cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34820/fk2/wxdarr
title: ANALISIS FAKTOR-FAKTOR KESUKSESAN E-LEARNING DALAM MENINGKATKAN PROSES BELAJAR MENGAJAR DI UNIVERSITAS TELKOM

description: Fenomena pembelajaran online saat ini semakin bermunculan dan menjamur seiring dengan perkembangan infrastruktur dan teknologi internet di Indonesia. Pemerintah Indonesia terus mendorong pengembangan sistem pembelajaran online di semua jenjang pendidikan. Padahal, untuk kurikulum 2020 sudah dirancang dalam satu mata kuliah, terdapat 25 persen atau sekitar tiga hingga empat pertemuan dari total pertemuan yang diadakan secara online dalam satu semester. Dalam rangka memberikan penyiapan infrastruktur dan dukungan teknis yang baik demi kelancaran implementasi kurikulum berbasis blended learning semester ganjil 2020/2021, 25 mata kuliah diujicobakan dengan menggunakan blended learning. Oleh karena itu, penelitian ini bertujuan untuk mengetahui bagaimana pengaruh penerapan E-Learning dan motivasi belajar siswa terhadap prestasi belajar siswa. Salah satu mata kuliah dari Program Studi MBTI yang diujicobakan adalah mata kuliah E-Commerce dan Manajemen Data. Sehingga sampel dan populasi penelitian ini adalah 88 mahasiswa kelas internasional yang mengambil mata kuliah E-Commerce yang telah menerapkan LMS CELOE. Data diolah melalui bantuan komputer dengan menggunakan program SPSS21 dan teknik analisis regresi berganda. Hasil penelitian ini menunjukkan bahwa variabel X1 (penggunaan E-Learning) tidak berpengaruh terhadap prestasi belajar siswa, sedangkan variabel X2 (Motivasi) berpengaruh terhadap prestasi belajar siswa. E-Learning belum mendorong siswa untuk belajar secara mandiri dan menurut siswa pembelajaran E-Learning belum menghemat biaya pendidikan secara keseluruhan. Oleh karena itu, masih diperlukan upaya untuk menyadarkan mahasiswa bahwa pembelajaran adalah kebutuhan dan harus ada dalam dirinya sendiri, dan bagi dosen tetap harus mempelajari desain pembelajaran untuk memvariasikan kegiatan yang dilakukan dalam kelas online, agar pembelajaran dapat berjalan dengan baik dan menyenangkan.
DOI: 10.34820/fk2/x2wiih
title: ANALISIS FAKTOR-FAKTOR KESUKSESAN E-LEARNING (STUDI PADA UMKM DI DSP YOGYAKARTA DAN LOMBOK)

description: The outbreak of the COVID-19 pandemic has had a major impact on Indonesia's economic growth. One of the sectors that are affected is tourism. To boost the tourism industry, we must not forget Micro, Small, and Medium Enterprises (MSMEs) which are one of the supporters. During this pandemic, as many as 75% of MSME players admitted to experiencing a significant decline in sales. Most of these business actors do not have a plan in running the business. Therefore, training is usually quite often targeting MSMEs with the aim of increasing the competence of MSME actors to survive in the current digital era. One of the trainings provided for external users by CELOE Telkom University through Open Online Learning is for MSMEs in super-priority destinations of Yogyakarta and Lombok. Therefore, the purpose of this study is to see what E-learning success factors in the implementation of online training conducted by Telkom University. This study tests a conceptual framework using survey data of 60 MSMEs super-priority destinations of Yogyakarta and Lombok and employing structural equation model (SEM) analysis from a partial least square (PLS) approach. The results show that there are two variables that do not influence the system use, they are system quality dan information quality. System users do not understand the system applied in this training, especially in operating the system. Respondents said that the obstacles faced in implementing the system were due to the lack of participation of system users in the training and the lack of user experience in using the E-Learning system. The quality of the system does not have a positive effect on usability because the system is mandatory so the users do not have any other options and no choices. Hence, the research result also can be used to evaluate the success of organizational E-Learning systems from learner perspectives. This evaluation can provide fast and early feedback for Telkom University and take corrective actions if necessary for improvement.
DOI: 10.5281/zenodo.6204296
title: Data and code for publication: A simple preparation protocol for shipping and storage of tissue sections for laser ablation-inductively coupled plasma-mass spectrometry imaging

description: Data & Code release for publication: Rebecca Buchholz, Sebastian Krossa, Maria K Andersen, Michael Holtkamp, Michael Sperling, Uwe Karst, May-Britt Tessem, A simple preparation protocol for shipping and storage of tissue sections for laser ablation-inductively coupled plasma-mass spectrometry imaging, <em>Metallomics</em>, Volume 14, Issue 3, March 2022, mfac013, https://doi.org/10.1093/mtomcs/mfac013 Python code for LA ICP MS imaging data segmentation Code & Data also on github Thresholding based segmentation of LA-ICP-MS imaging data Description src/main.py - run this to process LA ICP MS data in data folder - generates matplotlib.figures - project specific setup src/laicpms_data_handler.py - contains object to import, handle and segment (shimadzu) raw data Dependencies Python 3.8.1 or newer For packages see requirements.txt Data LA-ICP-MS imaging data of human prostate tissue of the elements Zn, Fe & P. Details on data generation & collection in publication. LA-ICP-MS imaging data as plain text files (comma-separated values) Condition 1 = fresh frozen (FF) Condition 2 = room temperature vacuum dried and sealed (RTV) Condition 3 = formalin fixed (FFix) Condition 4 = formalin fixed, paraffin sealed (FFPS) 3 replicate sectioning sets named A, B, C File-naming: LA_Data_CISN1.csv, where I = [1, 2, 3, 4] is indicating the condition used and N = [A, B, C] is indicating the replicate set License Data CC-BY 4.0 - respective LICENSE file in data folder Source code MIT - respective LICENSE file in src folder
DOI: 10.5281/zenodo.6204295
title: Data and code for publication: A simple preparation protocol for shipping and storage of tissue sections for laser ablation-inductively coupled plasma-mass spectrometry imaging

description: Data & Code release for publication: Rebecca Buchholz, Sebastian Krossa, Maria K Andersen, Michael Holtkamp, Michael Sperling, Uwe Karst, May-Britt Tessem, A simple preparation protocol for shipping and storage of tissue sections for laser ablation-inductively coupled plasma-mass spectrometry imaging, <em>Metallomics</em>, Volume 14, Issue 3, March 2022, mfac013, https://doi.org/10.1093/mtomcs/mfac013 Python code for LA ICP MS imaging data segmentation Code & Data also on github Thresholding based segmentation of LA-ICP-MS imaging data Description src/main.py - run this to process LA ICP MS data in data folder - generates matplotlib.figures - project specific setup src/laicpms_data_handler.py - contains object to import, handle and segment (shimadzu) raw data Dependencies Python 3.8.1 or newer For packages see requirements.txt Data LA-ICP-MS imaging data of human prostate tissue of the elements Zn, Fe & P. Details on data generation & collection in publication. LA-ICP-MS imaging data as plain text files (comma-separated values) Condition 1 = fresh frozen (FF) Condition 2 = room temperature vacuum dried and sealed (RTV) Condition 3 = formalin fixed (FFix) Condition 4 = formalin fixed, paraffin sealed (FFPS) 3 replicate sectioning sets named A, B, C File-naming: LA_Data_CISN1.csv, where I = [1, 2, 3, 4] is indicating the condition used and N = [A, B, C] is indicating the replicate set License Data CC-BY 4.0 - respective LICENSE file in data folder Source code MIT - respective LICENSE file in src folder
DOI: 10.34820/fk2/x4mqup
title: PENGARUH PELATIHAN DARING DAN KESIAPAN TEKNOLOGI TERHADAP MOTIVASI BERWIRAUSAHA UMKM DI INDONESIA (STUDI PADA UMKM DI 5 DESTINASI SUPER PRIORITAS DAN BALI)

description: The tourism sector is currently experiencing a significant decline. To increase SMEs' knowledge about digital business management, there are current trends of online training. One of the many challenges in conducting online training is the participants' technological readiness and understanding. The number of training participants decreased quite a lot as the activities progressed. It is necessary to study why the number of trainees can drop to almost half based on the motivation and technological readiness of the trainees. Based on the description above, it is necessary to know how much influence training and technological readiness have on the motivation for the entrepreneurship of SMEs in 5 Super Priority Destinations (Lake Toba, Borobudur, Mandalika, Likupang, and Labuan Bajo) and Bali. The population in this study were 300 SMEs spread across 5 Super Priority Destinations and Bali who participated in online training activities organized by the Ministry of Tourism and Creative Economy in collaboration with Sahabat UMKM and Telkom University. Data processing will be analyzed using path analysis. This research is expected to contribute to the quality of training, the digital skills of the trainees, and how much influence these two variables have on the entrepreneurship motivation of SMEs.
DOI: 10.23708/sdfm1q
title: Mortality of the Lamto (Ivory Coast) humid savanna perennial grass species between 2015 to 2016 according to the circumference of individuals and different fire regimes

description: These data represent the mortality of tussocks of perennial grass submitted since 2013 to four fire treatments: no fire, early fire (November fire), mid-season fire (January fire) and fire late (March fire). They were collected in 2016, on individuals previously marked in 2015, on 12 plots distributed in 3 different sites of shrubby savanna of the Lamto scientific reserve in Côte d'Ivoire. The mortality of these species was studied according to the fire treatments and also the individual circumferences to test the effects of these parameters on the mortality of these grass species. These data constitute part of the data published in Koffi et al. (2019).
DOI: 10.5281/zenodo.6397234
title: Round Robin on turbulence estimates from nacelle mounted Lidar systems

description: Reference Data for CW lidar dataset period 1. LOS Dir: 222.5; 252.4
DOI: 10.5281/zenodo.6397233
title: Round Robin on turbulence estimates from nacelle mounted Lidar systems

description: Reference Data for CW lidar dataset period 1. LOS Dir: 222.5; 252.4
DOI: 10.6084/m9.figshare.19456094
title: Dataset1_RNA.txt

description: LIBRA validation data<br>
DOI: 10.6084/m9.figshare.19456094.v1
title: Dataset1_RNA.txt

description: LIBRA validation data<br>
DOI: 10.6084/m9.figshare.19464197
title: Technical report: In-gel sample preparation prior to proteomic analysis of bovine faeces increases protein identifications by removal of high molecular weight glycoproteins

description: Supplementary table and figure to the article
DOI: 10.6084/m9.figshare.19464197.v1
title: Technical report: In-gel sample preparation prior to proteomic analysis of bovine faeces increases protein identifications by removal of high molecular weight glycoproteins

description: Supplementary table and figure to the article
DOI: 10.25345/c5125qd5w
title: MassIVE MSV000089158 - ERG-Proximity biotin labeling_2022-03-30
title: ERG N-terminal FLag-BirA* fusion, BioID performed in T-REx Flp-In HEK923.
DOI: 10.34820/fk2/qqjpbj/pgju0k
title: DATA ETAR.tab

description: :unav
DOI: 10.34820/fk2/qqjpbj
title: BUZZ MARKETING ANALYSIS OF EFFECT ON BRAND IMAGE, BRAND AWARENESS, AND PURCHASING DECISION THROUGH TWITTER

description: Internet has become one of modern communication tools in the development technology era. Internet users in Indonesia have increased from year to year. The highest activity is for social networking. Social networking is becoming a growing trend. In 2012, Indonesia is the fifth largest in the world of Twitter users as much as 29 million users. Twitter as one of the media for buzz marketing could be categorized as an effective tool for making a brand image of product that can be accepted and distributed to third party, consumers who chose the brand they will use, and redistribute about that brand. Buzz marketing or commonly known as viral marketing is a marketing activity in communication media which creates "noise" significant and appealing to the target audience to discuss about the product and give it a try. Research methods used in this research is descriptive and causal research by collecting primary and secondary data. Sampling technique in this study was non-probability convenience sampling. This study uses SEM (Structural Equation Modeling), a multivariate statistical analysis technique that analyzes the relation structured. This technique is a combination of factor analysis and multiple regression analysis. The results of this study are expected to be useful for the development of science and the needs of businesses, especially in marketing field.
DOI: 10.34740/kaggle/dsv/3382029
title: COVID Vaccination in World (updated daily)
DOI: 10.21227/69sh-m528
title: Experimental Materials

description: The experimental materials  include :Experimental video, experimental data, PID parameter tuning method and experimental program.
DOI: 10.5281/zenodo.6396977
title: WP5.3 Screening drug molecule that inhibit iron transport

description: TXTL reaction
DOI: 10.5281/zenodo.6396976
title: WP5.3 Screening drug molecule that inhibit iron transport

description: TXTL reaction
DOI: 10.23708/oiisfv
title: Growth and biomass allocation of West African savanna and forest tree seedlings in response to fire and shading. A dataset of a field experiment conducted at Lamto, Côte d'Ivoire, 2001-2002.

description: This dataset contains all the measurements on which the Gignoux et al. (2016) associated artilce is based. It describes the results of a 15-months growth experiment testing the response of 2 species of savanna tree seedlings and 2 species of forest tree species, all growing in the Lamto reserve, Côte d'Ivoire (6°13′​N, 5°02′​W), to fire and shading treatments. 432 seedlings of Ceiba pentandra, Cynometra megalophylla (rainforest species), Bridelia ferruginea and Piliostigma thonningii (humid savanna species) were grown during 15 months under 2 fire (burned, unburned) and shading (full light, 40% full light) experimental treatments. All seedlings were measured (1) every 3 months for height, number of leaves, stem diameter; (2) once for photosynthetic assimilation (Amax) under two different light levels matching the levels of the shading treatment; (3) three times for root, stem, leaf biomass, an leaf surface (destructive sampling of 36 sedlings per date). This work was not funded by any important programme.
DOI: 10.5281/zenodo.6397278
title: Puerto Rican CT Craniometric Known Data

description: Puerto Rican craniometric data collected from brain CT scans of known individuals.
DOI: 10.5281/zenodo.6397277
title: Puerto Rican CT Craniometric Known Data

description: Puerto Rican craniometric data collected from brain CT scans of known individuals.
DOI: 10.25500/edata.bham.00000816
title: CORTEX Automotive Radar and Video with GPS/IMU Ground Truth Dataset from Trials at MIRA Test Track - Run6
DOI: 10.20387/bonares-q9b3-z989
title: Open Tension-disk Infiltrometer Meta-database (OTIM)

description: Saturated and near-saturated hydraulic conductivities K (mm/h) are important soil properties that determine the partitioning of precipitation into surface runoff and infiltration and indicate soil susceptibility to preferential flow as well as soil aeration properties. Measurements of saturated and near-saturated soil hydraulic conductivities are time consuming and not practical for larger scales where they are mostly needed. The research community has therefore put effort in deriving pedotransfer functions to predict K using proxy variables. The precision of such pedotransfer functions has been very modest, however. As a result, recent studies have focused on assembling and analyzing bigger databases, aiming to find better predictors for the saturated and near-saturated soil hydraulic conductivities. A prominent example is the meta-database on tension-disk infiltrometer data compiled by Jarvis et al. (2013. Influence of soil, land use and climatic factors on the hydraulic conductivity of soil. Hydrology and Earth System Sciences 17(12), 5185-5195), who found that climate variables were better correlated with K than soil properties. OTIM (Open Tension-disk Infiltrometer Meta-database) builds on this database, adding 577 new data entries collated from 48 additional peer-reviewed scientific publications. OTIM contains more detailed information on local climate as well as land use and management.
DOI: 10.5281/zenodo.6397342
title: Data for: The rock-forming minerals and macroscale mechanical properties of asteroid rocks

description: HaH 346 meteorite samples are tested using the nanoindentation experiment with Berkovich indenter. The data includes the Young’s modulus of different rock-forming minerals in HaH 346 meteorites measured by nanoindentation test.
DOI: 10.5281/zenodo.6397341
title: Data for: The rock-forming minerals and macroscale mechanical properties of asteroid rocks

description: HaH 346 meteorite samples are tested using the nanoindentation experiment with Berkovich indenter. The data includes the Young’s modulus of different rock-forming minerals in HaH 346 meteorites measured by nanoindentation test.
DOI: 10.6073/pasta/7429426a13d5b6134893a8fc5e6dc5e5
title: Chemistry of rainfall and throughfall from El Verde and Bisley

description: Rain, throughfall, and stream water are collected weekly at the LEF sites listed below. Samples are collected by USDA Forest Service technicians Carlos Estrada in the field filtered in the lab by Miriam Salgado. These data sets begin as early as 1983; LTER sampling began in 1988. Rain and throughfall samples are the total catch for the week, and are exposed to field conditions for that time. No event sampling is conducted on a routine basis. Rain samples from WDEV are wet only from an automatically-closing collector that prevents any dry deposition (Aerochem Metrics NADP collector). RCEV and RCB are bulk or always-open collectors that receive dry deposition by sedimentation. All samples are measured for pH and conductivity, and then filtered (pre-combusted Whatman GF/F glass fiber filter) prior to further analysis. From 1983-1994 samples were cooled and returned to the San Juan chemistry laboratory for analysis. During those years, samples for NH4 and NO3 analyses were refrigerated continuously until analysis. Subsamples for NH4 analysis were also preserved with 1 molar H2SO4. From 1994 on, samples for NH4 and NO3 were frozen until analysis, were not acidified, and all analyses were conducted at the University of New Hampshire. Rain and Throughfall Sampling SitesDescriptions of LTER LUQ rain and throughfall weekly sample chemistry data from 1988 onwards. Chemical concentrations are recorded as mg/L or ug/L as appropriate. Values below detection limits are recorded as 1/2 the detection limit. Site Abbreviation Description Comments Rain collector Bisley. RCB Bulk collector. Rain collector El Verde RCEV Bulk collector. Wet/dry El Verde. WDEV. Wet only collector. Throughfall Bisley. TFB=TCB TF bulk 10-collector composite Bisley gap= BGAP, TF bulk 10-collector composite
DOI: 10.5517/ccdc.csd.cc299mkt
title: CCDC 2124416: Experimental Crystal Structure Determination

description: Related Article: Yutaro Yamashita, Shohei Tashiro, Mitsuhiko Shionoya|2022|CrystEngComm|24|1518|doi:10.1039/D1CE01654G
DOI: 10.5517/ccdc.csd.cc29cy2q
title: CCDC 2126633: Experimental Crystal Structure Determination

description: Related Article: Guang-Chuan Ou, Hai-Yang Chen, Qiong Wang, Qiang Zhou, Fei Zeng|2022|RSC Advances|12|6459|doi:10.1039/D1RA09284G
DOI: 10.14469/hpc/10316
title: PhBF2 in thiophene, closed

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10317
title: CFCl3 in thiophene

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10318
title: CFCl3 in pyridine

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10319
title: CFCl3 in acetonitrile

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10320
title: CFCl3 in chloroform

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10321
title: CFCl3 in thf

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10322
title: PhBF2+pyridine, closed, -168.2, -169.1 (-168.7) G = -704.290940

description: Gaussian 16 (C01) calculation
DOI: 10.5281/zenodo.6397318
title: dMRI-RCNN Pre-Trained Weights - 3D b1000 6q

description: Pre-trained weights for dMRI-RCNN. 3D RCNN model, for q_in = 6, b = 1000.
DOI: 10.5281/zenodo.6397317
title: dMRI-RCNN Pre-Trained Weights - 3D b1000 6q

description: Pre-trained weights for dMRI-RCNN. 3D RCNN model, for q_in = 6, b = 1000.
DOI: 10.5281/zenodo.6397221
title: dMRI-RCNN Pre-Trained Weights - 3D 10q

description: Pre-trained weights for dMRI-RCNN. 3D RCNN model, for q_in = 10 b = {1000, 2000, 3000}.
DOI: 10.5281/zenodo.6397220
title: dMRI-RCNN Pre-Trained Weights - 3D 10q

description: Pre-trained weights for dMRI-RCNN. 3D RCNN model, for q_in = 10 b = {1000, 2000, 3000}.
DOI: 10.5281/zenodo.6397392
title: dMRI-RCNN Pre-Trained Weights - 3D b1000 10q

description: Pre-trained weights for dMRI-RCNN. 3D RCNN model, for q_in = 10, b = 1000.
DOI: 10.5281/zenodo.6397391
title: dMRI-RCNN Pre-Trained Weights - 3D b1000 10q

description: Pre-trained weights for dMRI-RCNN. 3D RCNN model, for q_in = 10, b = 1000.
DOI: 10.26188/624464131f486
title: PhD thesis: Supplementary tables and figures

description: This dataset contains supplementary tables and figures associated with the PhD thesis 'Investigating features and interactions of the childhood respiratory microbiome' authored by Stephen Watts.<br>
DOI: 10.5066/p9k15p8y
title: Demographic model inputs and code, catchment area population estimates, and counterfactual (CIU) estimates for population growth for 23 focal bird species.

description: Renewable energy production can kill birds, but little is known about how it affects avian populations. We assessed vulnerability of populations for 23 priority bird species killed at wind and solar facilities in California, USA.
DOI: 10.5281/zenodo.6397405
title: dMRI-RCNN Pre-Trained Weights - 3D b1000 30q

description: Pre-trained weights for dMRI-RCNN. 3D RCNN model, for q_in = 30, b = 1000.
DOI: 10.5281/zenodo.6397404
title: dMRI-RCNN Pre-Trained Weights - 3D b1000 30q

description: Pre-trained weights for dMRI-RCNN. 3D RCNN model, for q_in = 30, b = 1000.
DOI: 10.5281/zenodo.6397413
title: dMRI-RCNN Pre-Trained Weights - 3D b2000 10q

description: Pre-trained weights for dMRI-RCNN. 3D RCNN model, for q_in = 10, b = 2000.
DOI: 10.5281/zenodo.6397412
title: dMRI-RCNN Pre-Trained Weights - 3D b2000 10q

description: Pre-trained weights for dMRI-RCNN. 3D RCNN model, for q_in = 10, b = 2000.
DOI: 10.5281/zenodo.3361582
title: Single-pass STEM-EMCD on a zone axis using apatterned aperture: Data and methodology

description: Raw data and code accompanying the paper "Experimental progress towards single-pass STEM-EMCD on a zone axis using a patterned aperture"
DOI: 10.5281/zenodo.3361581
title: Single-pass STEM-EMCD on a zone axis using apatterned aperture: Data and methodology

description: Raw data and code accompanying the paper "Experimental progress towards single-pass STEM-EMCD on a zone axis using a patterned aperture"
DOI: 10.5281/zenodo.6397422
title: dMRI-RCNN Pre-Trained Weights - 3D b3000 10q

description: Pre-trained weights for dMRI-RCNN. 3D RCNN model, for q_in = 10, b = 3000.
DOI: 10.5281/zenodo.6397421
title: dMRI-RCNN Pre-Trained Weights - 3D b3000 10q

description: Pre-trained weights for dMRI-RCNN. 3D RCNN model, for q_in = 10, b = 3000.
DOI: 10.5281/zenodo.6397440
title: dMRI-RCNN Pre-Trained Weights - 1D b1000 6q

description: Pre-trained weights for dMRI-RCNN. 1D RCNN model, for q_in = 6, b = 1000.
DOI: 10.5281/zenodo.6397439
title: dMRI-RCNN Pre-Trained Weights - 1D b1000 6q

description: Pre-trained weights for dMRI-RCNN. 1D RCNN model, for q_in = 6, b = 1000.