cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2dxk30
title: CCDC 2231972: Experimental Crystal Structure Determination

description: Related Article: Qiu-Qin Huang, Jian Chen, Mei-Yue Hu, Yu-Ling Wang, Yi Li, FengFu Fu, Qiao-Hua Wei|2023|Inorg.Chem.|62|14998|doi:10.1021/acs.inorgchem.3c01830
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dxk52
title: CCDC 2231974: Experimental Crystal Structure Determination

description: Related Article: Qiu-Qin Huang, Jian Chen, Mei-Yue Hu, Yu-Ling Wang, Yi Li, FengFu Fu, Qiao-Hua Wei|2023|Inorg.Chem.|62|14998|doi:10.1021/acs.inorgchem.3c01830
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.24226444.v1
title: <b>ADHERENCE TO ANTIRETROVIRAL THERAPY AMONG HIV PATIENTS IN GHANA: A SYSTEMATIC REVIEW AND META-ANALYSIS</b>

description: Meta analysis data on adherence to ART.
DOI: 10.6084/m9.figshare.24226444
title: <b>ADHERENCE TO ANTIRETROVIRAL THERAPY AMONG HIV PATIENTS IN GHANA: A SYSTEMATIC REVIEW AND META-ANALYSIS</b>

description: Meta analysis data on adherence to ART.
DOI: 10.5281/zenodo.8397451
title: Prediction-Powered Inference: Data Sets

description: Data sets used in the paper "Prediction-Powered Inference."
DOI: 10.5281/zenodo.8397450
title: Prediction-Powered Inference: Data Sets

description: Data sets used in the paper "Prediction-Powered Inference."
DOI: 10.48366/r642232
title: Overview of SemTab@ISWC 2020 systems papers

description: The following comparison table presents the systems proposed by the SemTab community in 2020 for tabular data annotation.
DOI: 10.34740/kaggle/dsv/6588784
title: Global Crypto Currency Price Database
DOI: 10.5281/zenodo.8397463
title: SG - September 2023

description: Measurements of SG (Global) in September 2023 Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397462
title: SG - September 2023

description: Measurements of SG (Global) in September 2023 Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397465
title: LPL1 - September 2023

description: Measurements of LPL1 (Teide NP) in September 2023 Tenerife Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397464
title: LPL1 - September 2023

description: Measurements of LPL1 (Teide NP) in September 2023 Tenerife Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397467
title: LPL2 - September 2023

description: Measurements of LPL2 (Caldera de Taburiente NP) in September 2023 La Palma Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397466
title: LPL2 - September 2023

description: Measurements of LPL2 (Caldera de Taburiente NP) in September 2023 La Palma Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397471
title: LPL3 - September 2023

description: Measurements of LPL3 (Madeira East Coast) in September 2023 Madeira Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397470
title: LPL3 - September 2023

description: Measurements of LPL3 (Madeira East Coast) in September 2023 Madeira Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397473
title: LPL4 - September 2023

description: Measurements of LPL4 (Ilha do Corvo) in September 2023 Corvo Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397472
title: LPL4 - September 2023

description: Measurements of LPL4 (Ilha do Corvo) in September 2023 Corvo Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397475
title: LPL5 - September 2023

description: Measurements of LPL5 (Tejeda) in September 2023 Gran Canaria Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397474
title: LPL5 - September 2023

description: Measurements of LPL5 (Tejeda) in September 2023 Gran Canaria Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397477
title: LPL7 - September 2023

description: Measurements of LPL7 (Archipielago Chinijo) in September 2023 Archipielago Chinijo Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397476
title: LPL7 - September 2023

description: Measurements of LPL7 (Archipielago Chinijo) in September 2023 Archipielago Chinijo Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397479
title: LPL8 - September 2023

description: Measurements of LPL8 (Timanfaya NP) in September 2023 Lanzarote Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397478
title: LPL8 - September 2023

description: Measurements of LPL8 (Timanfaya NP) in September 2023 Lanzarote Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397481
title: LPL11 - September 2023

description: Measurements of LPL11 (Frontera Rural Park) in September 2023 El Hierro Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397480
title: LPL11 - September 2023

description: Measurements of LPL11 (Frontera Rural Park) in September 2023 El Hierro Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397483
title: LPL14 - September 2023

description: Measurements of LPL14 (Ilha das Flores) in September 2023 Flores Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8397482
title: LPL14 - September 2023

description: Measurements of LPL14 (Ilha das Flores) in September 2023 Flores Light Pollution Laboratorie info
DOI: 10.48366/r642234
title: Comparison of SemTab@ISWC 2019 systems for tabular data annotation

description: The following comparison table presents the systems proposed by the SemTab community in 2019 for tabular data annotation.
DOI: 10.5281/zenodo.3974641
title: ROI analysis of abdominal aortic aneurysm

description: This data is used to compare three different positron emission tomography (PET) image reconstruction algorithms: ordered subsets expectation maximisation (OSEM), kernelised expectation maximisation (KEM) and Hybrid KEM (HKEM). There are two csv files, one contains the data from 61 patients with abdominal aortic aneurysm (AAA) and the other contains the data from 11 control subjects. There are three different regions of interest (ROIs): the aneurysm (AAA), the healthy aorta (aorta) and the vena cava (pool). these files can be read from any software such as R, excel, python etc. an example on how to read the data on R is the following: <em>s&lt;- read.csv(file = 'TotROI_list', header = F,sep =",", comment.char = "#" )<br> colnames(s) &lt;- c("Patient_ID","Algorithm","neighbours","sm","sp","sdm","sdp","Subiteration","ROI", "Mean", "SD","Max","Min", "CoV","Volume")</em> the columns are organised as follows: <strong>Patient_ID, Algorithm, neighbours, sigma_m, sigma_p, sigma_dm, sigma_dp, Subiteration, ROI, Mean value, Standard Deviation, Max value, Min value, Coefficient of Variation, Volume (number of voxels)</strong>
description: Extraction of this datasets was Funded by UK National Physical Laboratory through the National Measurement System. The National Physical Laboratory is operated by NPL Management Ltd, a wholly owned company of the UK Department for Business, Energy and Industrial Strategy (BEIS).
DOI: 10.5281/zenodo.202094
title: Predicting The Multisensory Consequences Of One'S Own Action: Bold Suppression In Auditory And Visual Cortices

description: <strong><em>Data related to the following publication:</em></strong>

Straube, B., Kemenade, B. M. van, Arikan, B. E., Fiehler, K., Leube, D. T., Harris, L. R., & Kircher, T. (2017). Predicting the Multisensory Consequences of One’s Own Action: BOLD Suppression in Auditory and Visual Cortices. <em>PLOS ONE</em>, <em>12</em>(1), e0169131. http://doi.org/10.1371/JOURNAL.PONE.0169131

Further information can be found in the data_description.docx file.
DOI: 10.5281/zenodo.3974767
title: FRB 121102 detections from the Lovell Telescope

description: We provide data for FRB 121102 pulses detected with the Lovell telescope at Jodrell Bank, UK at 1.4 GHz. These detections were used in Rajwade et al. (2020). Each file is a single pulse from FRB 121102 in the standard SIGPROC filterbank format and can be read in by standard radio pulsar data processing software. The tarball contains all the filterbank files. The text file contains the name of each file in the first column and seconds into the file where the pulse is located in the second column. The start time of the pulse is with reference to the highest frequency channel i.e seconds into the file where the pulse was detected in the highest frequency channel. Citation: If you are planning to use these data in any publication, please do not forget to put in the following acknowledgement: "The data used in this manuscript have been downloaded from the Zenodo open access repository 'FRB 121102 detections from the Lovell Telescope' (10.5281/zenodo.3974768)."
DOI: 10.5281/zenodo.3939047
title: Time series of electricity output for large grid connected photovoltaic installations in Chile

description: These data sets accompany the paper "Simulation of multi-annual time series of solar photovoltaic power: is the ERA5-land reanalysis the next big step?". They include capacity factors (values 0 to 1) in hourly temporal resolution of 103 large PV installations in Chile derived from official sources as well as simulated capacity factors using PV_LIB with ERA5-land and MERRA-2 reanalysis data. The data covers the period 2014-2018 and simulations were performed assuming either a "fixed" system with orientation towards north and inclination equal to the latitude (i.e. optimal inclination) or a horizontal single axis “tracking” system with backtracking. Furthermore, accuracy indicators (Pearson’s correlation, mean bias error and root mean square error) are provided, comparing the simulated time series with capacity factors derived from official sources. Capacity factors were calculated for the 103 installations and both alternative configurations (“fixed” and “tracking”) but only a subset of 23 installations has reference data of sufficient quality to allow for a validation (for a more detailed description, see the paper). Indicators were also calculated for all installations and configurations but should be only compared for installations inside a particular configuration: there are 14 systems classified as “tracking” and 9 as “fixed”. Further details are available in the paper and the entire Python and R code is available on github at https://github.com/inwe-boku/PV_from_era5.
description: The file "input_data.rar" includes a shape file "solares_fd0779de_0870_4194_b962_83a842d8c316.shp" containing the locations of the PV installation as well as a subdirectory "energia_abierta" where all raw time series of the installations in their original download. The MERRA-2 and ERA5-land files are not provided but the time series of the variables transformed to the units useful for the simulations for the location of each plant are provided in individual files (per variable and reanalysis source) in the file "data.rar". The file "data.rar" also includes all necessary files to run the R analysis scripts. The content of the file is described in more detail in the excel file "data_description".
DOI: 10.5281/zenodo.5199541
title: A temporally consistent 8-day 0.05° gap-free snow cover extent dataset over the Northern Hemisphere for the period 1981–2019

description: Northern Hemisphere (NH) snow cover extent (SCE) is one of the most important indicator of climate change for its unique surface property. However, short temporal coverage, coarse spatial resolution, and different snow discrimination approach among published SCE products hampers its detailed studies. Using the Advanced Very High Resolution Radiometer Surface Reflectance (AVHRR-SR) Climate Data Record (CDR) and several ancillary datasets, this study generated a temporally consistent 8-day 0.05° gap-free NH terrestrial SCE product for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite. This process consistent of five steps. First, a decision tree algorithm with multiple threshold tests was applied to detect SCE from daily AVHRR-SR CDR. Second, we merge two existing daily SCE products to take advantage of their spatial coverage. Third, an aggregation process was used to detect the maximum SCE in each 8-day periods. Forth, the GLASS SCE was generated with the help of snow cover probability climatology. Fifth, the validation process was carried out to evaluate the quality of GLASS SCE. Validation results by using 562 Global Historical Climatology Network stations during 1981–2017 (r=0.61, p&lt;0.05) and MOD10C2 during 2001–2019 (r=0.97, p&lt;0.01) proved that the GLASS SCE product is credible in snow cover frequency monitoring. Moreover, cross-comparison between GLASS SCE and surface albedo during 1982–2018 further confirmed its values in climate changes studies. The GLASS SCE data set provides binary maps of snow cover for the Northern Hemisphere from September 1981 to the December 2019. The data are organized by year and provided in GeoTIFF formats. The gridcells were flagged as “0” if classified as "Non-snow", "1" if retrieved from AVHRR satellite observations, and "2" if filled by IMS snow climatology. Spatial Coverage: N: 90, S: 0, E: 180, W: -180<br> Spatial Resolution: 0.05 deg x 0.05 deg<br> Samples = 7200<br> Lines = 1800<br> Temporal Coverage: September 1981 to December 2019<br> Temporal Resolution: 8-day
DOI: 10.5281/zenodo.4656151
title: Solar Weekly Dataset

description: The solar dataset contains approximately 6000 simulated time series representing 5-minute solar power and hourly day-ahead forecasts of photovoltaic (PV) power plants in United States in 2006. <br> The uploaded dataset contains the aggregated version of a subset of the original dataset. It contains 137 time series representing the weekly solar power production in Alabama state in 2006.
description: {"references": ["National Renewable Energy Laboratory, 2020. Solar power data for integration studies. Accessed: 2020-04-30. URL https://www.nrel.gov/grid/solar-power-data.html", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}
DOI: 10.5281/zenodo.6850188
title: Supplementary material for "Evaluating the Performance of High Spatial Resolution UAV-photogrammetry and UAV-LiDAR for Salt Marshes: the Cádiz Bay Study Case" article

description: Data presented in the study "Evaluating the Performance of High Spatial Resolution UAV-photogrammetry and UAV-LiDAR for Salt Marshes: the Cádiz Bay Study Case".
DOI: 10.5281/zenodo.293014
title: PPHPC Java vs OpenCL-CPU Datasets

description: <strong>Overview</strong> These datasets contain the results from a performance comparison between Java and OpenCL/CPU implementations of the PPHPC model. The datasets are used in the following study: Fachada, N. and Rosa, A.C., Assessing the feasibility of OpenCL CPU implementations for agent-based simulations. <em>IWOCL 2017: Proceedings of the 5th International Workshop on OpenCL</em>, Article No. 4, 2017, https://doi.org/10.1145/3078155.3078174 <strong>Experiment reproducibility</strong> The datasets were generated with the java.sh and cpu_l1.sh scripts under the following versions of the supporting software: cf4ocl v2.1.0 cl_ops v0.2.0 PPHPC, branch opencl, commit c17705882418 (release build, profiling off) OpenCL: Intel (CPU): OpenCL Runtime 16.1.1 for Intel(R) Core(TM) and Intel(R) Xeon(TM) Processors for Ubuntu (64-bit) reported driver version: 1.2.0.25) AMD (CPU): APP SDK 3.0 for 64-bit Linux (reported driver version: 1800.8) Hardware and operating system: Intel Xeon CPU E5-2650 v3 @ 2.30GHz (ten cores, two logical processors per core), 64GB RAM Ubuntu 16.04.1 LTS <strong>Licenses</strong> The datasets are made available under a CC-BY 4.0 license (see LICENSE_DATA.txt). The shell scripts and performance/statistical analysis scripts are made available under the MIT license (see LICENSE_SCRIPTS.txt).
description: {"references": ["Fachada, N. and Rosa, A.C.,\u00a0Assessing the feasibility of OpenCL CPU implementations for agent-based simulations.\u00a0IWOCL 2017: Proceedings of the 5th International Workshop on OpenCL,\u00a0Article No. 4, 2017,\u00a0https://doi.org/10.1145/3078155.3078174"]}
DOI: 10.5281/zenodo.5764032
title: PDAC_Badea

description: PDAC dataset curated by BHK Lab.
DOI: 10.5281/zenodo.4584586
title: Characteristics and sources of hourly elements in PM10 and PM2.5 during wintertime in Beijing

description: Data presented in the manuscript "Characteristics and sources of hourly elements in PM10 and PM2.5 during wintertime in Beijing" (https://doi.org/10.1016/j.envpol.2021.116865) by Rai et al. (2021).
DOI: 10.5281/zenodo.4762285
title: Example T1 data acquired on a Philips scanner

description: Example T1 data acquired on a Philips scanner
DOI: 10.5281/zenodo.581441
title: Friuli Venezia Giulia Nightlights

description: Nighttime lights in Friuli Venezia Giulia (North East Italy) available in raster format as yearly values from 1992 to 2013. Nightlights are expressed as Digital Number (DN) values, ranging from 0 (no luminosity) to 63 (maximum luminosity). Nightlights data are ready-to-use (i.e. intercalibration of original satellite values to allow for temporal comparison and average of simultaneous satellite already performed).
DOI: 10.5281/zenodo.820819
title: Landslides From Space - Volcan Landslide (10Th January 2017)

description: On 10th January 2017 a landslide hit the village Volcan. Two people died in this event and the famous Dakar Rally was disrupted.<br>
<br>
The pre-event acquisition is from 17th December 2016 (Sentinel-2) and the post-event acquisition is from 15th February 2017 (Sentinel-2).<br>
<br>
<em>Contains modified Copernicus Sentinel data (2016-2017)</em>
description: {"references": ["http://blogs.agu.org/landslideblog/2017/01/12/volcan-1/"]}
DOI: 10.5281/zenodo.3749596
title: Preprocessed UC Berkeley Home IP Web Traces

description: This is a acommulated version of the UC Berkeley Home IP Web Traces. Unlike the original dataset this records are stored in plain text format. The source dataset was provided by Steven D. Gribble and the conversion was done by Florian Wörister. <strong>Acknowledgements</strong> Steven D. Gribble contributed the traces to the ITA. He also maintains the official UC Berkeley page dedicated to this tracing effort. For inquiries, contact Steve Gribble at <em>gribble [at] gmail [dot] com</em>. <em>These traces, documentation, and associated trace tools were created by Steve Gribble with the assistance of Armando Fox, Ian Goldberg, Eric Brewer, and Cliff Frost.</em>
DOI: 10.5517/ccdc.csd.cc281f70
title: CCDC 2086741: Experimental Crystal Structure Determination

description: Related Article: Zi-Jian Li, Yu Ju, Zeya Zhang, Huangjie Lu, Yongxin Li, Ningjin Zhang, Xian-Long Du, Xiaofeng Guo, Zhi-Hui Zhang, Yuan Qian, Ming-Yang He, Jian-Qiang Wang, Jian Lin|2021|Chem.-Eur.J.|27|17586|doi:10.1002/chem.202103062
DOI: 10.5281/zenodo.3974768
title: FRB 121102 detections from the Lovell Telescope

description: We provide data for FRB 121102 pulses detected with the Lovell telescope at Jodrell Bank, UK at 1.4 GHz. These detections were used in Rajwade et al. (2020). Each file is a single pulse from FRB 121102 in the standard SIGPROC filterbank format and can be read in by standard radio pulsar data processing software. The tarball contains all the filterbank files. The text file contains the name of each file in the first column and seconds into the file where the pulse is located in the second column. The start time of the pulse is with reference to the highest frequency channel i.e seconds into the file where the pulse was detected in the highest frequency channel. Citation: If you are planning to use these data in any publication, please do not forget to put in the following acknowledgement: "The data used in this manuscript have been downloaded from the Zenodo open access repository 'FRB 121102 detections from the Lovell Telescope' (10.5281/zenodo.3974768)."
DOI: 10.5281/zenodo.1204914
title: Plantvillage Disease Classification Challenge - Color Images

description: <br>
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 United States License.<br>
<br>
# Data origins<br>
The dataset is originally hosted at PlantVillage Disease Classification Challenge.<br>
We use the modified version in this github repository to do controlled experiments.<br>
We only use the raw color images dataset and delete the unconventional characters in the classes directory name and `.csv` filenames.<br>
<br>
# Directory explanation<br>
The `80-20` direcotry has multiple `.txt` files which contain the training (~80%), validation(~10%) and testing (~10%) datasets instances filenames and the corresponding label indexes. The validation dataset quantity is `5430` in all data separation. In our experiment code (not included in this archive), the validation and testing dataset are merged together.<br>
<br>
# Data usage<br>
## Replicate our experiments<br>
We have used this dataset in writing our paper. The reference information can be seen at https://gitlab.com/huix/leaf-disease-plant-village.<br>
<br>
### Steps<br>
1. `cd` to the direcotry (e.g. `/home/usrname/plantvillage_deeplearning_paper_dataset`) that contains the `color` directory.<br>
2. run `python change_filename_prefix.py --prefix /home/usrname/plantvillage_deeplearning_paper_dataset` to modify the prefix path (which is `/home/h/plantvillage_deeplearning_paper_dataset` in our former generated datasets).<br>
3. Fin. You can use our opens ource codes repository to do the later experiments.<br>
<br>
## Generate your own training/validation/testing datasets<br>
This data separation generating code isn't included in the dataset archive, it is in our open source code. Please see our open source code repository for the detailed information.<br>
If you have any questions, you can contact the author through email.<br>
The email address is a QR code in the archive.
description: https://gitlab.com/huix/leaf-disease-plant-village
description: {"references": ["Hughes, D.P., Salathe, M.: An open access repository of images on plant health to enable the development of mobile disease diagnostics. ArXiv e-prints (2015). 1511.08060", "Mohanty, S.P., Hughes, D.P., Salath\u00e9, M.: Using deep learning for image-based plant disease detection. Frontiers in Plant Science 7, 1419 (2016). doi:10.3389/fpls.2016.01419"]}
DOI: 10.5281/zenodo.3902671
title: Beijing PM2.5 Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict PM2.5 air quality in the city of Beijing. This dataset contains 17532 time series with 9 dimensions. This includes hourly air pollutants measurments (SO2, NO2, CO and O3), temperature, pressure, dew point, rainfall and windspeed measurments from 12 nationally controlled air quality monitoring sites. The air-quality data are from the Beijing Municipal Environmental Monitoring Center. The meteorological data in each air-quality site are matched with the nearest weather station from the China Meteorological Administration. The time period is from March 1st, 2013 to February 28th, 2017. <br> <br> Please refer to https://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data for more details<br> <br> Relevant Papers <br> Zhang, S., Guo, B., Dong, A., He, J., Xu, Z. and Chen, S.X. (2017) Cautionary Tales on Air-Quality Improvement in Beijing. Proceedings of the Royal Society A, Volume 473, No. 2205, Pages 20170457 <br> <br> Citation Request <br> Zhang, S., Guo, B., Dong, A., He, J., Xu, Z. and Chen, S.X. (2017) Cautionary Tales on Air-Quality Improvement in Beijing. Proceedings of the Royal Society A, Volume 473, No. 2205, Pages 20170457