cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0957473.10fu
title: SH0957473.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0957474.10fu
title: SH0957474.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.12770/3e177f76-96b0-42e2-8007-62210767dc07
title: Données de production et d'effort de pêche (SACROIS)

description: Le projet SACROIS consiste en un travail de rapprochement, vérification, contrôles de cohérence de différents flux de données afin de produire des séries de données de production et d’effort validées, consolidées et qualifiées. Algorithme opérationnel, l'application SACROIS fournit mensuellement des estimations d'effort et de capture spatialisées et redressées, ainsi que des indicateurs de qualification issus du croisement de l'ensemble des flux disponibles. L'ensemble des données est restitué dans trois fichiers distincts par année de 2012 à 2016 :- NAVIRES-MOIS-MAREES-RECT-JOUR-IFR_.txt : Ce fichier fournit les séries de données consolidées et qualifiées d’effort de pêche et de captures générées par l’application « SACROIS », par triplet navire*marée*jour (ou navire*vente*jour pour les ventes orphelines sans marée associée).- RAPPROCHEMENT-MAREES-VENTES-IFR_.txt : Ce fichier permet de reconstituer les liens entre les marées SACROIS et les ventes OFIMER (via la fourniture par marée SACROIS de l’identifiant « navire » et des dates des ventes associées).- CATEGORIES-COMMERCIALES-IFR_.txt : Ce fichier fournit le détail des catégories commerciales, de l'état et de la présentation par capture. Cette information des catégories commerciales n’est pas directement restituée dans le fichier principal de SACROIS parce que cette information double le volume du fichier. Cette information est donc à relier aux informations disponibles dans le fichier SACROIS globale via l'identifiant de la capture (CAPT_ID).
DOI: 10.15156/bio/sh0957475.10fu
title: SH0957475.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.17632/gdt8yr9m3d.4
title: Model algorithms

description: This dataset contains files with algorithms created in MatLabR2021b. Each file contains one of two methods to calculate the response of a synthesized system with and without damping elements. One method is done through the classical matrix calculation and the other method is done with the use of edge graphs and structural numbers derived from them.
The second method is developed as a substitute to matrix calculation with hopes of developing a more straightforward method with less computational overload.
DOI: 10.18462/iif.nitec48.06.2022
title: Low-GWP Refrigerants: Status and Outlook
DOI: 10.21227/ba0m-cy61
title: SensorNetGuard: A Dataset for Identifying Malicious Sensor Nodes

description: The dataset, titled "SensorNetGuard: A Dataset for Identifying Malicious Sensor Nodes," comprises 10,000 samples with 21 features. It is designed to facilitate the identification of malicious sensor nodes in a network environment, specifically focusing on IoT-based sensor networks.General Metrics§  Node ID: The unique identifier for each node.§  Timestamp: The time at which data or a packet is sent or received.§  IP Address: Internet Protocol address of the node.Network Traffic Metrics§  Packet Rate: The number of packets sent/received per unit time.§  Packet Drop Rate: The rate at which packets are being dropped.§  Packet Duplication Rate: The rate at which packets are duplicated.§  Data Throughput: The amount of data successfully transferred from one point to another in a given time frame.Signal Metrics§  Signal Strength: Measured in dBm, indicates the power level of the signal.§  Signal-to-Noise Ratio (SNR): Measures the clarity of the signal.Power Usage Metrics§  Battery Level: Remaining power in the node.§  Energy Consumption Rate: The rate at which the node consumes energy.Routing Metrics§  Number of Neighbors: The count of nodes within direct communication range.§  Route Request Frequency: The frequency with which a node is asking for routes.§  Route Reply Frequency: The frequency with which a node responds to route requests.Behavioral Metrics§  Data Transmission Frequency: How often a node sends data.§  Data Reception Frequency: How often a node receives data.§  Error Rate: The number of erroneous packets per unit time.Miscellaneous Metrics§  CPU Usage: The percentage of CPU being used by the node.§  Memory Usage: The amount of RAM being used.§  Bandwidth: Available data transmission rate.Metrics Specific to Attacks§  Is_Malicious: A binary flag (0 or 1) indicating whether the node is malicious.The dataset includes a diverse range of features that allow for the application of machine learning models to identify various types of attacks, such as black hole, gray hole, flooding attacks, and Sybil attacks. Some features may be more relevant for specific types of attacks, so feature engineering and selection are crucial steps. Various machine learning algorithms can be trained on this dataset. The "Is_Malicious" column serves as the ground truth for learning algorithm. For unsupervised learning, anomaly detection algorithms can be used to identify outliers or anomalies that may correspond to malicious activities.Being able to accurately identify malicious nodes can significantly improve the security posture of sensor networks, particularly in IoT environments where such attacks can have severe consequences.By training a model on this dataset, network administrators can identify potential security threats in real-time, thus enabling proactive measures to isolate or remove malicious nodes.Ultimately, the dataset serves as a comprehensive resource for developing and validating machine learning models aimed at enhancing network security by identifying malicious sensor nodes.
DOI: 10.5258/soton/d2543
title: Dataset in support of the journal paper 'Experimental investigation of BDFA-based O-band direct-detection transmission using an optical recirculating loop'

description: This dataset contains: Data supporting the following figures: Fig. 1. Insets: the optical spectra after the DD-MZM in the SSB-QPSK case (left), and the gain and NF performance of BDFA2 at an input power of -6 dBm (right). Fig. 2. (a) BER versus transmission distance for 50-Gb/s Nyquist PAM4 transmission over the BDFA-based optical recirculating loop, and (b) BER versus wavelength for the 50-Gb/s Nyquist PAM4 transmission after a 250-km length of SMF. Fig. 3. Achievable capacity versus wavelength for the adaptively-loaded DMT transmission over the O-band BDFA-based optical recirculating loop. Fig. 4. SNR profiles and the corresponding allocated bits for the 1340-nm transmission in the B2B and 200-km cases. Fig. 5. (a) BER versus transmission distance for 50-Gb/s SSB-QPSK transmission over the BDFA-based optical recirculating loop, and (b) BER versus wavelength for the 50-Gb/s SSB-QPSK transmission after 400-km and 550-km lengths of SMF. Fig. 6. The optical spectra of the 50-Gb/s/ SSB-QPSK WDM signals at the transmitter in the CWDM case (upper), and DWDM case (lower). Fig. 7. BER versus transmission distance for the 3×50-Gb/s Nyquist OOK CWDM transmission. Fig. 9. BER versus transmission distance for the 3×50-Gb/s SSB-QPSK CWDM transmission. Fig. 10. BER versus transmission distance for the 3×50-Gb/s DWDM transmission.
DOI: 10.5281/zenodo.10000371
title: Thin film structural color is widespread in slime molds (Myxomycetes, Amoebozoa)

description: Dataset supporting the article "Bauernfeind, Viola, et al. "Thin film structural color is widespread in slime molds (Myxomycetes, Amoebozoa)." Optics Express 32.4 (2024): 5429-5443."
DOI: 10.5281/zenodo.1302867
title: Nonhuman Primate Reaching With Multichannel Sensorimotor Cortex Electrophysiology: Broadband For Indy_20161207_02

description: This dataset supplements https://doi.org/10.5281/zenodo.583331 .<br>
<br>
<strong>General description.</strong> These data consist of extracellular neural recordings ("broadband") from primate subject "Indy", session identifier "indy_20161207_02".

<strong>Filtering. </strong>The data are sampled at 24414.0625 Hz and are unfiltered, except for an anti-aliasing filter built-in to the recording amplifier: a 4th order low-pass with a roll-off of 24 dB per octave at 7.5 kHz, operating at the sampling rate.

<strong>File format.</strong> The data are contained in an HDF5 formatted file, organized according to the Neurodata Without Borders (NWB) version 1.0.6 specification.

<strong>Datasets. </strong>A few of the relevant dataset paths are listed here for convenience. In the below, <em>n</em> refers to the number of recording channels and <em>k</em> refers to the number of samples.


	"/acquisition/timeseries/broadband/data" - k x n
	
		The broadband neural recordings.
	
	
	"/acquisition/timeseries/broadband/data/conversion" (scalar attribute)
	
		When multiplied by each sample converts the data into units of volts.
	
	
	"/acquisition/timeseries/broadband/timestamps" - k x 1
	
		Timestamps for each sample, seconds.
	
	
	"/general/extracellular_ephys/electrode_map" - n x 3
	
		The relative coordinates of each electrode contact (x, y, z), meters.
	
	


Please refer to the master dataset for further information.
description: This research was supported by the Congressionally Directed Medical Research Program (W81XWH-14-1-0510). JEO was supported by fellowship #2978 from the Paralyzed Veterans of America. JGM was supported by a fellowship from the Swartz Foundation.
DOI: 10.5281/zenodo.3572401
title: Tumor growth kinetics of subcutaneously implanted Lewis Lung carcinoma cells

description: <strong>Please cite</strong><br> Benzekry, S., Lamont, C., Beheshti, A., Tracz, A., Ebos, J. M. L., Hlatky, L., & Hahnfeldt, P. (2014). Classical mathematical models for description and prediction of experimental tumor growth. <em>PLoS Computational Biology</em>, <em>10</em>(8), e1003800. http://doi.org/10.1371/journal.pcbi.1003800 <strong>Cell culture</strong><br> Murine Lewis lung carcinoma (LLC) cells, originally derived from a spontaneous tumor in a C57BL/6 mouse [1], were obtained from American Type Culture Collection (Manassas, VA). <strong>Tumor injections</strong><br> For the subcutaneous mouse syngeneic lung tumor model, C57BL/6 male mice with an average lifespan of 878 days were used [2]. At time of injection mice were 6 to 8 weeks old (Jackson Laboratory, Bar Harbor, Maine). Subcutaneous injections of 10<sup>6</sup> LLC cells in 0.2 ml phosphate-buffered saline (PBS) were performed on the caudal half of the back in anesthetized mice. <strong>Tumor measurements</strong><br> Tumor size was measured regularly with calipers to a maximum of 1.5 cm<sup>3</sup> for the lung data set. Largest (L) and smallest (w) diameters were measured subcutaneously using calipers and the formula V = \(\frac{\pi}{6}w^2 L\) was then used to compute the volume (ellipsoid). Volumes ranged 14–1492 mm<sup>3</sup> over time spans from 4 to 22 days for the lung tumor model (two experiments of 10 animals each). [1] Bertram JS, Janik P (1980) Establishment of a cloned line of Lewis Lung Carcinoma cells adapted to cell culture. Cancer Lett 11: 63–73. Available: http://www.ncbi.nlm.nih.gov/pubmed/7226139. Accessed 9 July 2013. [2] Kunstyr I, Leuenberger HG (1975) Gerontological data of C57BL/6J mice. I. Sex differences in survival curves. J Gerontol 30: 157–162. Available: http:// www.ncbi.nlm.nih.gov/pubmed/1123533. Accessed 9 July 2013.
description: {"references": ["Benzekry, S., Lamont, C., Beheshti, A., Tracz, A., Ebos, J. M. L., Hlatky, L., & Hahnfeldt, P. (2014). Classical mathematical models for description and prediction of experimental tumor growth. PLoS Computational Biology, 10(8), e1003800.", "Vaghi, C., Rodallec, A., Fanciullino, R., Ciccolini, J., Mochel, J. P., Mastri, M., Poignar, C., Ebos, J. ML & Benzekry, S. (2020). Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors. PLoS Computational Biology, 16(2), e1007178."]}
DOI: 10.5281/zenodo.7225126
title: 3D sympathetic cooling and detection of levitated nanoparticles

description: Dataset for the paper 3D sympathetic cooling and detection of levitated nanoparticles
DOI: 10.6084/m9.figshare.25422805
title: Supplementary Material for: Proactive diagnosis and tailor-made treatment of patent ductus arteriosus in very preterm infants with routine echocardiography in Japan: a post-hoc analysis of the PLASE study

description: Introduction
A feature of the management of extremely preterm infants in Japan is proactive circulatory management using early routine echocardiography performed by neonatologists.
Methods
This study was a post-hoc analysis of the Patent ductus arteriosus and Left Atrial Size Evaluation in preterm infants (PLASE) study, which is a prospective cohort study including preterm infants admitted to 34 tertiary neonatal intensive care units in Japan between October 2015 and December 2016. We described the details of the treatment strategy of patent ductus arteriosus (PDA) based on early routine echocardiography.
Results
In total 613 preterm infants were included into the analysis. Twenty percent of infants with prophylactic indomethacin were switched to therapeutic cyclooxygenase inhibitor (COX-I) before the completion of the full prophylactic indomethacin course. Therapeutic COX-I was mostly administered based on echocardiographic findings before PDA became symptomatic or hemodynamically significant. Therapeutic COX-I was frequently discontinued after one or two doses before the full course (three doses) was completed. The proportion of infants requiring additional treatment (additional therapeutic COX-I course or surgical PDA closure) after discontinued COX-I courses (&lt; 3 doses) compared to infants after completed 3 doses course was significantly lower (after the first therapeutic COX-I course 46% vs. 68%, p &lt; 0.001) or without a significant difference (after the second or third course).
Conclusions
The clinical management of PDA in Japan featured: 1) COX-I administration based on echocardiographic findings before symptomatic or hemodynamically significant PDA appeared, and 2) frequent discontinuation of therapeutic COX-I before completing the standard three doses course.
DOI: 10.6084/m9.figshare.26520046
title: Supplementary Material for: Galectin-3 and soluble CD146 identify cardiorenal injuries in severe burn patients: a biomarker-based approach.

description: Introduction: Acute kidney injury (AKI) and myocardial injury (MI) are severe conditions in patients with severe burn injury, and combination of both is even worst and is called the cardiorenal syndrome (CRS). Identifying a distinct cardiorenal phenotype could significantly enhance the management of these patients. Galectin-3 (Gal3) and soluble CD146 (sCD146) are biomarkers for renal and cardiac injuries. This study aims to assess the occurrence and reliability of these biomarkers in recognizing CRS in individuals who have been severely burn.
Methods: This study is a single-center prospective proof of concept study involving patients with severe burn injuries. Plasma samples for Gal3 and sCD146 measurements were collected daily during the initial 7 days following admission. CRS was defined after 24h of admission by the association of AKI stage 1 or more (KDIGO definition) and MI defined on high sensitive troponine (hsTnT) (variation &gt; 20 % baseline value or absolute value &gt; 40 ng/mL).
Results: Forty patients met the inclusion criteria and were included in this study. Thirty-eight patients had CRS. The pooled values of Gal3, or combination of Gal3 and sCD146 values following 7 days after admission were associated with CRS with an OR of 1.145 [CI 95% (1.081-1.211)], p &lt; 0.001, and 1.147 [CI 95% (1.085-1.212)], p &lt; 0.001, respectively. Gal3 values at admission (D0) had a predictive performance for CRS with an AUC of 0.78 [CI 95% (0.63-0.93)], and this performance improved when using the combination of Gal3 and sCD146 values at admission (D0), with an AUC of 0.81 [CI 95% (0.66-0.96)]. Gal3 levels during the first 7 days were associated with patients experiencing AKI and no MI, with an Odds Ratio (OR) of 1.129 [CI 95% (1.065-1.195)], p &lt; 0.001 and MI without AKI with an OR of 1.095 [CI 95% (1.037-1.167)], p &lt; 0.001. sCD146 alone was not associated with AKI without MI or MI without AKI and was poorly associated with CRS.
Conclusion: In severely burned patients, cardiorenal syndrome is a frequent and severe condition. Gal3 values during the first 7 days following admission were associated with cardiorenal syndrome. The use of sCD146 with Gal3 improved prediction performance for CRS identification. The use of such biomarkers to identify cardiorenal syndrome is important and needs to be confirmed in other studies.
DOI: 10.6103/share.w9ca.900
title: Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 9. COVID-19 Survey 2
DOI: 10.15156/bio/sh0957476.10fu
title: SH0957476.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.1594/pangaea.904967
title: Summary of sizes, weights, counts and coverage of poly-metallic nodules from box cores taken during SONNE cruises SO268/1 and SO268/2

description: Aggregation of single nodule measurements (see https://doi.pangaea.de/10.1594/PANGAEA.904962) to values describing the entire box corer:- Nodules [#]: Total number of nodules sampled in the box corer (N). Includes surface nodules (N_s) and those from deeper layers (N_d)- Nodules [#](buried): Number of nodules from deeper layers (N_d) in the box corer that were not visible on the sea floor surface- Nodules size [cm^2]: The area A_i [cm^2] of an individual nodule n_i was computed from its two main axes (a_i [mm] & b_i [mm]) and the ellipsoidal formula (A_i = pi * a_i/2 * b_i/2 * 0.01). The value given here represents the median value of all nodule sizes in this box corer (Nodules size [cm^2] = MEDIAN(A_i,i=1..N)- Nodules m [kg]: Sum of the weights w_i [g] of all nodules in the box corer (surface and deeper layers; Nodules m [kg] = SUM(n_i,i=1..N)) * 0.001. To quantify nodule abundance per square meter, multiply this value by 4!- Nodules [%]: The sum of all nodule sizes A_i [cm^2] (i=1..N_s) visible at the seafloor, divided by the area of the box corer (50x50 cm^2): Nodules [%] = SUM(A_i,i=1..N_s) / 2500
DOI: 10.17632/dgrrxg2ckx.1
title: Cosmogenic dating and OSL dating results of Kumysh Fault, eastern Tian Shan

description: These data are the dating results of samples collected around the Kumysh Fault, eastern Tian Shan, China
DOI: 10.17632/pydnfmkfdk.3
title: Piezoelectric electromechanical model (first iteration)

description: This dataset contains an algorithm created in MatLabR2021b to anaylse the response of a synthesized piezoelectric model. The algorithm is based on constitutive equations of piezoelectricity combined with Van Dyke - Butterworth model.
DOI: 10.18434/mds2-3186
title: Code and data to run the reference case simulation for Walsh et al "Pulse interaction induced systematic errors in dual comb spectroscopy"
DOI: 10.23728/b2share.bb37150f3f1d4e6c89a94527881ad2f2
title: Intra-oscillator high harmonic source reaching 100-eV photon energy - Dataset

description: Raw acquired data
DOI: 10.24403/jp.1016278
title: Einige bewertungstheoretische Anmerkungen zur Marktwertanalyse der Plattform transfermarkt.de / Phil Ackermann & Florian Follert
DOI: 10.26093/cds/vizier.22090022
title: SSDF survey: IRAC catalogs

description: VizieR online Data Catalogue associated with article published in journal Astronomical Journal (AAS) with title 'The Spitzer South Pole Telescope Deep Field: survey design and Infrared Array Camera catalogs.' (bibcode: 2013ApJS..209...22A)
DOI: 10.26093/cds/vizier.22370039
title: Spitzer survey of UltraVISTA deep Stripes (SMUVS)

description: VizieR online Data Catalogue associated with article published in journal Astronomical Journal (AAS) with title 'Spitzer Matching survey of the UltraVISTA ultra-deep Stripes (SMUVS): full-mission IRAC mosaics and catalogs.' (bibcode: 2018ApJS..237...39A)