the data represented in the paper.<br> <em>Esrfit_Npeak.m</em>, <em>Esrfit_xN.m</em>, <em>GaussianFunc.m</em>, <em>Gaussian_xN_Func.m</em>, <em>Lorentz_Func.m</em>, <em>Lorentz_xN_Func.m</em>, <em>Rabifit_xN.m</em>, <em>Rabi_xN_Func.m</em>, <em>FourierTransformRabi.m</em> are Matlab code files to transform and fit the data.
description: We gratefully acknowledge financial support through the NCCR QSIT (Grant No.185902), through the Swiss Nanoscience Institute, through the EU Quantum Flagship project ASTERIQS (Grant No. 820394) and through the Swiss NSF Project Grant No. 169321.
description: {"references": ["arxiv:1907.08631"]}
DOI: 10.5281/zenodo.3516928
title: Hydroperiod Doñana 1974/2019

description: Time Series of annual Hydroperiods derived from Landsat MSS, TM, ETM+ & OLI in the Path 202 Row 34 (Doñana) cover the period 1974-2018.
DOI: 10.5281/zenodo.3516929
title: Hydroperiod Doñana 1974/2019

description: Time Series of annual Hydroperiods derived from Landsat MSS, TM, ETM+ & OLI in the Path 202 Row 34 (Doñana) cover the period 1974-2018.
DOI: 10.17632/54mf3v6mnk.1
title: Tolerance modelling of stress distribution in thin composite plates with dense system of beams in two directions

description: Raw data of the results obtained within several models analysed in article "Tolerance modelling of stress distribution in thin composite plates with dense system of beams in two directions"
DOI: 10.17632/54mf3v6mnk.2
title: Tolerance modelling of stress distribution in thin composite plates with dense system of beams in two directions

description: Raw data of the results obtained within several models analysed in article "Tolerance modelling of stress distribution in thin composite plates with dense system of beams in two directions"
DOI: 10.7910/dvn/gle7om/0bdis1
title: phillips_jhr_cleandata.tab

description: :unav
DOI: 10.7910/dvn/gle7om/hrrcxb
title: tl_2010_11001_tract10.shp.xml

description: :unav
DOI: 10.7910/dvn/gle7om/1gukpv
title: acs_poverty.tab

description: :unav
DOI: 10.7910/dvn/gle7om/6gi1ai
title: 0_master_replication.do

description: :unav
DOI: 10.7910/dvn/gle7om/euztic
title: acs.tab

description: :unav
DOI: 10.7910/dvn/gle7om/gxmkjh
title: dctracts.tab

description: :unav
DOI: 10.7910/dvn/gle7om/itcnfu
title: avg_dist.tab

description: :unav
DOI: 10.7910/dvn/gle7om/jx7pl2
title: dctracts-coord.tab

description: :unav
DOI: 10.7910/dvn/gle7om/szntom
title: tl_2010_11001_tract10.dbf

description: :unav
DOI: 10.7910/dvn/gle7om/tokili
title: tl_2010_11001_tract10.shp

description: :unav
DOI: 10.7910/dvn/gle7om/v8ncub
title: tl_2010_11001_tract10.prj

description: :unav
DOI: 10.7910/dvn/gle7om/3ks6vq
title: 2_context.do

description: :unav
DOI: 10.7910/dvn/gle7om/fd2zfp
title: census_gazetteer.tab

description: :unav
DOI: 10.7910/dvn/gle7om/hg0oms
title: avg_dist_app_tojob.tab

description: :unav
DOI: 10.7910/dvn/gle7om/tdaytf
title: 3_treatassign.do

description: :unav
DOI: 10.7910/dvn/gle7om/u47hx9
title: 1_deidentifying.do

description: :unav
DOI: 10.7910/dvn/gle7om/x60wtw
title: data dictionary.pdf

description: :unav
DOI: 10.7910/dvn/gle7om/xo1xoh
title: 4_results.do

description: :unav
DOI: 10.7910/dvn/gle7om/zkiag2
title: acs_2015_pov.tab

description: :unav
DOI: 10.7910/dvn/elg3de/5dscov
title: Graph3.tab

description: :unav
DOI: 10.7910/dvn/elg3de/bppuyi
title: Inv_Dist.tab

description: :unav
DOI: 10.7910/dvn/elg3de/cxoxcn
title: Contiguity.tab

description: :unav
DOI: 10.7910/dvn/elg3de/s9cxre
title: Diff_PoP_contiguity.tab

description: :unav
DOI: 10.7910/dvn/elg3de/tgswk9
title: Data Frame.tab

description: :unav
DOI: 10.7910/dvn/elg3de/z9wcof
title: readme.txt

description: :unav
DOI: 10.7910/dvn/gle7om/rc1ggl
title: tl_2010_11001_tract10.shx

description: :unav
DOI: 10.7910/dvn/elg3de/cywacc
title: Diff_GDP_contiguity.tab

description: :unav
DOI: 10.7910/dvn/elg3de/f5tj74
title: Codebook.pdf
DOI: 10.7910/dvn/elg3de/nfegfr
title: Contiguity_Culture.tab

description: :unav
DOI: 10.7910/dvn/elg3de/uagyrz
title: Final Dofile.do

description: :unav
DOI: 10.7910/dvn/elg3de/unqrh9
title: Graph Spatial Effects.tab

description: :unav
DOI: 10.17036/researchdata.aston.ac.uk.00000442
title: Data underpinning article: "Noise Figure Evaluation of Polarization-insensitive Single-pump Fiber Optical Parametric Amplifiers"
DOI: 10.21227/jp18-kk82
title: Date Fruit Dataset for Automated Harvesting and Visual Yield Estimation

description: The date fruit dataset was created to address the requirements of many applications in the pre-harvesting and harvesting stages. The two most important applications are automatic harvesting and visual yield estimation. The dataset is divided into two subsets and each of them is oriented into one of these two applications. The first dataset consists of 8079 images of more than 350 date bunches captured from 29 date palms. The date bunches belong to five date types: Naboot Saif, Khalas, Barhi, Meneifi, and Sullaj. The pictures of date bunches were captured using a color camera in six imaging sessions. The imaging sessions covered all date maturity stages: immature, Khalal, Rutab, and Tamar. The dataset is provided with a large degree of variations to reflect the challenges in natural environments and date fruit orchards. These variations in images include different angels and scales, different daylight conditions having poor illumination images, and date bunches covered by bags. The dataset is fully labeled according to type, maturity, and harvesting decision. We can use this dataset in many applications including fruit detection, segmentation, classification, maturity analysis, and automatic harvesting. The second dataset contains images, videos, and weight measurements to help in many applications such as yield estimation. In this dataset, we marked date bunches for selected palms, recorded 360° video for each palm, and measured their data (height, trunk circumference, total yield, number of bunches, and weight of bunches). We also captured images of each bunch from different angles before harvesting and on a graph paper after harvesting. Both datasets have been arranged with a coding scheme to simplify referring, linking, and facilitating future extensions. Update: Dataset-2 was uploaded. Date: 16/05/2019. The PDF files of dataset documentation and experiment papers were attached. Date: 23/10/2019.
DOI: 10.21227/cmkq-0h66
title: Date Fruit Dataset for Automated Harvesting and Visual Yield Estimation

description: The date fruit dataset was created to address the requirements of many applications in the pre-harvesting and harvesting stages. The two most important applications are automatic harvesting and visual yield estimation. The dataset is divided into two subsets and each of them is oriented into one of these two applications. The first dataset consists of 8079 images of more than 350 date bunches captured from 29 date palms. The date bunches belong to five date types: Naboot Saif, Khalas, Barhi, Meneifi, and Sullaj. The pictures of date bunches were captured using a color camera in six imaging sessions. The imaging sessions covered all date maturity stages: immature, Khalal, Rutab, and Tamar. The dataset is provided with a large degree of variations to reflect the challenges in natural environments and date fruit orchards. These variations in images include different angels and scales, different daylight conditions having poor illumination images, and date bunches covered by bags. The dataset is fully labeled according to type, maturity, and harvesting decision. We can use this dataset in many applications including fruit detection, segmentation, classification, maturity analysis, and automatic harvesting. The second dataset contains images, videos, and weight measurements to help in many applications such as yield estimation. In this dataset, we marked date bunches for selected palms, recorded 360° video for each palm, and measured their data (height, trunk circumference, total yield, number of bunches, and weight of bunches). We also captured images of each bunch from different angles before harvesting and on a graph paper after harvesting. Both datasets have been arranged with a coding scheme to simplify referring, linking, and facilitating future extensions. Update: Dataset-2 was uploaded. Date: 16/05/2019. The PDF files of dataset documentation and experiment papers were attached. Date: 23/10/2019.
DOI: 10.5281/zenodo.3516687
title: Spożycie mięsa wieprzowego i drobiowego oraz ich przetworów w Polsce, w 2016 roku

description: Opracowanie tabelaryczne o spożyciu i wydatkach ludności w 2016 roku na podstawie danych źródłowych z badania budżetów gospodarstw domowych, dane pozyskano z Głównego Urzędu Statystycznego
DOI: 10.5281/zenodo.3516688
title: Spożycie mięsa wieprzowego i drobiowego oraz ich przetworów w Polsce, w 2016 roku

description: Opracowanie tabelaryczne o spożyciu i wydatkach ludności w 2016 roku na podstawie danych źródłowych z badania budżetów gospodarstw domowych, dane pozyskano z Głównego Urzędu Statystycznego
DOI: 10.21227/51q5-jk18
title: Date Fruit Dataset for Automated Harvesting and Visual Yield Estimation

description: The date fruit dataset was created to address the requirements of many applications in the pre-harvesting and harvesting stages. The two most important applications are automatic harvesting and visual yield estimation. The dataset is divided into two subsets and each of them is oriented into one of these two applications. The first dataset consists of 8079 images of more than 350 date bunches captured from 29 date palms. The date bunches belong to five date types: Naboot Saif, Khalas, Barhi, Meneifi, and Sullaj. The pictures of date bunches were captured using a color camera in six imaging sessions. The imaging sessions covered all date maturity stages: immature, Khalal, Rutab, and Tamar. The dataset is provided with a large degree of variations to reflect the challenges in natural environments and date fruit orchards. These variations in images include different angels and scales, different daylight conditions having poor illumination images, and date bunches covered by bags. The dataset is fully labeled according to type, maturity, and harvesting decision. We can use this dataset in many applications including fruit detection, segmentation, classification, maturity analysis, and automatic harvesting. The second dataset contains images, videos, and weight measurements to help in many applications such as yield estimation. In this dataset, we marked date bunches for selected palms, recorded 360° video for each palm, and measured their data (height, trunk circumference, total yield, number of bunches, and weight of bunches). We also captured images of each bunch from different angles before harvesting and on a graph paper after harvesting. Both datasets have been arranged with a coding scheme to simplify referring, linking, and facilitating future extensions. Please cite the related articles as:[1] H. Altaheri, M. Alsulaiman, G. Muhammad, S. U. Amin, M. Bencherif, and M. Mekhtiche, “Date Fruit Dataset for Intelligent Harvesting", Data in Brief, 2019. DOI: 10.1016/j.dib.2019.104514[2] H. Altaheri, M. Alsulaiman and G. Muhammad, "Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning", in IEEE Access, vol. 7, pp. 117115-117133, 2019. DOI: 10.1109/ACCESS.2019.2936536 Update: Dataset-2 was uploaded. Date: 16/05/2019. The PDF files of dataset documentation and experiment papers were attached. Date: 23/10/2019.