cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/57e19f213f74aac49848d25301594d8b
title: Jornada Basin LTER: Wireless substation at NPP T-EAST site: 30-minute soil volumetric water content data: 2013 - ongoing

description: 30-minute Soil Moisture data is measured at 10cm, 20 cm, and 30 cm soil depths at the NPP T-EAST substation located in center of 70m x 70m Jornada LTER NPP T-EAST site. Collection of soil volumetric water content data at Jornada LTER NPP sites, New Mexico, supports the environmental monitoring objectives of the Jornada LTER monitoring program that look at plant-soil water dynamics. Volumetric water content, bulk electrical conductivity, soil temperature, and bulk dielectric permittivity are measured every 30 minutes. This is an ONGOING dataset.
DOI: 10.5281/zenodo.4271643
title: Hungarian Novel Corpus (ELTeC)

description: No description provided.
DOI: 10.6073/pasta/c45f1626b38d3c7b3b0eb0712347e9c4
title: Jornada Basin LTER: Wireless substation at NPP T-TAYL site: 30-minute soil volumetric water content data: 2013 - ongoing

description: 30-minute Soil Moisture data is measured at 10cm, 20 cm, and 30 cm soil depths at the NPP T-TAYL substation located in center of 70m x 70m Jornada LTER NPP T-TAYL site. Collection of soil volumetric water content data at Jornada LTER NPP sites, New Mexico, supports the environmental monitoring objectives of the Jornada LTER monitoring program that look at plant-soil water dynamics. Volumetric water content, bulk electrical conductivity, soil temperature, and bulk dielectric permittivity are measured every 30 minutes. This is an ONGOING dataset.
DOI: 10.21227/xhaa-ss97
title: Baseline 8e

description: Same as method as described in baseline 8c, exept that the monthly data have been corrected using a factor. This factor has been computed according to the number of missing days in each month.
DOI: 10.5281/zenodo.4271645
title: Romanian Novel Corpus (ELTeC-rom): Release with 80 novels encoded at level 1.

description: This release adds numerous novels to the collection, taking the total to 80 novels encoded at level 1.
DOI: 10.21227/w5wt-j695
title: 6.66 kVA Solar

description: This dataset is in support of my original research paper " 6.66kVA Solar " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60). Paper is under review.This is not connected to the grid. The other related dataset " " , where this will be connected to the inverter which in turn will be connected to grid.Though few similar kind papers are there, this paper is original and different from them as evident from the abstract and the transaction paper , and is based on the actual submitted project for funding. ( project didn't got any funding, though from self finances, submission was acceptable !)Abstract of the above submitted paper
DOI: 10.5281/zenodo.4271648
title: Slovenian Novel Corpus (ELTeC-slv): Release with 100 novels in level 1 and level 2 encoding.

description: No description provided.
DOI: 10.21227/4687-m787
title: Baseline 8f

description: Same as baseline 8c, with a correcting factor on the monthly aggregated data, taking in ccount the (number of missing days-2), considering that there will also be missing days in the predicted data.
DOI: 10.5281/zenodo.4271661
title: Spanish Novel Corpus (ELTeC-spa): Release with 81 novels

description: Release with 81 novels in level 1.
DOI: 10.21227/zr5e-za86
title: submission

description: dt max depth
DOI: 10.5281/zenodo.4271662
title: Serbian Novel Corpus (ELTeC-srp): Release with 67 novels encoded at level 1.

description: No description provided.
DOI: 10.5281/zenodo.4271681
title: Swedish Novel Corpus (ELTeC-swe): Release with 58 novels.

description: No description provided.
DOI: 10.5281/zenodo.4271627
title: German Novel Corpus (ELTeC-deu): Release with 98 novels at level 0

description: No description provided.
DOI: 10.5281/zenodo.4274866
title: Trajectories of RNA adsorption on a flat surface

description: SPQR trajectories of a RNA fragment (40 nts). More to come...
description: Any further information please contact horacio.v.g@gmail.com
DOI: 10.5281/zenodo.4270252
title: Trajectories of RNA adsorption on a flat surface

description: SPQR trajectories of a RNA fragment (40 nts). More to come...
description: Any further information please contact horacio.v.g@gmail.com
DOI: 10.5281/zenodo.4271467
title: European Literary Text Collection (ELTeC): Release with 10 collections of at least 50 novels.

description: This release has 10 collections of at least 50 novels, 5 of which have reached 100 novels. Note that this an umbrella release, referencing the collections included in the release, but not containing the actual files.
DOI: 10.5281/zenodo.4274951
title: Long read proteogenomics codeathon

description: Datasets for the CSHL LRP Codeathon team
DOI: 10.21227/6fw6-8509
title: AI_QSample_001

description: During the occupancy of a given tenant, the electricity usage of a house is subject to unexpected fluctuation. One notable example is when the house tenets were outstation for a few days (eg on vacation). in such a case, the trend of electric usage is characterized by a very abrupt drop of electricity, which is then followed by a period of very low variations. On the other hand, a house may experience a sudden increase of electrical usage due to special events or guests, or when the house in undergoing on-site renovations or construction projects.
DOI: 10.5281/zenodo.4274812
title: Rhizosphere metagenome of Punica granatum

description: A comparison of microbial community of Rhizosphere and Bulk soil procured from a pomegranate farm.
DOI: 10.5281/zenodo.4274811
title: Rhizosphere metagenome of Punica granatum

description: A comparison of microbial community of Rhizosphere and Bulk soil procured from a pomegranate farm.
DOI: 10.21227/tw1p-0n57
title: Hybrid_baseline_2

description: The approach consists of two steps: dealing missing data and predicting values for the next year. The missing data problem is managed by means of a hybrid approach using linear regression for singular missing values and a clustering procedure for a large set of missing ones. As for the prediction phase, a simple baseline method is used.
DOI: 10.6084/m9.figshare.3761562.v1679
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.21227/yzf4-v297
title: AI_QSample_002

description: During the occupancy of a given tenant, the electricity usage of a house is subject to unexpected fluctuation. One notable example is when the house tenets were outstation for a few days (eg on vacation). in such a case, the trend of electric usage is characterized by a very abrupt drop of electricity, which is then followed by a period of very low variations. On the other hand, a house may experience a sudden increase of electrical usage due to special events or guests, or when the house in undergoing on-site renovations or construction projects.Sometimes, a house may be left vacant as the owner moved to a different house. Moreover, change of tenants may also occur. And even among those houses where the same tenants remains, they may from time to time renovate their houses or even hire building contractors to do so. Such act will permanently change the electric consumption. In such a case, only the most recent cases will be considered.In determining the estimated kWh spent of a month, an electrician will disregard all the incidental cases and will only consider the usual trend, ie when the tenants are carrying on their normal daily lifestyles. The electrical consumption fo much daily lifestyle will hence mostly depends on the temperature. In general, the electric consumption is higher in the colder months of the year. The changing lifestyle of the tenants, though to a lesser extent, also contribute to the permanent change of the electric usage.Our program first work on determining the useful portion of each data by comparing the trends of change across the entire 365 days. First and foremost, our program considers every single number of data as represented. And we have developed out own novel way of similarity measures chich takes even the time of a day into accounts. Such definition of similarity measures enables us to quantitatively determine the differences of consumption pattern across the entire year, yet taking every half hourly data into account. Then, our program will detect all abrupt changes from the pattern it observed, the program will determine on its own, whether such abrupt changes are temporary or permanent. Our program is even able to determine whether such changes are due to even or vacant, and will assign the minimum number of days to be considered a permanent change, based on the nature of such abrupt change. Even for the slight changes of electricity consumption due to gradual change of lifestyle, our program can still detect it via quantitatively measuring the symmetry of those graphs.
DOI: 10.21227/0xmd-qh74
title: DMOPSO based Multivariate Linear Regression Model for Electricity Consumption Prediction from Smart Meter

description: The Particle Swarm Optimization (PSO)algorithm is very useful for solving a high range of complexstatic and dynamic multi-objective real world problems. Thispaper presents a new challenge: Dynamic Multi-ObjectiveParticle Swarm Optimization based Multivariate LinearRegression model (DMOPSO-MLR). The DMOPSO-MLR isused to predict the electricity consumption for the incomingyear. Firstly, the weight vector is initialized randomly as aswarm of particles. Then, the iterative loop of optimizationprocess is started to determine the best weight vector that willbe multiplied with the real data for predicting electricityconsumption. In addition, the weight vector aims to ensure thegood convergence to minimize the difference between predictedand realistic data. This smart prediction system is providedwith a dataset of real historical half-hourly energy readingsfrom 3248 smart meters during twelve months. The proposedsystem is evaluated using the total year and monthly RelativeAbsolute Error. According to the quantitative and thequalitative results, the DMOPSO-MLR is proved as a goodprediction method for electricity consumption.
DOI: 10.21227/00k7-6z97
title: AI_QSample_003

description: During the occupancy of a given tenant, the electricity usage of a house is subject to unexpected fluctuation. One notable example is when the house tenets were outstation for a few days (eg on vacation). in such a case, the trend of electric usage is characterized by a very abrupt drop of electricity, which is then followed by a period of very low variations. On the other hand, a house may experience a sudden increase of electrical usage due to special events or guests, or when the house in undergoing on-site renovations or construction projects.Sometimes, a house may be left vacant as the owner moved to a different house. Moreover, change of tenants may also occur. And even among those houses where the same tenants remains, they may from time to time renovate their houses or even hire building contractors to do so. Such act will permanently change the electric consumption. In such a case, only the most recent cases will be considered.In determining the estimated kWh spent of a month, an electrician will disregard all the incidental cases and will only consider the usual trend, ie when the tenants are carrying on their normal daily lifestyles. The electrical consumption fo much daily lifestyle will hence mostly depends on the temperature. In general, the electric consumption is higher in the colder months of the year. The changing lifestyle of the tenants, though to a lesser extent, also contribute to the permanent change of the electric usage.Our program first work on determining the useful portion of each data by comparing the trends of change across the entire 365 days. First and foremost, our program considers every single number of data as represented. And we have developed out own novel way of similarity measures chich takes even the time of a day into accounts. Such definition of similarity measures enables us to quantitatively determine the differences of consumption pattern across the entire year, yet taking every half hourly data into account. Then, our program will detect all abrupt changes from the pattern it observed, the program will determine on its own, whether such abrupt changes are temporary or permanent. Our program is even able to determine whether such changes are due to even or vacant, and will assign the minimum number of days to be considered a permanent change, based on the nature of such abrupt change. Even for the slight changes of electricity consumption due to gradual change of lifestyle, our program can still detect it via quantitatively measuring the symmetry of those graphs.
DOI: 10.21227/p9f0-hp53
title: AI_QSample_005

description: During the occupancy of a given tenant, the electricity usage of a house is subject to unexpected fluctuation. One notable example is when the house tenets were outstation for a few days (eg on vacation). in such a case, the trend of electric usage is characterized by a very abrupt drop of electricity, which is then followed by a period of very low variations. On the other hand, a house may experience a sudden increase of electrical usage due to special events or guests, or when the house in undergoing on-site renovations or construction projects.Sometimes, a house may be left vacant as the owner moved to a different house. Moreover, change of tenants may also occur. And even among those houses where the same tenants remains, they may from time to time renovate their houses or even hire building contractors to do so. Such act will permanently change the electric consumption. In such a case, only the most recent cases will be considered.In determining the estimated kWh spent of a month, an electrician will disregard all the incidental cases and will only consider the usual trend, ie when the tenants are carrying on their normal daily lifestyles. The electrical consumption fo much daily lifestyle will hence mostly depends on the temperature. In general, the electric consumption is higher in the colder months of the year. The changing lifestyle of the tenants, though to a lesser extent, also contribute to the permanent change of the electric usage.Our program first work on determining the useful portion of each data by comparing the trends of change across the entire 365 days. First and foremost, our program considers every single number of data as represented. And we have developed out own novel way of similarity measures chich takes even the time of a day into accounts. Such definition of similarity measures enables us to quantitatively determine the differences of consumption pattern across the entire year, yet taking every half hourly data into account. Then, our program will detect all abrupt changes from the pattern it observed, the program will determine on its own, whether such abrupt changes are temporary or permanent. Our program is even able to determine whether such changes are due to even or vacant, and will assign the minimum number of days to be considered a permanent change, based on the nature of such abrupt change. Even for the slight changes of electricity consumption due to gradual change of lifestyle, our program can still detect it via quantitatively measuring the symmetry of those graphs.
DOI: 10.6084/m9.figshare.13116809.v1
title: Gene catalogue (compl.incompl.95.prot.faa)

description: Gene catalogue associated with MAGs generated as part of this study
DOI: 10.21227/syra-d173
title: AI_QSample_011

description: During the occupancy of a given tenant, the electricity usage of a house is subject to unexpected fluctuation. One notable example is when the house tenets were outstation for a few days (eg on vacation). in such a case, the trend of electric usage is characterized by a very abrupt drop of electricity, which is then followed by a period of very low variations. On the other hand, a house may experience a sudden increase of electrical usage due to special events or guests, or when the house in undergoing on-site renovations or construction projects.Sometimes, a house may be left vacant as the owner moved to a different house. Moreover, change of tenants may also occur. And even among those houses where the same tenants remains, they may from time to time renovate their houses or even hire building contractors to do so. Such act will permanently change the electric consumption. In such a case, only the most recent cases will be considered.In determining the estimated kWh spent of a month, an electrician will disregard all the incidental cases and will only consider the usual trend, ie when the tenants are carrying on their normal daily lifestyles. The electrical consumption fo much daily lifestyle will hence mostly depends on the temperature. In general, the electric consumption is higher in the colder months of the year. The changing lifestyle of the tenants, though to a lesser extent, also contribute to the permanent change of the electric usage.Our program first work on determining the useful portion of each data by comparing the trends of change across the entire 365 days. First and foremost, our program considers every single number of data as represented. And we have developed out own novel way of similarity measures chich takes even the time of a day into accounts. Such definition of similarity measures enables us to quantitatively determine the differences of consumption pattern across the entire year, yet taking every half hourly data into account. Then, our program will detect all abrupt changes from the pattern it observed, the program will determine on its own, whether such abrupt changes are temporary or permanent. Our program is even able to determine whether such changes are due to even or vacant, and will assign the minimum number of days to be considered a permanent change, based on the nature of such abrupt change. Even for the slight changes of electricity consumption due to gradual change of lifestyle, our program can still detect it via quantitatively measuring the symmetry of those graphs.
DOI: 10.5281/zenodo.4274987
title: Training dataset: Mass spectrometry based proteomics of healthy human serum samples

description: The two raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/). Serum of a healthy person was obtained by centrifugation of full blood in a serum-gelmonovette. One serum sample was depleted for high abundant proteins, the other not.<br> For the non-depleted sample: 5µl of serum was diluted with 0.1% Rapigest, resulting in a concentration of 1mg/ml.<br> Depletion was performed with the Seppro IgY14 Spin columns which are able to deplete 14 high abundent blood proteins by immunoaffinity. For the depleted sample 9µl of serum was diluted with TBS/HCl/NaCl buffer and added to the Seppro IgY14 spin column. After depletion the sample was buffered with Hepes pH 8.0 and Rapigest was added to a final 0.1% Rapigest concentration. From here on, both samples were reduced by adding TCEP, alkylated by IAA and quenched with DTT in solution. Digestion was performed by adding trypsin in a ratio of 1:50 to the samples. After incubation at 37°C, 600rpm, over night, the sample clean-up was performed with the PreOmics desalting columns. iRT peptides were added and the sample was measured with a Q-Exactive Plus mass spectrometer. Besides the two raw files, we uploaded a fasta file that serves as human protein sequence database and the Galaxy MaxQuant training result files: protein groups, peptides, mqpar and PTXQC.
DOI: 10.5281/zenodo.3774451
title: Training dataset: Mass spectrometry based proteomics of healthy human serum samples

description: The two raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/). Serum of a healthy person was obtained by centrifugation of full blood in a serum-gelmonovette. One serum sample was depleted for high abundant proteins, the other not.<br> For the non-depleted sample: 5µl of serum was diluted with 0.1% Rapigest, resulting in a concentration of 1mg/ml.<br> Depletion was performed with the Seppro IgY14 Spin columns which are able to deplete 14 high abundent blood proteins by immunoaffinity. For the depleted sample 9µl of serum was diluted with TBS/HCl/NaCl buffer and added to the Seppro IgY14 spin column. After depletion the sample was buffered with Hepes pH 8.0 and Rapigest was added to a final 0.1% Rapigest concentration. From here on, both samples were reduced by adding TCEP, alkylated by IAA and quenched with DTT in solution. Digestion was performed by adding trypsin in a ratio of 1:50 to the samples. After incubation at 37°C, 600rpm, over night, the sample clean-up was performed with the PreOmics desalting columns. iRT peptides were added and the sample was measured with a Q-Exactive Plus mass spectrometer. Besides the two raw files, we uploaded a fasta file that serves as human protein sequence database and the Galaxy MaxQuant training result files: protein groups, peptides, mqpar and PTXQC.
DOI: 10.25919/23xp-mg67
title: Parkes observations for project P1068 semester 2020APRS_27

description: In the fast-moving world of Fast Radio Bursts (FRBs), the key question of what fraction (as high as 100%?) of FRBs emit repeat bursts remains unanswered. Of order 10 repeating FRBs are now known, and their spectro-temporal properties hint at differences with apparently non-repeating bursts - but poor statistics preclude a definitive diagnosis. FRB 180301 is a promising source for further study for multiple reasons: it is the first repeating FRB seen by the FAST telescope, and has the second-highest rotation measure ever seen for a FRB (after the original repeater, FRB121102). We request Parkes observations with the UWL to characterise the broadband emission properties of this FRB, with the following key goals: to constrain the repetition rate, identify any variation of pulse characteristics over time, and provide insight into the emission mechanisms. In its initial detection, FRB 180301 was poorly fit by regular Faraday rotation models, and showed significant circular polarization, which may indicate the progenitor is embedded in an extreme magneto-ionic environment with complex field structure that has not been seen before. These observations will generate a large data volume. We will use the Medusa backend in parallel with the Breakthrough Listen data recorder (BL), and will transfer data to BL immediately after observations.These data will be used by P. Kumar to prototype a FRB pipeline for the UWL, that will be made broadly available for similar studies.