cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/da5d873d6698b89799ee84d780bda074
title: Biocomplexity at North Temperate Lakes LTER: Coordinated Field Studies: Riparian Littoral Sites 2001 - 2004

description: General descriptive data for sites sampled as part of the "cross-lake comparison" segment of the Biocomplexity Project (Landscape Context - Coordinated Field Studies). The goal of the study was to explore the links between terrestrial and aquatic systems across a gradient of residential development and lake landscape position. Specifically, this project attempted to relate the abundance of Coarse Wood in the littoral zone with abiotic, biotic and anthropogenic features of the adjacent shoreline. Sampling Frequency: each site sampled once Number of sites: 488 sites on 61 Vilas County lakes were sampled from 2001-2004 (approximately 15 different lakes each year; eight sites per lake).
DOI: 10.5281/zenodo.4426306
title: Mixed Layer Depth Anomaly

description: time is in days since 0
DOI: 10.5281/zenodo.4422095
title: Xanthos - Output Data

description: This dataset includes output data for 20 combinations of GCM/RCP scenarios for the Xanthos model. The GCM and RCP selected for the study of Argentina Energy-Water-Land Systems are MIROC-ESM-CHEM and RCP 6.0.
DOI: 10.5281/zenodo.4422094
title: Xanthos - Output Data

description: This dataset includes output data for 20 combinations of GCM/RCP scenarios for the Xanthos model. The GCM and RCP selected for the study of Argentina Energy-Water-Land Systems are MIROC-ESM-CHEM and RCP 6.0.
DOI: 10.5281/zenodo.4426319
title: Mixed Layer Depth Anomaly

description: time is in days since 0
DOI: 10.5281/zenodo.4425341
title: Mixed Layer Depth Anomaly

description: time is in days since 0
DOI: 10.6073/pasta/5559946b335ed5f1b3373e922efdec1e
title: Biocomplexity at North Temperate Lakes LTER; Coordinated Field Studies: Littoral Plots 2001 - 2009

description: In 2001 - 2004 the abundance of coarse wood and other aspects of the physical structure of the littoral zone were surveyed along transects that followed the 0.5 m depth contour at 488 sites in Vilas County. These data were collected as part of the "cross-lake comparison" segment of the Biocomplexity Project (Landscape Context - Coordinated Field Studies). The study explored the links between terrestrial and aquatic systems across a gradient of residential development and lake landscape position. Specifically, this project attempted to relate the abundance of Coarse Wood in the littoral zone with abiotic, biotic and anthropogenic features of the adjacent shoreline. Each of the 488 sites was a 50 m stretch of shoreline. The transects started and ended at the beginning and end of the site; the length of each transect, therefore, varied. Logs which were at least 150 cm in length were counted; more detailed descriptions were taken of logs at least 10 cm in diameter and 150 cm long. Information on littoral and shoreline substrate was also collected. Sampling Frequency: each site sampled once Number of sites: 488 sites on 61 Vilas County lakes were sampled from 2001-2004 (approximately 15 different lakes each year; eight sites per lake).
DOI: 10.21227/j174-qv54
title: THoDBRL2015 Database

description: Animal recognition is an active research topic in recent years. Horse’s recognition is an important task in the world and in order to promote horse’s recognition research, the Tunisian Research Groups in Intelligent Machines of University of Sfax (REGIM of Sfax) will provide the Tunisian Horses DataBase of Regim Lab’2015 (THoDBRL’2015) freely of charge to mainly horses’ face recognition researchers and to increase total of researches done to enhance animal recognition. This Database is used in [1]. The main objective of the construction of this database is to identify horses keeping its natural behavior without any direct ontact between the animal and the camera.THoDBRL’2015 database is a multiview horses’ faces database, which is created in March 2015. Images of 47 horses’ faces were captured from four equestrian centers in Sfax (a south town in Tunisia) during 3 days in daylight. The size of the database is 4.67Go and contains 2820 images.All documents and papers that uses the Tunisian Horses DataBase of Regim Lab’2015 (THoDBRL’2015) will acknowledge the use of the database by including an appropriate citation to the following:[1] Islem Jarraya, Wael Ouarda, and Adel M. Alimi. "A preliminary investigation on horses recognition using facial texture features." In 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 2803-2808. IEEE, 2015.
DOI: 10.21227/zxh4-g824
title: THoDBRL2015 Database

description: Animal recognition is an active research topic in recent years. Horse’s recognition is an important task in the world and in order to promote horse’s recognition research, the Tunisian Research Groups in Intelligent Machines of University of Sfax (REGIM of Sfax) will provide the Tunisian Horses DataBase of Regim Lab’2015 (THoDBRL’2015) freely of charge to mainly horses’ face recognition researchers and to increase total of researches done to enhance animal recognition. This Database is used in [1]. The main objective of the construction of this database is to identify horses keeping its natural behavior without any direct ontact between the animal and the camera.THoDBRL’2015 database is a multiview horses’ faces database, which is created in March 2015. Images of 47 horses’ faces were captured from four equestrian centers in Sfax (a south town in Tunisia) during 3 days in daylight. The size of the database is 4.67Go and contains 2820 images.All documents and papers that uses the Tunisian Horses DataBase of Regim Lab’2015 (THoDBRL’2015) will acknowledge the use of the database by including an appropriate citation to the following:[1] Islem Jarraya, Wael Ouarda, and Adel M. Alimi. "A preliminary investigation on horses recognition using facial texture features." In 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 2803-2808. IEEE, 2015.
DOI: 10.21227/zypr-a546
title: ADAB database

description: The ADAB database (The Arabic handwriting Data Base) was developed to advance the research and development of Arabic on-line handwritten systems. This database is developed in cooperation between the Institut fuer Nachrichtentechnik (IfN) and Research Groups in Intelligent Machines, University of Sfax, Tunisia. The text written is from 937 Tunisian town/village names. A pre-label assigned to each file consists of the postcode in a sequence of Numeric Character References, which stored in the UPX file format. An InkML file including trajectory information and a plot image of the word trajectory are also generated. Additional information about the writer can also be provided.All documents and papers that uses the ADAB database will acknowledge the use of the database by including an appropriate citation to the following:[1] H. Boubaker, A. Elbaati, N. Tagougui, M. Kherallah, H. Elabed, and A. M. Alimi, “Online Arabic Databases and Applications,” Book chapter in : Märgner, V., El Abed, H. (eds) “Guide to OCR for Arabic Scripts,” Chp. Part IV pp 541 – 557 , 2012 , Springer.[2] M. Kherallah, N. Tagougui, A. M. Alimi, H. El Abed and V. Margner, “Online Arabic handwriting recognition competition”, In International Conference on Document Analysis and Recognition, pp. 1454-1458 , 2011.[3] H. El Abed, M. Kherallah, V. Margner and A. M. Alimi, “on-line Arabic handwriting recognition competition ADAB database and participating systems”, International Journal on Document Analysis and Recognition, volume 13, July 2010.
DOI: 10.6084/m9.figshare.13285577
title: Controlling nanoparticle formulation: a low-budget prototype for the automation of a microfluidic platform

description: This repository contains the raw data and the code necessary to reproduce the figures published in Loy et al. DATE, DOI, "Controlling nanoparticle formulation: a low-budget prototype for the automation of a microfluidic platform"
DOI: 10.6084/m9.figshare.13285577.v1
title: Controlling nanoparticle formulation: a low-budget prototype for the automation of a microfluidic platform

description: This repository contains the raw data and the code necessary to reproduce the figures published in Loy et al. DATE, DOI, "Controlling nanoparticle formulation: a low-budget prototype for the automation of a microfluidic platform"
DOI: 10.21227/8z2s-cw36
title: REST database

description: Biometric-based hand modality is considered as one of the most popular biometric technologies especially in forensic applications. Hand recognition is an active research topic in recent years and in order to promote hand’s recognition research, the REGIM-Lab.: REsearch Groups in Intelligent Machines, ENIS, University of Sfax, Tunisia provides the REgim Sfax Tunisian hand database (REST database) freely of charge to mainly hand and palmprint recognition researchers.The acquisition is performed using a low cost digital Camera device. The captured left and right hand images are in RGB and have a size of 1536 * 1250 pixels, in low resolution of 72 dpi. The hands are placed in a comfortable way without any contact nor restriction of pegs or template, and the camera should be placed in front of the hand at approximately 50 cm in order to capture, simultaneously, hand and palmprint modalities. However, users are asked to separate their fingers from each other and change angles between them, during acquisition. The lighting of hand images has been naturally diffused due to illumination variations inside the REGIM laboratory environment. The images are collected from 150 subjects in the age group of 6-70 years, over a period of four months. In order to ensure the success of the image acquisition step, the subjects were just requested to place their hand entirely in front of a uniform dark background. All documents and papers that uses the REgim Sfax Tunisian hand database (REST database) will acknowledge the use of the database by including an appropriate citation to the following:Nesrine Charfi, Hanene Trichili, Adel M. Alimi, and Basel Solaiman. "Local invariant representation for multi-instance toucheless palmprint identification." In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 003522-003527. IEEE, 2016.
DOI: 10.5281/zenodo.4319038
title: Parallel CUHRE regions for Genz 1abs in 5 dimensions

description: Parallel CUHRE algorithm performance data from the Genz 1 function, absolute value, in 5 dimensions. The integrand is: $$ | \cos(4 v +5 w + 6 x +7 y + 8 z)/k |$$, with k = 0s .6371054. This integrand is only approximately normalized. These data were colllected with an uncertain version of the parallel algorithm.
DOI: 10.5281/zenodo.4319037
title: Parallel CUHRE regions for Genz 1abs in 5 dimensions

description: Parallel CUHRE algorithm performance data from the Genz 1 function, absolute value, in 5 dimensions. The integrand is: $$ | \cos(4 v +5 w + 6 x +7 y + 8 z)/k |$$, with k = 0s .6371054. This integrand is only approximately normalized. These data were colllected with an uncertain version of the parallel algorithm.
DOI: 10.5281/zenodo.4315393
title: Parallel CUHRE performance data for DES sigma_miscent_y1_scalarintegrand

description: This is detailed performance data from the parallel CUHRE algorithm, integrating the sigma_miscent_y1_scalarintegrand integrand from DES. This is from an early version of the algorithm.
DOI: 10.5281/zenodo.4315392
title: Parallel CUHRE performance data for DES sigma_miscent_y1_scalarintegrand

description: This is detailed performance data from the parallel CUHRE algorithm, integrating the sigma_miscent_y1_scalarintegrand integrand from DES. This is from an early version of the algorithm.
DOI: 10.5281/zenodo.4423332
title: P-COV-20-18

description: <strong>Dataset of research project's ''Lithuanian businesses financial resistance to economic COVID-19 shock and different economic activities losses evaluation'' (LIFA) (No. P-COV-20-18) (funded by Research Council of Lithuania) is presented in full in these annexes.</strong> The research project ''Lithuanian businesses financial resistance to economic COVID-19 shock and different economic activities losses evaluation'' is designed to assess the change in the set of financial indicators of selected companies of different economic activities of the country (according to NACE2) related to the impact of the COVID-19 pandemic and the probable net damage (additional benefits and losses). Evaluation has two significant components: financial and economic. The evaluation was carried out from the first quarter of 2005 to second quarter of 2020 based on data collected from different databases (Statistics Departments, Bloomberg, European Commission, World Bank, etc.). The assessment of financial resilience and economic net damage modeled the baseline situation, which would have taken into account the macroeconomic indicators “without the impact of COVID-19” (baseline situation) in each of the sectors concerned, and compared it with the actual situation (Q1 and Q2 2020) indicators in order to determine the net effect. <strong>All input data, calculated indicators, and output data are provided in separate files (see annexes). </strong>
DOI: 10.5281/zenodo.4423331
title: P-COV-20-18

description: <strong>Dataset of research project's ''Lithuanian businesses financial resistance to economic COVID-19 shock and different economic activities losses evaluation'' (LIFA) (No. P-COV-20-18) (funded by Research Council of Lithuania) is presented in full in these annexes.</strong> The research project ''Lithuanian businesses financial resistance to economic COVID-19 shock and different economic activities losses evaluation'' is designed to assess the change in the set of financial indicators of selected companies of different economic activities of the country (according to NACE2) related to the impact of the COVID-19 pandemic and the probable net damage (additional benefits and losses). Evaluation has two significant components: financial and economic. The evaluation was carried out from the first quarter of 2005 to second quarter of 2020 based on data collected from different databases (Statistics Departments, Bloomberg, European Commission, World Bank, etc.). The assessment of financial resilience and economic net damage modeled the baseline situation, which would have taken into account the macroeconomic indicators “without the impact of COVID-19” (baseline situation) in each of the sectors concerned, and compared it with the actual situation (Q1 and Q2 2020) indicators in order to determine the net effect. <strong>All input data, calculated indicators, and output data are provided in separate files (see annexes). </strong>
DOI: 10.21227/9df9-ws96
title: REST database

description: Biometric-based hand modality is considered as one of the most popular biometric technologies especially in forensic applications. Hand recognition is an active research topic in recent years and in order to promote hand’s recognition research, the REGIM-Lab.: REsearch Groups in Intelligent Machines, ENIS, University of Sfax, Tunisia provides the REgim Sfax Tunisian hand database (REST database) freely of charge to mainly hand and palmprint recognition researchers.The acquisition is performed using a low cost digital Camera device. The captured left and right hand images are in RGB and have a size of 1536 * 1250 pixels, in low resolution of 72 dpi. The hands are placed in a comfortable way without any contact nor restriction of pegs or template, and the camera should be placed in front of the hand at approximately 50 cm in order to capture, simultaneously, hand and palmprint modalities. However, users are asked to separate their fingers from each other and change angles between them, during acquisition. The lighting of hand images has been naturally diffused due to illumination variations inside the REGIM laboratory environment. The images are collected from 150 subjects in the age group of 6-70 years, over a period of four months. In order to ensure the success of the image acquisition step, the subjects were just requested to place their hand entirely in front of a uniform dark background.All documents and papers that uses the REgim Sfax Tunisian hand database (REST database) will acknowledge the use of the database by including an appropriate citation to the following:Nesrine Charfi, Hanene Trichili, Adel M. Alimi, and Basel Solaiman. "Local invariant representation for multi-instance toucheless palmprint identification." In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 003522-003527. IEEE, 2016.
DOI: 10.6073/pasta/4270ff83f190674d7bb1e266fdb0701a
title: Biocomplexity at North Temperate Lakes LTER; Coordinated Field Studies: Riparian Plots 2001 - 2004

description: Living and dead trees and abiotic and anthropogenic characteristics of the shoreline were surveyed at 488 sites around lakes in Vilas County. These data were collected as part of the "cross-lake comparison" segment of the Biocomplexity Project (Landscape Context - Coordinated Field Studies). The study explored the links between terrestrial and aquatic systems across a gradient of residential development and lake landscape position. Specifically, this project attempted to relate the abundance of coarse wood in the littoral zone with abiotic, biotic and anthropogenic features of the adjacent shore. At each of the 488 sites, three 100 sq m plots, extending from the shoreline 10 m inland, were sampled. Additional plots farther inland were sampled at some sites. At each plot the survey team recorded the general appearance of the plot, measured all trees at least 5 cm dbh, measured and described downed wood and snags at least 10 cm in diameter, and recorded any overhanging trees. Saplings (at least 30 cm tall, but less than 5 cm dbh) were counted in two 5m x 5m plots per site. Sampling Frequency: each site sampled once Number of sites: 488 sites on 61 Vilas County lakes were sampled from 2001-2004 (approximately 15 different lakes each year; eight sites per lake).
DOI: 10.21227/2wxh-1p02
title: THDD Database

description: Animal detection is an active research topic in recent years. Horse’s face detection is an important task in the world and in order to promote horse’s detection and recognition research, the REGIM-Lab.: REsearch Groups in Intelligent Machines, ENIS, University of Sfax, Tunisia will provide the Tunisian Horses Detection Database (THDD Database) freely of charge to mainly horses’ face detection researchers and to increase total of researches done to enhance animal detection.All documents and papers that uses the Tunisian Horses Detection Database (THDD Database) will acknowledge the use of the database by including an appropriate citation to the following:[1] Islem Jarraya, Wael Ouarda, and Adel M. Alimi. "A preliminary investigation on horses recognition using facial texture features." In 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 2803-2808. IEEE, 2015.[2] Islem Jarraya, Wael Ouarda, and Adel M. Alimi. "Deep neural network features for horses identity recognition using multiview horses’ face pattern." In Ninth International Conference on Machine Vision (ICMV 2016), vol. 10341, p. 103410B. International Society for Optics and Photonics, 2017.[3] Wael Ouarda, Hanene Trichili, Adel M. Alimi, and Basel Solaiman. "Towards A Novel Biometric System For Smart Riding Club." Journal of Information Assurance & Security 11, no. 4 (2016).
DOI: 10.6084/m9.figshare.13525637
title: Propagation constants of a particular step-index fiber

description: Propagation constants for a step-index fiber with 5 micrometer core radius and 14 percent GeO2 doping. Sellmeier coefficients were used to calculate material indices.<br>
DOI: 10.6084/m9.figshare.13525637.v2
title: Propagation constants of a particular step-index fiber

description: Propagation constants for a step-index fiber with 5 micrometer core radius and 14 percent GeO2 doping. Sellmeier coefficients were used to calculate material indices.<br>
DOI: 10.6084/m9.figshare.13525637.v1
title: Propagation constants of a particular step-index fiber

description: Propagation constants for a step-index fiber with 5 micrometer core radius and 14 percent GeO2 doping. Sellmeier coefficients were used to calculate material indices.<br>
DOI: 10.15482/usda.adc/1520663
title: Supporting Materials: Skeletal muscle transcriptome in myoglobin knockout mice
DOI: 10.6073/pasta/4b63aa96e05438e79c1c857a0202af8c
title: Biocomplexity at North Temperate Lakes LTER; Coordinated Field Studies: Littoral Macrophytes 2001 - 2004

description: The aquatic vegetation of 60 lakes selected for the "cross-lake comparison" segment of the Biocomplexity Project was surveyed during the month of August in years 2001 through 2004. The study lakes are located in Vilas County, Wisconsin and were chosen to represent a range of positions on gradients of both human development and landscape position. The purpose of the macrophyte portion of the "cross-lake comparison" study was to evaluate the roles of landscape position and human development in the presence and composition of macrophyte communities.
DOI: 10.15482/usda.adc/1520664
title: Supporting Materials: Brown adipose tissue transcriptome in myoglobin knockout mice
DOI: 10.6084/m9.figshare.9995618
title: Repeats Proteins contact prediction based modelling datasets

description: Datasets of repeats proteins model obtained by contact prediction based modelling.<br>The multiple sequence alignments were generate using HHblits with an E-value cutoff of 0.001 against the Uniclust30_2017_04 database.<br>Protein contacts were calculated with PconsC4 and Deep Meta Psicov were used as input for Confold. The modelling was run using the top scoring 1.5 L contracts where L is the length of the modelled regions and the two-stage modelling.For trRosetta the angles and distances were calculated with trRosetta and folded with pyRosetta.<br>
DOI: 10.6084/m9.figshare.9995618.v1
title: Repeats Proteins contact prediction based modelling datasets

description: Datasets of repeats proteins model obtained by contact prediction based modelling.<br>The multiple sequence alignments were generate using HHblits with an E-value cutoff of 0.001 against the Uniclust30_2017_04 database.<br>Protein contacts were calculated with PconsC4 and Deep Meta Psicov were used as input for Confold. The modelling was run using the top scoring 1.5 L contracts where L is the length of the modelled regions and the two-stage modelling.<br>
DOI: 10.6084/m9.figshare.13542098
title: Propagation constants for a step-index fiber with 5 mi- crometer core radius and 14 percent GeO2 doping

description: Sellmeier coefficients were used to calculate material indices. V-b diagram was used to calculate propagation constants.
DOI: 10.6084/m9.figshare.13542098.v1
title: Propagation constants for a step-index fiber with 5 mi- crometer core radius and 14 percent GeO2 doping

description: Sellmeier coefficients were used to calculate material indices. V-b diagram was used to calculate propagation constants.