cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/rrg4pnm36z.1
title: The Neuropsychiatric Phenotype of anti-NMDAr Encephalitis

description: A prospective and longitudinal study was conducted, with patients admitted to the National Institute of Neurology and Neurosurgery of Mexico, who fulfilled criteria for possible autoimmune encephalitis along a time window of seven years. Systematic clinical assessments were conducted before knowing NMDAR antibodies' results, including a bespoke inventory to register a broad scope of neuropsychiatric features, and different scales to measure the behavioral, cognitive, and functional features. CSF antibodies against the NR1 subunit of the NMDA receptor were processed with rat brain immunohistochemistry and cell-based assays with NMDA expressing cells. Outcome measures were registered.
DOI: 10.34810/data72/529
title: 02AC_1.MR.0009.0155.2013.03.10.09.04.41.296875.165176106.IMA
DOI: 10.17632/pvxxnhrvbv.2
title: Magnetic parameters for ODP Hole 885A

description: This data set contains the magnetic parameters for ODP Hole 885A since the 8.0 Ma in the paper “Magnetotactic bacterial activity in the North Pacific Ocean and its relationship to Asian dust inputs and primary productivity since 8.0 Ma”.
DOI: 10.17632/wcrrtjh37k.1
title: AGFACE CO2 Dataset

description: Within the Australian Grains Free Air CO2 Enrichment (AGFACE) research program, several facilities were established at different field sites near the towns of Horsham (36.752 S, 142.114 E; 127 m elevation), and Walpeup (35.121 S, 142.005 E; 94 m elevation) in the state of Victoria Australia from 2007 – 2017. These included: TraitFACE, SoilFACE, WalpeupFACE, VegeFACE, and NFACE. These facilities were designed to answer different questions to support research to understand the impacts of elevated CO2 on crop physiology and production. FACE ‘rings’ (octagons) were built to elevate atmospheric CO2 to 550 µmol/mol expected by 2050 and allowing crops to grow freely, without enclosures. Each side of an octagonal ring was individually controlled by a ring-side controller that injected CO2 over crops as per control program. Infrared Gas Analysers (IRGAs) placed at ring centres sampled and analysed air continuously. IRGAs measured atmospheric CO2 levels during the cropping season and provided feedback to the controller to maintain ring-centre CO2 concentration ([CO2]) at 550 µmol/mol. The [CO2] data, measured each second, were collected from the centre of each active FACE ring from 2007 until 2017. Wind direction and speed were monitored continuously at 2 m above the soil surface at the centre of each ring. These measurements were also collected at the centres of a couple of ambient experimental areas (control – no rings) using the same IRGA and wind sensors. A wireless ethernet local area network (LAN) and a Visual Basic program were used to monitor and transmit data from the individual rings and control areas for data logging. Data at every 4th second and one-minute average (A_MN_CO2) from each ring were logged to daily files, and only A_MN_CO2 data were combined into a seasonal cumulative file. All data recorded during the IRGA warmup period and due to equipment malfunction were removed from cleaned data files. Only A_MN_CO2 data from the rings are uploaded in the Mendeley Data Repository, because these data are principally used by the scientists and researchers. Data columns in an individual clean file are labelled with abbreviated column names and their description is provided in Table 3 of the related article. • These data are useful for the design and performance of Free Air CO2 Enrichment (FACE) systems which inject pure CO2 from the horizontal tubes surrounding FACE rings. It will provide data to assess the variation and stability of CO2 at ring-centres. Models for wind speed and direction vs [CO2] can be developed from these data as co-variates to analyse biophysical data. • Engineers and scientists who design and develop FACE systems will benefit from these data. • Year-to-year [CO2] data at ring-centre can be analysed to evaluate the performance of pure CO2 injected FACE rings for various ring sizes and wind speeds. This will help engineers and scientists to optimise the FACE design.
DOI: 10.17632/229bfk2d2b.1
title: Rathore et al. 2021 - MS Datasets

description: MS Monosome Results WT and Mtg1-KO Exp #1 and #2: Scaffold file contains masss-pectrometry results, from the analysis of sucrose gradient fractions containing mitochondrial monosome, from WT (YEAST_MONO_WT ) and Mtg1-ko (YEAST_MONO_MTG1) yeast strains. Mitochondria isolated from both strains were analyzed in two independet experiments. CL-MS Exp #1: raw data from the first crosslinking mass spectrometry experiment. Excell file contains: list of crosslinked peptides and list of all detected proteins. CL-MS Exp #2 : raw data from the second crosslinking mass spectrometry experiment. Excell file contains: list of crosslinked peptides and list of all detected proteins.
DOI: 10.17632/43d5f6j3xc.1
title: Data and R codes for the fuzzy FMEA assessment of hydroelectric earth dam failure modes

description: It constains two sets of R codes and their corresponding data and rules, which were used to to calculate the Fuzzy Inference System Risk Priority Number (FIS-RPN) under the Fuzzy FMEA approach for an earth dam hydropower plant located in Brazil. In the first set, we computed the RCI through the combined effect of Occurrence and Severity. In the second set, we combined the RCI with Detection to compute the FIS-RPN. In addition, it contains two Excel files, one has the FIS-RPN results of a case study, and the other has the Pearson and Kendall statistics comparing the FIS-RPN estimates of five experts.
DOI: 10.17632/7k9sg622g7.1
title: Dataset of "A Meta-analysis on the Success of Resin Composite Restorations Since 2010"

description: This dataset consists of data extracted from the following studies: 1. Rocha A, Salas MS, Masotti A, da Rosa W, Zanchi C, Lund R. A Randomized Double-blind Clinical Trial of Dentin Surface Treatments for Composite Restorations in Noncarious Cervical Lesions: A 36-month Evaluation. Operative dentistry. 2019;44(2):114-26. 2. Dilsad OF, Ergin E, Attar N, Gurgan S. Comparison of laser-and bur-prepared class I cavities restored with two different low-shrinkage composite resins: a randomized, controlled 60-month clinical trial. Clinical oral investigations. 2020;24(1):357-68. 3. Gurgan S, Vural UK, Kutuk ZB, Cakir FY. Does a new formula have an input in the clinical success of posterior composite restorations? A chat study. Clinical oral investigations. 2021;25(4):1715-27. 4. Loguercio A, Rezende M, Gutierrez M, Costa T, Armas-Vega A, Reis A. Randomized 36-month follow-up of posterior bulk-filled resin composite restorations. Journal of dentistry. 2019;85:93-102. 5. Kitasako Y, Sadr A, Burrow M, Tagami J. Thirty‐six month clinical evaluation of a highly filled flowable composite for direct posterior restorations. Australian dental journal. 2016;61(3):366-73. 6. Celik EU, Tunac AT, Yilmaz F. Three-year clinical evaluation of high-viscosity glass ionomer restorations in non-carious cervical lesions: a randomised controlled split-mouth clinical trial. Clinical oral investigations. 2019;23(3):1473-80. 7. Cieplik F, Scholz KJ, Tabenski I, May S, Hiller K-A, Schmalz G, et al. Flowable composites for restoration of non-carious cervical lesions: Results after five years. Dental materials. 2017;33(12):428-37. 8. Favetti M, Schroeder T, Montagner AF, Correa MB, Pereira-Cenci T, Cenci MS. Effectiveness of pre-treatment with chlorhexidine in restoration retention: a 36-month follow-up randomized clinical trial. Journal of dentistry. 2017;60:44-9.
DOI: 10.17632/wnbjgf96nw.1
title: Response of shallow lakes in the arid-semiarid Pampas of Argentina to Late Holocene hydroclimatic change

description: This dataset contains raw information used in the paper 'Response of shallow lakes in the arid-semiarid Pampas of Argentina to Late Holocene hydroclimatic change' to build figures and diagrams. The dataset includes diatoms and phytoliths counting and percentage, main grain size fractions, and PCoA scores of four shallow lakes located in the Pampean region, Argentina. This research aims to identify the main drivers and mechanisms of long-term ecological change; it also attempts to identify the main climatic mechanisms behind the regional hydroclimatic variability. It was found that these lakes were initially temporary, with clear waters, and eventually, became permanent with turbid waters; we propose that the reason behind the change is an increase in precipitation that promoted an increase in water levels, lake expansion increasing lakes' fetch, and resuspension of the sediment.
DOI: 10.17632/9scn37td2p.4
title: Dataset on optimization and development of a point-of-care glucometer-based SARS-CoV2 detection assay by using aptamers

description: The paper presents the Supplementary Data of the published research work entitled “Hitting the diagnostic sweet spot: Point-of-care SARS-CoV-2 salivary antigen testing with an off-the-shelf glucometer” (Singh et al., 2021, Biosensor and Bioelectronics) . The assay is designed to be performed at home or a healthcare center without the requirements of expensive instruments or operational training. The virus is detected by an aptamer-based assay that specifically targets SARS CoV-2 Nucleocapsid (N) or Spike (S) proteins. The binding of N or S proteins by their respective aptamers results in the competitive release of a complementary antisense-invertase enzyme complex from the aptamers. The released enzyme then catalyzes the conversion of sucrose to glucose that is measured by a glucometer. The data presented here describe the optimization of the assay parameters and their contribution in the development of the aptamer-based assay for the detection of SARS CoV-2. The assay performance was checked in standard buffer, contrived, and real patient samples, and validated with well-established scientific methods. The resulting dataset can be used to further develop the glucometer-based point-of-care system for diagnosing other communicable and non-communicable diseases
DOI: 10.17632/nxmx5w8y63.1
title: Reservoir Inflow and SWE

description: Historical collection of snow water equivalent (SWE) data from National Resource Conservation Service (NRCS) snow telemetry monitoring stations and calculated reservoir inflow for Upper Stillwater reservoir from the Central Utah Water Conservancy District (CUWCD). SWE is measured in units of inches (in) and reservoir inflow is calculated in units of cubic feet per second (cfs). Daily reservoir inflow is calculated from CUWCD Supervisory Control and Data Aquisition (SCADA) data considering the change in reservoir elevation to determine change in storage volume compared to monitored losses (e.g. seepage and evaporation) and releases (e.g. Rock Creek and Stillwater Tunnel).
DOI: 10.34810/data72/530
title: 02AC_1.MR.0009.0020.2013.03.10.09.04.41.296875.165173676.IMA
DOI: 10.5281/zenodo.5085871
title: Dataset MTrill project

description: This dataset includes data collected from 30 human subjects (anonymised) taking part in a syntactic priming experiment. All speakers of Brazilian Portuguese who were exposed to sentences with a relation of possession between nouns in Portuguese (e.g. A porta do quarto esta fechada) and were asked to freely translate them into English. We called this phase as "baseline phase" (Column "test" level Baseline). Following this phase, participants were requested to translate other sentences with the same structure using Google Translate, read the output of Google Translate out loud and, immediately after reading the output, participants were requested to describe images in English using words appearing on the screen of the computer- We called this phase as "priming" phase. (Column "test" level priming). All the sentences presented to participants could have been translated by them or by Google using a prepositional noun phrase (PNP) (e.g. the door of the room is open) or as a noun phrase (NP) (e.g. the room door is open) If participants used the same structure seen in the output of Google Translate (always noun phrase structure) when describing the images, then we considered participants were primed (column "prime" level "1") otherwise not primed (column "prime" level "0"). Column named "cumulativity" contains the proportion of noun phrase sentences out of the total sentences produced on the target trials before the current trial. Column named "EnglishTestGrade" contains participants' grades to an English Online Test The grades reflect participants' English proficiency level. Column named "structures" contain the structure of the sentences produced at the target trials (PNP or other and NP structures)
DOI: 10.17632/p46dwfyvzg.1
title: Agent-based models predict emergent behavior of heterogeneous cell populations in dynamic microenvironments: POPULATION HETEROGENEITY (5/5)

description: Data and results for `POPULATION HETEROGENEITY` simulations. Simulations of all possible combinations of four populations (X, A, B, C) under colony and tissue context with different levels of parameter heterogeneity (0, 0.1, 0.2, 0.3, 0.4, and 0.5). For tissue context, the healthy cell populations also varies parameter heterogeneity independent of the cancerous cell populations. Each condition is run for 15 days (21600 ticks) with 20 replicates (random seeds 0 - 19). Cells are introduced to the center of the constant source environment after a 1 day delay. Snapshots are taken every 0.5 days (720 ticks). The data folder contains .tar.xz compressed replicate sets. The data.param folder contains .tar.xz compressed replicate sets for .PARAM output. The results folder contains .pkl files of data parsed into arrays. Simulations are labeled as: [context]_[populations]_[population heterogeneity]_[background heterogeneity] - [context] - C = colony context, cancerous cell populations only - CH = tissue context, cancerous cell populations and healthy cells - [populations] - X = cancerous cell population with basal parameters - A = cancerous cell population with max_height x 1.5 - B = cancerous cell population with meta_pref x 1.5 - C = cancerous cell population with migra_threshold x 0.5 - [population heterogeneity] - ## = heterogeneity value for cancerous cell populations (##/10) - [background heterogeneity] - ## = heterogeneity value for healthy cell population (##/10) Additional data and results for this simulation set available at: - doi: 10.17632/bk9s769t3t - doi: 10.17632/mjpcm5my8z - doi: 10.17632/hskf9mxbpw - doi: 10.17632/ynzsfdswjz
DOI: 10.14457/cu.the.2008.2114
title: การศึกษาการใช้โปรแกรมการพยาบาลแบบองค์รวมต่อความสามารถในการดูแลตนเองของผู้ป่วยจิตเภทสูงอายุ สถาบันจิตเวชศาสตร์สมเด็จเจ้าพระยา
DOI: 10.34810/data72/531
title: 02AC_1.MR.0009.0239.2013.03.10.09.04.41.296875.165177625.IMA
DOI: 10.17632/ynzsfdswjz.1
title: Agent-based models predict emergent behavior of heterogeneous cell populations in dynamic microenvironments: POPULATION HETEROGENEITY (4/5)

description: Data and results for `POPULATION HETEROGENEITY` simulations. Simulations of all possible combinations of four populations (X, A, B, C) under colony and tissue context with different levels of parameter heterogeneity (0, 0.1, 0.2, 0.3, 0.4, and 0.5). For tissue context, the healthy cell populations also varies parameter heterogeneity independent of the cancerous cell populations. Each condition is run for 15 days (21600 ticks) with 20 replicates (random seeds 0 - 19). Cells are introduced to the center of the constant source environment after a 1 day delay. Snapshots are taken every 0.5 days (720 ticks). The data folder contains .tar.xz compressed replicate sets. The data.param folder contains .tar.xz compressed replicate sets for .PARAM output. The results folder contains .pkl files of data parsed into arrays. Simulations are labeled as: [context]_[populations]_[population heterogeneity]_[background heterogeneity] - [context] - C = colony context, cancerous cell populations only - CH = tissue context, cancerous cell populations and healthy cells - [populations] - X = cancerous cell population with basal parameters - A = cancerous cell population with max_height x 1.5 - B = cancerous cell population with meta_pref x 1.5 - C = cancerous cell population with migra_threshold x 0.5 - [population heterogeneity] - ## = heterogeneity value for cancerous cell populations (##/10) - [background heterogeneity] - ## = heterogeneity value for healthy cell population (##/10) Additional data and results for this simulation set available at: - doi: 10.17632/bk9s769t3t - doi: 10.17632/mjpcm5my8z - doi: 10.17632/hskf9mxbpw - doi: 10.17632/p46dwfyvzg
DOI: 10.17632/hskf9mxbpw.1
title: Agent-based models predict emergent behavior of heterogeneous cell populations in dynamic microenvironments: POPULATION HETEROGENEITY (3/5)

description: Data and results for `POPULATION HETEROGENEITY` simulations. Simulations of all possible combinations of four populations (X, A, B, C) under colony and tissue context with different levels of parameter heterogeneity (0, 0.1, 0.2, 0.3, 0.4, and 0.5). For tissue context, the healthy cell populations also varies parameter heterogeneity independent of the cancerous cell populations. Each condition is run for 15 days (21600 ticks) with 20 replicates (random seeds 0 - 19). Cells are introduced to the center of the constant source environment after a 1 day delay. Snapshots are taken every 0.5 days (720 ticks). The data folder contains .tar.xz compressed replicate sets. The data.param folder contains .tar.xz compressed replicate sets for .PARAM output. The results folder contains .pkl files of data parsed into arrays. Simulations are labeled as: [context]_[populations]_[population heterogeneity]_[background heterogeneity] - [context] - C = colony context, cancerous cell populations only - CH = tissue context, cancerous cell populations and healthy cells - [populations] - X = cancerous cell population with basal parameters - A = cancerous cell population with max_height x 1.5 - B = cancerous cell population with meta_pref x 1.5 - C = cancerous cell population with migra_threshold x 0.5 - [population heterogeneity] - ## = heterogeneity value for cancerous cell populations (##/10) - [background heterogeneity] - ## = heterogeneity value for healthy cell population (##/10) Additional data and results for this simulation set available at: - doi: 10.17632/bk9s769t3t - doi: 10.17632/mjpcm5my8z - doi: 10.17632/ynzsfdswjz - doi: 10.17632/p46dwfyvzg
DOI: 10.34810/data72/532
title: 02AC_1.MR.0009.0162.2013.03.10.09.04.41.296875.165176232.IMA
DOI: 10.48370/ofd/cdm6bv/cy2cbt
title: HMW_Cerasus_avium_(L.)_Moench_H_2278.jpg

description: :unav
DOI: 10.17632/mjpcm5my8z.1
title: Agent-based models predict emergent behavior of heterogeneous cell populations in dynamic microenvironments: POPULATION HETEROGENEITY (2/5)

description: Data and results for `POPULATION HETEROGENEITY` simulations. Simulations of all possible combinations of four populations (X, A, B, C) under colony and tissue context with different levels of parameter heterogeneity (0, 0.1, 0.2, 0.3, 0.4, and 0.5). For tissue context, the healthy cell populations also varies parameter heterogeneity independent of the cancerous cell populations. Each condition is run for 15 days (21600 ticks) with 20 replicates (random seeds 0 - 19). Cells are introduced to the center of the constant source environment after a 1 day delay. Snapshots are taken every 0.5 days (720 ticks). The data folder contains .tar.xz compressed replicate sets. The data.param folder contains .tar.xz compressed replicate sets for .PARAM output. The results folder contains .pkl files of data parsed into arrays. Simulations are labeled as: [context]_[populations]_[population heterogeneity]_[background heterogeneity] - [context] - C = colony context, cancerous cell populations only - CH = tissue context, cancerous cell populations and healthy cells - [populations] - X = cancerous cell population with basal parameters - A = cancerous cell population with max_height x 1.5 - B = cancerous cell population with meta_pref x 1.5 - C = cancerous cell population with migra_threshold x 0.5 - [population heterogeneity] - ## = heterogeneity value for cancerous cell populations (##/10) - [background heterogeneity] - ## = heterogeneity value for healthy cell population (##/10) Additional data and results for this simulation set available at: - doi: 10.17632/bk9s769t3t - doi: 10.17632/hskf9mxbpw - doi: 10.17632/ynzsfdswjz - doi: 10.17632/p46dwfyvzg
DOI: 10.48370/ofd/cdm6bv
title: Wiśnia ptasia (Cerasus avium (L.) Moench), 2278

description: Herbarium
DOI: 10.48370/ofd/fvrytd/f7svrj
title: HMW_Cerasus_avium_(L.)_Moench_H_1687.jpg

description: :unav
DOI: 10.48370/ofd/fvrytd
title: Wiśnia ptasia (Cerasus avium (L.) Moench), 1687

description: Herbarium
DOI: 10.17632/dbj46mw6j9.1
title: Agent-based models predict emergent behavior of heterogeneous cell populations in dynamic microenvironments: CELL COMPETITION (3/3)

description: Data and results for `CELL COMPETITION` simulations. Simulations of a modified population with one of three parameters (crowding tolerance, metabolic preference, migratory threshold) varied +/- 10, 20, 30, 40, and 50% from baseline values with an unmodified population in varying fractions (0.0 to 1.0 in increments of 0.1). Each condition is run for 14 days (20160 ticks) with 20 replicates (random seeds 0 - 19). Cells are introduced to the center of the constant source environment at the start of the simulation. Snapshots are taken every 0.5 days (720 ticks). The data folder contains .tar.xz compressed replicate sets. The results folder contains .pkl files of data parsed into arrays. Simulations are labeled as: [parameter]_[percent]_[unmodified fraction]_[modified fraction] - [parameter] - max_height = crowding tolerance parameter - meta_pref = metabolic preference parameter - migra_threshold = migratory threshold parameter - [percent] - ### = percent change in parameter from baseline denoted (### - 100)% - [unmodified fraction] - ### = fraction of unmodified population (###/100) - [modified fraction] - ### = fraction of modified population (###/100) Additional data and results for this simulation set available at: - doi: 10.17632/5h6ng2y4fc - doi: 10.17632/gzfwhgdtwz
DOI: 10.34810/data72/533
title: 02AC_1.MR.0009.0072.2013.03.10.09.04.41.296875.165174612.IMA
DOI: 10.17632/gzfwhgdtwz.1
title: Agent-based models predict emergent behavior of heterogeneous cell populations in dynamic microenvironments: CELL COMPETITION (2/3)

description: Data and results for `CELL COMPETITION` simulations. Simulations of a modified population with one of three parameters (crowding tolerance, metabolic preference, migratory threshold) varied +/- 10, 20, 30, 40, and 50% from baseline values with an unmodified population in varying fractions (0.0 to 1.0 in increments of 0.1). Each condition is run for 14 days (20160 ticks) with 20 replicates (random seeds 0 - 19). Cells are introduced to the center of the constant source environment at the start of the simulation. Snapshots are taken every 0.5 days (720 ticks). The data folder contains .tar.xz compressed replicate sets. The results folder contains .pkl files of data parsed into arrays. Simulations are labeled as: [parameter]_[percent]_[unmodified fraction]_[modified fraction] - [parameter] - max_height = crowding tolerance parameter - meta_pref = metabolic preference parameter - migra_threshold = migratory threshold parameter - [percent] - ### = percent change in parameter from baseline denoted (### - 100)% - [unmodified fraction] - ### = fraction of unmodified population (###/100) - [modified fraction] - ### = fraction of modified population (###/100) Additional data and results for this simulation set available at: - doi: 10.17632/5h6ng2y4fc - doi: 10.17632/dbj46mw6j9