cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/g86wh6k6dh.1
title: PACKMAN – A portable instrument to investigate space weather

description: PACKMAN (PArticle Counter k-index Magnetic ANomaly) is an autonomous, light and robust and space weather instrument for operation within the subsurface, surface and atmosphere (as payload in stratospheric balloons) of the Earth. It has been designed using to Commercial Off The Shelf (COTS) components to reduce the cost of each unit and to allow to have multiple units monitoring simultaneously at different sites and also incorporate an open-access approach. The hardware-core of each PACKMAN units, weights around 600 g, including electronics and a plastic designed mechanical support structure. The total weight of the PACKMAN, including a 3D printed enclosure and batteries, is 1kg with a maximum dimension of 30x30x20cm. The enclosure is packed with polystyrene foam to maintain a stable temperature of the electronic components as the temperature plunges to negative 50°C at high altitudes. PACKMAN consumes about 500 mA of current at 12V and can be powered by lithium batteries which are light and have a high-power density. The balloon version of PACKMAN has a camera to record the different phases of the launch and flight. PACKMAN has been deployed at multiple latitudes and altitudes ranging from stratospheric heights (corroborating its TRL8 maturity) to subsurface depths of around 1km. The data from PACKMAN have been compared with the state-of-the-art ground-based observatories, and satellites and scientific observations have been documented. A 3-D network of PACKMAN units operating continuously around the globe, from the subsurface to the stratosphere, would help to improve the understanding of the space weather phenomena, and its implications on the climate and infrastructures. With its straightforward design, PACKMAN is also an excellent tool for education and outreach. This article outlines the building instructions of two types of PACKMAN units: PACKMAN-S for ground-based measurements and PACKMAN-B for stratospheric measurements aboard high-altitude balloons. It also provides guidelines on the data format, to encourage multiple users to build their own PACKMAN instrument and allow sharing a unique formatted data for comparison and thus enable a citizen science approach.
DOI: 10.17632/43sbvvh7g4.1
title: Thematic analysis result （LDA results and community network results）in construction and demolition waste management

description: The anlaysis result and node data for research. The LDA analysis result provide the topic information of research and used as node data in the lateral thematic model. The edge data is provide by word2vec model. Combining with edge data and node data, the social network can be conducted and visualized.
DOI: 10.17632/m7r93c3tsh.1
title: Dataset for plasmids construction

description: The dataset shows the method for the construction of plasmids that used for pinene production.
DOI: 10.17632/9hmmvjm5vg.1
title: nutrition support of VLBWIs with EUGR in NICU

description: nutrition support of VLBWIs with EUGR in NICU
DOI: 10.17632/x3z4zx8vwr.1
title: Data from 9,549 financial and audit reports of Serbian business entities from the periods 2016-2018

description: The data from 9,549 complete sets of annual financial statements are combined with the data from the corresponding audit reports, forming an unbalanced panel data set. The client companies included in the sample represent a supermajority of medium and large-sized companies registered in the Republic of Serbia. Information on the name of the auditing firm, the type of auditor, the date of audit, and the type of audit opinion is hand-collected from the audit reports issued by 77 audit firms (the Big 4 plus 73 other auditing firms), which, again, represents a supermajority of all the auditing firms registered in this country. In the total sample of audit opinions (6,343), the following frequencies of the four main types of audit opinions are observed: an adverse opinion (50), a disclaimer of opinion (344), a qualified opinion (1,278), and an unqualified opinion (4,671). Additionally, most common financial indicators are calculated based on the collected financial statements. Feel free to use it for research purposes or to reproduce the results presented in the article. For a detailed description of the variables and their descriptive statistics, please read the article: Empirical Data on Financial and Audit Reports of Serbian Business Entities. Proceedings of the 7th International Scientific Conference - FINIZ 2020, 193–198. https://doi.org/10.15308/finiz-2020-193-198 When referring to the data set in publications, please cite the article. These data are used in a research study and may not be redistributed or used for commercial purposes. If you have any questions, please feel free to contact me at nstanisic@singidunum.ac.rs
DOI: 10.17632/hg38nn6c45.1
title: Latvian Used-car Market Announcements Monitoring in 2020

description: Used-car market monitoring was started in 2018, 03 January, when Federal Administrative Court in Leipzig (Germany) allowed cities and communes in Germany to impose bans for diesel cars in order to reduce the level of nitric oxide in the air. This data-set represent third year of market monitoring, 2020. First year monitoring (2018) data-set is available together with data article: Skribans, V. Used-car market dataset for Latvia 2018, (2019) Data in Brief, 22, pp. 859-862. doi: 10.1016/j.dib.2018.12.075. Methodology of data mining is available in research paper: SKRIBANS, V. and HEGERTY, S.W., 2019. Data mining application for used-car market analysis, IMCIC 2019 - 10th International Multi-Conference on Complexity, Informatics and Cybernetics, Proceedings 2019, pp. 35-40. Second year monitoring (2019) data-set is available there: Skribans, Valerijs (2019), “Latvian Used-car Market Announcements Monitoring in 2019”, Mendeley Data, V1, doi: 10.17632/kyf948x63n.1
DOI: 10.17632/8f5db6m83f.1
title: Effects of Hf additions on the microstructures and mechanical properties of Ni-rich Ni55Ti45 alloys

description: Effects of Hf additions on the microstructures and mechanical properties of Ni-rich Ni55Ti45 alloys
DOI: 10.21258/1540125
title: Inventaire forestier national suisse - Tableau no 897056
DOI: 10.21258/1615981
title: Inventario forestale nazionale svizzero - Tabella n. 891904
DOI: 10.21258/1547907
title: Inventaire forestier national suisse - Tableau no 898098
DOI: 10.17632/fm86gnkw6x.1
title: Salary, offensive, and defensive stats of 2604 NBA players over 11 seasons (2005-2016)

description: Dataset of 2604 National Basketball Association (NBA) players who played more than 1000 minutes in the regular seasons from the season 2005-2006 until 2015-2016. The dataset covered several stats related to salary, offensive, and defensive activities such as salary, minutes played, assists, field goal, free throws, offensive rebounds, blocks, steals, and defensive rebounds.
DOI: 10.17632/rz57w938fs.1
title: Data for: Geometry of bilayer nanoscroll rolled from zigzag nanoribbons of graphene and boron nitride

description: The file "xyz.mcd" provides MathCAD code (v. 5.0+, 10/01/1994) for reproduction of Cartesian coordinates of bilayer nanoscroll made of graphene and boron nitride zigzag nanoribbons shown in Figure 3 of the article. See screenshot "xyz.png" for the results of the code execution.
DOI: 10.17632/532b4srrp7.1
title: Dr. Mete Emin Atmaca_Sharpe_treynor Article dataset

description: There are two files I would like to share with researchers. first one is my dataset and the second one is results of calculations and additional graphics
DOI: 10.17632/6nf7b7ffb7.1
title: TopBP1 assembles nuclear condensates to switch on ATR signalling. Frattini et al.

description: Raw data - video - MS results
DOI: 10.21258/1570924
title: Inventaire forestier national suisse - Tableau no 899494
DOI: 10.17632/45tbyx9r3n.1
title: The list of rotifer and micro-crustacean taxa identified in the inland waters of the regions of Russian Arctic

description: The list of taxa from planktonic and benthic samples collected from inland continental waterbodies in seven regions of the Russian Arctic is provided in Table 1. The table contains a complete list of the lower-rank taxa of micro-invertebrates (Rotifera, Cladocera and Copepoda) for the regions, which summarizes results of our own and literature data on the freshwater fauna of the chiefly lentic waterbodies, based on modern taxonomy of the groups under study. Each column of Table 1 corresponds to one region: the Kola Peninsula (Region I), the Pechora River Delta (Region II), the Bolshezemelskaya tundra (Region III), the Polar Ural (Region IV), the Putorana Plateau (Region V), the Lena River Delta (Region VI), the Indigirka River Basin (Region VII). If the only source of information on species presence in the study region was literature data, we referred to the source in Table 1. The detailed analysis of the list of taxa is given in [1], where it is similar to Dataset 2: Zooplankton and Meiofauna across Arctic Regions of Russia. The list of species of zooplankton and meiofauna contains our own findings and literature data chiefly for the period from the 1960-s to the 2010-s (1960-2017). Our own findings were obtained by analyzing samples of zooplankton, meiobenthos, and two cores of bottom sediments (from Regions I, Lake Antyuh-Lambina, and III, Lake Kharbey) that we collected in summer time. Primary data sources for some points: [2-8].
DOI: 10.17632/y4kmnb3tvx.1
title: E.denticulatum quant BUP

description: MaxQuant output files (txt folder) from quantitative bottom-up proteomics analysis of two pilot-scale hot-water extracts from E. denticulatum, described in the manuscript "Proteomic characterization of pilot scale hot-water extracts from the industrial carrageenan red seaweed Eucheuma denticulatum" Extract A is in the data found under the folder/name "A-013-05e" and Extract B under the folder/name ""A-022-05e". Raw MS data are available upon request, but size limitations makes submission to the repository impossible.
DOI: 10.21258/1559436
title: Inventaire forestier national suisse - Tableau no 898033
DOI: 10.21258/1577097
title: Inventario forestale nazionale svizzero - Tabella n. 902098
DOI: 10.5519/qd.0lxnaj98
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (8 records)
DOI: 10.21227/hf6q-kv55
title: A UAV aerial photograpy vehicle data set

description: This paper produces a data set containing 1127 images, using VOC12 format, the size of the image is 3840*2160, and the corresponding relation of file names
DOI: 10.21258/1546798
title: Inventaire forestier national suisse - Tableau no 902291
DOI: 10.21227/arkh-0r60
title: A UAV aerial photograpy vehicle data set

description: This paper produces a data set containing 1127 images, using VOC12 format, the size of the image is 3840*2160, and the corresponding relation of file names
DOI: 10.21258/1609014
title: Inventario forestale nazionale svizzero - Tabella n. 897405
DOI: 10.21258/1562626
title: Inventaire forestier national suisse - Tableau no 899427
DOI: 10.21258/1537051
title: Inventaire forestier national suisse - Tableau no 902064
DOI: 10.5281/zenodo.4322353
title: Equipment Sensor Data from Semiconductor Frontend Production

description: This data set was generated in accordance with the semiconductor industry and contains sensor recordings from high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. Typically, bunches of wafers are aggregated into so-called lots of size 25, which always pass through the same operations in the production chain. In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data recorded during each process step. To keep the entire production as stable as possible, the sensor data is used in order to intervene in case of deviations. After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given sensor data is provided. The aim is to find correlations between the wafer test data and the sensor data in order to identify the root cause. The given data is divided into three data sets: "equipment1.csv", "equipment2.csv" and "response.csv". "equipment1.csv" and "equipment2.csv" represent the sensor data for two process equipment. The "response.csv" data set contains the corresponding wafer test data. For the unique identification, the first two columns in each data set are the lot number and the wafer number respectively. It must be mentioned that the number of wafers contained can vary within but also between the equipment. The exact column structure is given as follows: for "equipment1.csv" and "equipment2.csv": lot: the lot number wafer: the wafer number timestamp: the timestamp of the respective sensor recordings (176 timestamps per wafer - represented as approximately every second one recording for the sensors) sensor_1: the recordings of the first sensor sensor_2: the recordings of the second sensor ... sensor_56: the recordings of the last sensor "sensor_1"-"sensor_24" belongs to "equipment1" and "sensor_25"-"sensor_56" belongs to "equipment2". for "response.csv": lot: the lot number wafer: the wafer number response: the numerical test values class: the "good"/"bad" classification depending on the response value (threshold: 0,75)
DOI: 10.5281/zenodo.4282610
title: Equipment Sensor Data from Semiconductor Frontend Production

description: This data set was generated in accordance with the semiconductor industry and contains sensor recordings from high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. Typically, bunches of wafers are aggregated into so-called lots of size 25, which always pass through the same operations in the production chain. In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data recorded during each process step. To keep the entire production as stable as possible, the sensor data is used in order to intervene in case of deviations. After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given sensor data is provided. The aim is to find correlations between the wafer test data and the sensor data in order to identify the root cause. The given data is divided into three data sets: "equipment1.csv", "equipment2.csv" and "response.csv". "equipment1.csv" and "equipment2.csv" represent the sensor data for two process equipment. The "response.csv" data set contains the corresponding wafer test data. For the unique identification, the first two columns in each data set are the lot number and the wafer number respectively. It must be mentioned that the number of wafers contained can vary within but also between the equipment. The exact column structure is given as follows: for "equipment1.csv" and "equipment2.csv": lot: the lot number wafer: the wafer number timestamp: the timestamp of the respective sensor recordings (176 timestamps per wafer - represented as approximately every second one recording for the sensors) sensor_1: the recordings of the first sensor sensor_2: the recordings of the second sensor ... sensor_56: the recordings of the last sensor "sensor_1"-"sensor_24" belongs to "equipment1" and "sensor_25"-"sensor_56" belongs to "equipment2". for "response.csv": lot: the lot number wafer: the wafer number response: the numerical test values class: the "good"/"bad" classification depending on the response value (threshold: 0,75)
DOI: 10.21258/1561742
title: Inventaire forestier national suisse - Tableau no 899065
DOI: 10.21258/1625525
title: Inventario forestale nazionale svizzero - Tabella n. 899494
DOI: 10.21258/1611977
title: Inventario forestale nazionale svizzero - Tabella n. 891710
DOI: 10.5281/zenodo.4322342
title: Prediction of Churning Credit Card Customers

description: A business manager of a consumer credit card portfolio is facing the problem of customer attrition. They want to analyze the data to find out the reason behind this and leverage the same to predict customers who are likely to drop off. We could construct a model to predict which customer might be churned and the manager could proactively provide them better services and turn customers' decisions in the opposite direction.
description: {"references": ["Goyal, S. (2020, November 19). Credit Card customers. Retrieved December 15, 2020, from https://www.kaggle.com/sakshigoyal7/credit-card-customers?select=BankChurners.csv"]}
DOI: 10.5281/zenodo.4322341
title: Prediction of Churning Credit Card Customers

description: A business manager of a consumer credit card portfolio is facing the problem of customer attrition. They want to analyze the data to find out the reason behind this and leverage the same to predict customers who are likely to drop off. We could construct a model to predict which customer might be churned and the manager could proactively provide them better services and turn customers' decisions in the opposite direction.
description: {"references": ["Goyal, S. (2020, November 19). Credit Card customers. Retrieved December 15, 2020, from https://www.kaggle.com/sakshigoyal7/credit-card-customers?select=BankChurners.csv"]}
DOI: 10.21258/1546669
title: Inventaire forestier national suisse - Tableau no 902278
DOI: 10.5281/zenodo.4317556
title: Supplementary data for 'Comparison of sparse biclustering algorithms for gene expression datasets'

description: <strong>Motivation:</strong> Gene clustering and sample clustering are commonly used to find patterns in gene expression datasets. However, in heterogeneous samples (e.g. different tissues or disease states), genes may cluster differently. Biclustering algorithms aim to solve this issue by performing sample clustering and gene clustering simultaneously. Existing reviews of biclustering algorithms have yet to include a number of more recent algorithms and have based comparisons on simplistic simulated datasets without specific evaluation of biclusters in real datasets, using less robust metrics. <br> <strong>Results:</strong> In this study we compared four classes of sparse biclustering algorithms on a range of simulated and real datasets. In particular we use a knockout mouse RNA-seq dataset to evaluate each algorithm’s ability to simultaneously cluster genes and cluster samples. We found that Bayesian algorithms with strict sparsity constraints had high accuracy on the simulated datasets and didn't require any post-processing, but were considerably slower than other algorithm classes. We assessed whether non-negative matrix factorisation algorithms can be repurposed for biclustering and found that, although the raw output was poor, after using a sparsity-inducing post-processing procedure we introduce, one such algorithm was one of the most highly ranked on real datasets. We also exhibit the limitations of biclustering algorithms by varying the complexity of simulated datasets. The algorithms generally struggled on simulated datasets with a large number of implanted factors, or with a large number of genes. In real datasets, the algorithms rarely returned clusters containing samples from multiple tissues, which highlights the need for further thought in the design and analysis of multi-tissue studies to avoid differences between tissues dominating the analysis. <br> Code to run the analysis is available at https://github.com/nichollskc/biclust_comp, including wrappers for each algorithm, implementations of evaluation metrics, and code to simulate datasets and perform pre- and post-processing.
DOI: 10.21258/1532469
title: Inventaire forestier national suisse - Tableau no 899097
DOI: 10.21258/1570603
title: Inventaire forestier national suisse - Tableau no 892906
DOI: 10.5519/qd.cdlfqca7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2 records)
DOI: 10.21258/1565061
title: Inventaire forestier national suisse - Tableau no 897240
DOI: 10.21258/1560691
title: Inventaire forestier national suisse - Tableau no 899164
DOI: 10.21258/1540258
title: Inventaire forestier national suisse - Tableau no 898141
DOI: 10.21258/1576524
title: Inventario forestale nazionale svizzero - Tabella n. 897597
DOI: 10.21258/1538375
title: Inventaire forestier national suisse - Tableau no 897750
DOI: 10.21258/1578669
title: Inventario forestale nazionale svizzero - Tabella n. 899376
DOI: 10.21258/1608379
title: Inventario forestale nazionale svizzero - Tabella n. 898326
DOI: 10.17632/cp6pwz5cfs.7
title: RDM - File Type Support 25,26/10Nov,Dec 2020

description: RDM - File Type Support 21May2019 ElsCustomer 26Nov2020 Apart from .u3d all files previeww [ .obj / .ply / .vtk / .stl / .ent / .brk / .pdb / .pse / .mol / .mol2 / .cif / .u3d / .dcm / .nii] - .pse is not supported
DOI: 10.21258/1557581
title: Inventaire forestier national suisse - Tableau no 891898
DOI: 10.21258/1577281
title: Inventario forestale nazionale svizzero - Tabella n. 897817
DOI: 10.21258/1550833
title: Inventaire forestier national suisse - Tableau no 891750
DOI: 10.21258/1567596
title: Inventaire forestier national suisse - Tableau no 897116
DOI: 10.21258/1568026
title: Inventaire forestier national suisse - Tableau no 892016
DOI: 10.21227/pcf7-m292
title: Sintel 4D Light Field Video dataset

description: A medium-scale synthetic 4D Light Field video dataset for depth (disparity) estimation. From the open-source movie Sintel.Click here for image samples. (Our project page)===Light Field video:23 synthetic scenes1,204x436 pixels9x9 views (Currently, only the crosshair viewpoints are provided.)20--50 frames Ground-truth disparity values:Provide the values of the central view.The disparity value was obtained by transforming the depth value obtained in Blender.Light Field setup:Rendering with a “clean” pass.The Light Field captured by moving the camera with a baseline of 0.01[m] towards a common focal plane while keeping the optical axes parallel.