Find research datasets worth reusing

opencc-by-4.0May 2019View details →

Raw Data supporting Promoters adopt distinct dynamic manifestations depending on transcription factor context

All source data is provided as MAT-files, which can be opened in Matlab. In total, the source data contain 270 core data files and 540 processed data files. The data for each individual promoter is stored in a different directory and the 9 promoters are: <ul> <li> ALD3 </li> <li> DCS2 </li> <li> DDR2 </li> <li> HXK1 </li> <li> RTN2 </li> <li> TKL2 </li> <li> SIP18 </li> <li> pSIP18_mut6 (also referred to as mutant A4) </li> <li> pSIP18_mut21 (also referred to as mutant D6) </li> </ul> The data for the first 7 promoters was previously reported in (Hansen and O’Shea, 2013), though in an unnormalized form. That is, it was previously reported as a concentration per cell in arbitrary fluorescence units (AU). In the present manuscript, we have calibrated the data to obtain absolute abundances, such that the MAT-files now contain both the old AU concentration as well as absolute abundances, i.e. number of YFP molecules per cell. The calibration was performed as described in (Huang et al., 2016). Similarly, the data for the last 2 promoters (A4 and D6) was previously reported in its unnormalized form in (Hansen and O’Shea, 2015) and it is here also reported in the form of absolute abundances.   The MAT-files containing the raw data have the suffix “_size.mat”. The name of the MAT files describes the experiment. If the file name contains “DM”, then it is a single pulse. Thus, “SIP18_DM_40min_275nM_size.mat” refers to a single 40 min pulse with 275 nM 1-NM-PP1 for the SIP18 promoter. Similarly if the file name contains “FM”, e.g. “RTN2_FM_8_5min_690nM_size.mat” then it refers to eight 5 min pulses separated by 5 min intervals at 690 nM for the RTN2 promoter. Finally, if the file name contains “FM4”, e.g.  “TKL2_FM4_15minINT_690nM_size.mat” then the experiment was four 5 min pulses separated by 15 min intervals at 690 nM for the TKL2 promoter. The concentration is the concentration of 1-NM-PP1 that was used and 100 nM, 275 nM, 690 nM and 3mM refers to approximately, 25%, 50%, 75% and 100% Msn2 activation. For full experimental details please see (Hansen and O’Shea, 2013; Hansen et al., 2015).   The “_size.mat” MAT-files contain the following variables: <ul> <li> cell_size_pixels</li> <li> CFP</li> <li> CFP_molecules</li> <li> CFP_raw</li> <li> inhibitor_conc</li> <li> MSN2_raw</li> <li> MSN2_RFP</li> <li> pulse_parameters • time</li> <li> YFP</li> <li> YFP_molecules</li> <li> YFP_raw</li> </ul> CFP, CFP_molecules, CFP_raw and YFP, YFP_molecules, YFP_raw are Nx64 matrices, where each row N correspond to a different cell and the 64 columns correspond to the 64 experimentally measured timepoints corresponding to the “time” vector running from -5 min to 152.5 min in increments of 2.5 min and the 1NM-PP1 inhibitor was added at time 0. “CFP_raw” and “YFP_raw” contains raw, uncorrected data, so without photobleaching correction and background subtraction. “CFP” and “YFP” contain corrected data in arbitrary fluorescence units (AU) and report on the concentration (i.e. size normalized). Finally, “CFP_molecules” and “YFP_molecules” contains the total number of CFP and YFP molecules per cell (i.e. this is not a concentration, but the absolute abundance). The area of each cell at each timepoint can be found in the matrix “cell_size_pixels”. Since the cells are live and growing, this will tend to increase during the experiments. Occasionally large fluctuations can occur due to errors in cell segmentation or due to division. For full details on the image analysis and cell segmentation, please see (Hansen and O’Shea, 2013; Hansen et al., 2015).   The variables “inhibitor_conc” and “pulse_parameters” refer to the type of experiment and is also given by the name. “inhibitor_conc”  gives the 1NMPP1 concentration: 100 nM, 275 nM, 690 nM or 3000 nM. “pulse_parameters” contains either 2 or 3 elements and given the dynamical pulse sequence parameters. Column 1 contains the number of pulses and column 2 the duration of the pulses. Column 3 gives the interval between the pulses if more than one pulse is used – otherwise column 3 is zero.   Moreover, on a more technical note it should be noted that the signal-to-noise of the CFP reporter is worse than the YFP reporter. Therefore, we always use the YFP reporter for quantitative analysis. Furthermore, the two other MAT-files “…MSN2.mat” and “…YFP.mat” contain processed data. Please see the ReadMe file on the code for a full description and how these were derived.   Finally, Supplementary Table 1 contains the model-inferred parameters for each promoter and condition.     References Hansen, A.S., and O’Shea, E.K. (2013). Promoter decoding of transcription factor dynamics involves a trade-off between noise and control of gene expression. Mol. Syst. Biol. Hansen, A.S., and O’Shea, E.K. (2015). Cis Determinants of Promoter Threshold and Activation Timescale. Cell Rep. Hansen, A.S., Hao, N., and OShea, E.K. (2015). High-throughput microfluidics to control and measure signaling dynamics in single yeast cells. Nat. Protoc. Huang, L., Pauleve, L., Zechner, C., Unger, M., Hansen, A.S., and Koeppl, H. (2016). Reconstructing dynamic molecular states from single-cell time series. J. R. Soc. Interface.

dryad36/100

Average cover crop adoption rates in the U.S. Midwest in 2000-2010 and 2011-2021

Cover crops have critical significance for agroecosystem sustainability and have long been promoted in the U.S. Midwest. Knowledge of the variations of cover cropping and the impacts of government policies remains very limited. We developed an accurate and cost-effective approach utilizing multi-source satellite fusion data, environmental variables, and machine learning to quantify cover cropping in corn and soybean fields from 2000 to 2021 in the U.S. Midwest. We found that cover crop adoption in most counties has significantly increased in the recent 11 years from 2011 to 2021. The adoption percentage of 2021 is 3.3 times that of 2011, which was highly correlated to the increased funding for federal and state conservation programs. However, the percentage of cover crop adoption is still low (7.2%). The averaged county-level cover crop adoption rates in 2000-2010 and 2011-2021 are publicly available on Dryad.

opencc-zeroNov 2022View details →

opencc-by-4.0Nov 2022View details →

Investigating the Adoption of Research Software: A Survey with Brazilian Academic Researchers

Artifacts used for data collection and analysis.

opencc-by-4.0Feb 2023View details →

Raw Data for the article: Impact of the Organizational Model Adopted during the COVID-19 Pandemic on the Perceived Safety of Intensive Care Unit Staff

Background: The SARS-CoV-2 pandemic had a devastating health, social, and economic effect on the population. Organizational, technical and structural operations aimed at protecting staff, outpatients and inpatients were implemented in an Italian hospital with a COVID-19 dedicated intensive care unit. The impact of the organizational model adopted on the perceived safety among staff was evaluated. Methods: Descriptive, structured and voluntary, anonymous, non-funded, self-administered cross-sectional surveys on the impact of the organizational model adopted during COVID-19 on the perceived safety among staff. Results: Response rate to the survey was 67.4% (153 completed surveys). A total of 91 (59%) of respondents had more than three years of ICU experience, while 16 (10%) were employed for less than one year. Group stratification according to profession: 74 nurses (48%); 12 medical-doctors (7%); 11 physiotherapists (7%); 35 nurses-aides (22%); 5 radiology-technicians (3%); 3 housekeeping (1%); 13 other (8%). The organizational model implemented at ISMETT made them feel safe during their workday. A total of 113 (84%) agreed or strongly agreed with the sense of security resulting from the implemented measures. A vast majority of respondents perceived COVID-19 as a dangerous and deadly disease (94%) not only for themselves but even more as vectors towards their families (79%). A total of 55% of staff took isolation measures and moved away from their home by changing personal habits. The organizational model was perceived overall as appropriate (91%) to guarantee their health. Conclusion: The vast majority of respondents perceived the overall model applied during an unexpected, emergency situation as appropriate.

opencc-by-4.0Feb 2023View details →

Adoptive Immune Transfer from Donors Offers Anti-HBV Protection to HBsAb Negative Patients After Allo-HSCT

Original data of the manuscript of "Adoptive Immune Transfer from Donors Offers Anti-HBV Protection to HBsAb Negative Patients After Allo-HSCT " which published in iScience.

dryad36/100

Factors influencing open government data post-adoption in the public sector: The perspective of data providers

Providing access to non-confidential government data to the public is one of the initiatives adopted by many governments today to embrace government transparency practices. The initiative of publishing non-confidential government data for the public to use and re-use without restrictions is known as Open Government Data (OGD). Nevertheless, after several years after its inception, the direction of OGD implementation remains uncertain. The extant literature on OGD adoption concentrates primarily on identifying factors influencing adoption decisions. Yet, studies on the underlying factors influencing OGD after the adoption phase are scarce. Based on these issues, this study investigated the post-adoption of OGD in the public sector, particularly the data provider agencies. The OGD post-adoption framework is crafted by anchoring the Technology–Organization–Environment (TOE) framework and the innovation adoption process theory. The data was collected from 266 government agencies in the Malaysian public sector. This study employed the partial least square-structural equation modeling as the statistical technique for factor analysis. The results indicate that two factors from the organizational context (top management support, organizational culture) and two from the technological context (complexity, relative advantage) have a significant contribution to the post-adoption of OGD in the public sector. The contribution of this study is threefold: theoretical, conceptual, and practical. This study contributed theoretically by introducing the post-adoption framework of OGD that comprises the acceptance, routinization, and infusion stages. As the majority of OGD adoption studies conclude their analysis at the adoption (decisions) phase, this study gives novel insight to extend the analysis into unexplored territory, specifically the post-adoption phase. Conceptually, this study presents two new factors in the environmental context to be explored in the OGD adoption study, namely, the data demand and incentives. The fact that data providers are not influenced by data requests from the agency's external environment and incentive offerings is something that needs further investigation. In practicality, the findings of this study are anticipated to assist policymakers in strategizing for long-term OGD implementation from the data provider's perspective. This effort is crucial to ensure that the OGD initiatives will be incorporated into the public sector's service thrust and become one of the digital government services provided to the citizen.

opencc-zeroFeb 2023View details →

opencc-by-4.0Jul 2023View details →

Managerial Adoption in E-fulfillment Services Using UTAUT Theory Approach in Indonesia

This is the dataset related to the research topic: managerial adoption of E-fulfillment services in Indonesia using UTAUT approach

opencc-by-4.0Aug 2023View details →

EnergyPROSPECTS Energy Citizenship Factsheet Series, Part 6: Aspects of ENCI II.: Frontrunners and late adopters, pragmatic and transformative ENCI

This document is Part 6 of the EnergyPROSPECTS Factsheet Series. We have created the Series to publish the results of a mapping of energy citizenship in Europe, along with the first stage of our analysis of the respective data. The EnergyPROSPECTS consortium mapped 596 cases of energy citizenship (ENCI) between November 2020 and May 2021 using desk research, collecting data on many aspects of the cases. Although the analysis is a work in progress, we believe it is important to share our data and, through doing this, contribute to the understanding of energy citizenship in Europe. EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), a H2020 project between 2021-2024, works with a critical understanding of energy citizenship that is grounded in state-of-the-art social sciences and humanities (SSH) insights.

opencc-by-4.0Dec 2024View details →

2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics

This dataset is the second release of data for the <a href="https://www.gecad.isep.ipp.pt/ERM-competitions/2025-energy-forecast/">2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics</a> The competition is open and welcomes everyone who wishes to participate and to anyone who can benefit from these data. <h2>Competition Outline</h2> Forecasting of electric energy consumption can be a very difficult tasks when handling building-level data. However, an accurate forecast is needed to boost the potential of energy management systems. The need to forecast energy consumption grows as our reliance on renewable energy sources, such as solar and wind power, grows. This means that to meet consumer demand with renewable energy generation, energy management systems must operate based on accurate energy forecasting models for both short and long-term periods. Energy consumption forecasting techniques that can manage a variety of scenarios, including varying prediction timeframes, accessible data, data frequency, and even data quality, have been the subject of intense research. There is no one-size-fits-all approach, where certain situations call for different approaches. The goal of this competition is to compile and evaluate the most recent advances in energy consumption forecasting techniques.   <h2>Releases Details</h2> <ul> <li>v1.0: one year of data from a smart building with readings taken every 5 minutes.</li> <li>v2.0: 40 days of data from a smart building with readings taken every 5 minutes.</li> <li>v3.x: a single day of data from a smart building with readings taken every hour. These releases will become available during the first competition period (from 06/01/2025 to 10/01/2025).</li> <li>v4.x: a single day of data from a smart building with readings taken every hour. These releases will become available during the second competition period (from 14/07/2025 to 18/07/2025).</li> </ul>   <h2>Dataset Description</h2> All releases are composed of the following data: <ul> <li>Time: in hours and minutes</li> <li>Power: in Watts</li> <li>Voltage: in Volts</li> <li>Current: in Ampers</li> <li>Generation power: in Watts</li> <li>Temperature: in ºC</li> </ul>

Individual Counseling and/or Computer-Based Counseling in Helping Healthy Women Adopt a Cancer Prevention Diet

ClinicalTrials.gov study NCT00217490. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Study of Adoptive Transfer of iNKT Cells Combined With TAE/TACE to Treat Unresectable HCC

ClinicalTrials.gov study NCT04011033. IPD Sharing: NO. Countries: 1. Publications: 1.

Cyclophosphamide, Fludarabine, and Total-Body Irradiation Followed By Cellular Adoptive Immunotherapy, Autologous Stem Cell Transplantation, and Interleukin-2 in Treating Patients With Metastatic Mela

ClinicalTrials.gov study NCT00096382. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Peers Promoting Exercise Adoption and Maintenance Among Cancer Survivors

ClinicalTrials.gov study NCT02694640. IPD Sharing: NO. Countries: 1. Publications: 3.

Overcoming Barriers and Obstacles to Adopting Diabetes Devices

ClinicalTrials.gov study NCT04161131. IPD Sharing: NO. Countries: 1. Publications: 2.

controlledIPD-YESFeb 2026View details →

An Implementation Strategy for the Adoption of an Evidence-Based Guideline for Pit-and-Fissure Sealants

ClinicalTrials.gov study NCT04682730. IPD Sharing: YES. Countries: 1. Publications: 5.

Evaluation of the Usefulness of Adopting Remote, Mobile-based 6MWT Among Hospital Outpatients (the 6-APPnow), Within the Constraints Imposed by the SARS-COV2 Pandemic

ClinicalTrials.gov study NCT05096819. IPD Sharing: UNDECIDED. Countries: 1. Publications: 9.

IPA Targeted Adoptive Immunotherapy vs Adult Haplo-identical Cell Infusion During Induction of High Risk Leukemia

ClinicalTrials.gov study NCT02508324. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Nudging Provider Adoption of Clinical Decision Support

ClinicalTrials.gov study NCT05203185. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Cytokine Induced Memory-like NK Cell Adoptive Therapy for Relapsed AML After Allogeneic Hematopoietic Cell Transplant

ClinicalTrials.gov study NCT03068819. IPD Sharing: NO. Countries: 1. Publications: 1.

Cytokine Induced Memory-like NK Cell Adoptive Therapy After Haploidentical Donor Hematopoietic Cell Transplantation

ClinicalTrials.gov study NCT02782546. IPD Sharing: NO. Countries: 1. Publications: 1.