Find research datasets worth reusing

opencc-by-4.0Jul 2024View details →

data_set for "Time-resolved sensing of electromagnetic fields with single-electron interferometry"

Open the record for dataset details and reuse information.

opencc-by-4.0Feb 2024View details →

Data from "The relevance of flash coloration against avian predation in a Morpho butterfly: A field experiment in a tropical rainforest"

This dataset contains all the data related to the manuscript "The relevance of flash coloration against avian predation in a Morpho butterfly: A field experiment in a tropical rainforest".

opencc-by-4.0Oct 2024View details →

Accompanying data for the paper "Experimental characterization of material strain-rate dependence based on full-field Data-Driven Identification"

Experimental Data accompanying the paper "Experimental characterization of material strain-rate dependence based on full-field Data-Driven Identification" <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ijimpeng.2024.105083" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ijimpeng.2024.105083</a> One (holed and double notched) specimen is dynamically loaded via an hydraulic tensile test machine (MTS-819, 20 kN) at 5 m/s. The specimen were cut from a 0.8 mm-thick DC04 (XES French standards) sheet in the rolling direction provided by ONERA. Reference image is captured using a high definition camera (29 Mpix, Prosilica GT from Stemmer) combined with the same objective lens than the one used for experiments. Deformed sample images are captured using the rotating mirror Ultra-high speed (HR-UHS) Cordin camera Model 580 at 68 kfps with a resolution of 3296 x 2472 pixels. The field of view is 35.8mm x 47.9mm leading to a pixel size of 14.49um. Ufreckles <a href="https://doi.org/10.5281/zenodo.1433776">10.5281/zenodo.1433775</a> is used to perform FE-based DIC using T3P1 linear triangular elements and a Tikhonov regularisation (over 3 elements). Eventually, kinematic data and load measurement  are used to identify stress fields via Data-Driven stress Identification (DDI) method. Are provided: <ul> <li>raw images, camera distortion modes and parameters, load net force and timeline</li> <li>kinematic fields obtained from Digital Image Correlation</li> <li>Stress fields identified using Data-Driven stress Identification</li> </ul> Matlab Codes to produce results (working with Ufreckles) <ul> <li>MultiSensor_DIC_script.m: in /Codes/ is the main script to run DIC</li> <li>Shape functions: in /Codes/shape_functions/ containing Zernike polynomial shape functions and deconvolution algorithm to get effective displacement from total displacement knowing camera distortions</li> </ul>

opencc-zeroAug 2024View details →

The data for Prediction of Large Solar Flares Based on SHARP and HED Magnetic Field Parameters

The repository includes HED and SHARP Datasets, along with descriptions of the data processing procedures, and the experimental code utilized in our study. The files named "HED parameters.csv" and "SHARP parameters.csv" represent the raw data of HED and SHARP Datasets, respectively. Each dataset consists of 286 ARs, specifically, each dataset includes 189 C-class flaring ARs, 86 M-class flaring ARs, and 11 X-class flaring ARs. The column name "AR" represents the NOAA AR numbers, the column name "time" represents the time at which the AR samples were acquired, and the column name "level" represents the flare class of the AR. Moreover, in the HED dataset, the column name "energy" represents the "E_{free}", "shear_mean" represents the "\Psi", "uj_mean" represents the "J_{Z}", "uhc_mean" represents the "H_{C}", "GBH_MEAN" represents the "B_{h}", and "ALP_MEAN" represents the "\alpha". In the SHARP dataset, the column names of the other columns correspond to the names of various parameters. The files named "SHARP_CV.zip" and "HED_CV.zip" represent the ten cross-validation sets that have been normalized and divided using an AR-based cross-validation method. The data processing procedures are as follows. Since the SHARP and HED parameters have distinct scales and units, they are individually normalized using mean-standard deviation. Subsequently, we set the labels for C-class flaring ARs to the negative class which are equal to 0, and the labels for M/X-class flaring ARs to positive class which are equal to 1. We employ an AR-based cross-validation (CV) method to partition the SHARP and HED datasets with the distribution of the training, validation, and testing sets at a ratio of 60%, 20%, and 20%, respectively. This process is repeated ten times, resulting in ten cross-validation sets in both SHARP datasets (SHARP_CV) and HED datasets (HED_CV), respectively. The column named "level" in the SHARP_CV data and the column named "key" in the HED_CV data represent the labels for the flare classes of the ARs. The file named "Parameter description and formula of SHARP and HED as well as data division process.pdf" represents the description and formula of SHARP and HED parameters, as well as the procedure for generating 10-fold cross-validation set divisions for the SHARP/HED datasets. We design five solar flare prediction models leveraging five currently popular deep learning algorithms: Transformer, BiLSTM-Attention, BiLSTM, LSTM-Attention, and LSTM. Moreover, we use the NN model as the baseline model to compare with other deep learning model. The solar flare prediction model used in this paper can be accessed via https://drive.google.com/drive/folders/1n6fXcQdCBogKt6L0aaPFagraFcXmX0fw?usp=drive_link.

opencc-by-4.0Nov 2024View details →

Data for Simulating strong-field electron-hole dynamics in solids probed by attosecond transient absorption spectroscopy

These are the source data for Simulating strong-field electron-hole dynamics in solids probed by attosecond transient absorption spectroscopy. A preprint is available at: https://arxiv.org/abs/2409.01033. A readme.txt file is included with the data.

opencc-zeroDec 2017View details →

Data from: Evaluating consumptive and nonconsumptive predator effects on prey density using field times series data

Determining the degree to which predation affects prey abundance in natural communities constitutes a key goal of ecological research. Predators can affect prey through both consumptive effects (CEs) and nonconsumptive effects (NCEs), although the contributions of each mechanism to the density of prey populations remain largely hypothetical in most systems. Common statistical methods applied to time series data cannot elucidate the mechanisms responsible for hypothesized predator effects on prey density (e.g., differentiate CEs from NCEs), nor provide parameters for predictive models. State space models (SSMs) applied to time series data offer a way to meet these goals. Here, we employ SSMs to assess effects of an invasive predatory zooplankter, Bythotrephes longimanus, on an important prey species, Daphnia mendotae, in Lake Michigan. We fit mechanistic models in a SSM framework to seasonal time series (1994-2012) using a recently developed, maximum likelihood-based optimization method, iterated filtering, which can overcome challenges in ecological data (e.g. nonlinearities, measurement error, and irregular sampling intervals). Our results indicate that B. longimanus strongly influences D. mendotae dynamics, with mean annual peak densities of B. longimanus observed in Lake Michigan estimated to cause a 61% reduction in D. mendotae population growth rate and a 59% reduction in peak biomass density. Further, the mechanism underlying the B. longimanus effect is most consistent with an NCE via reduced birth rates. The SSM approach also provided estimates for key biological parameters (e.g., demographic rates) and the contribution of dynamic stochasticity and measurement error. Our study therefore highlights the utility of SSMs to enhance inference for species interactions from time series data. In particular, our findings provide evidence derived directly from survey data that the invasive zooplankter B. longimanus is affecting zooplankton demographics and offer parameter estimates needed to inform predictive models that explore the effect of B. longimanus under different scenarios such as climate change.

opencc-zeroDec 2018View details →

Data from: Sexual signal loss in field crickets maintained despite strong sexual selection favoring singing males

Evolutionary biologists commonly seek explanations for how selection drives the emergence of novel traits. While trait loss is also predicted to occur frequently, few contemporary examples exist. In Hawaii, the Pacific field cricket (Teleogryllus oceanicus) is undergoing adaptive sexual signal loss due to natural selection imposed by eavesdropping parasitoids. Mutant male crickets ("flatwings") cannot sing. We measured the intensity of sexual selection on wing phenotype in a wild population. First, we surveyed the relative abundance of flatwings and "normal-wings" (non-mutants) on Oahu. Then, we bred wild-mated females' offspring to determine both female genotype with respect to the flatwing mutation and the proportion of flatwing males that sired their offspring. We found evidence of strong sexual selection favoring the production of song: females were predominantly homozygous normal-wing; their offspring were sired disproportionately by singing males; and at the population level, flatwing males became less common following a single sexual selection event. We report a selection coefficient describing the total (pre- and postcopulatory) sexual selection favoring normal-wing males in nature. Given the maintenance of the flatwing phenotype in Hawaii in recent years, this substantial sexual selection additionally suggests an approximate strength of opposing natural selection that favors silent males.

opencc-zeroJun 2021View details →

Data from: Direct and indirect effects of landscape and field management intensity on carabids through trophic resources and weeds

Carabids are important biological control agents of weeds and other pests in agricultural fields. The carabid community is built upon direct and indirect ecological effects of landscape complexity, field management intensity and biotic components that in interaction make any prediction of community size and composition challenging. We analyse a large-scale sample of 60 European cereal fields using Structural Equation Modelling to quantify the direct effects of field management intensity and the surrounding landscape, and their indirect effect via biotic components, on carabid diversity. Our results highlight that direct and indirect effects of increasing landscape complexity, mediated by trophic resources, mainly affect carabids positively. Field management intensity only ever affects carabids through indirect effects that are generally negative, by suppressing standing weeds and weed seeds. Indirect effects on granivore carabid species depended on weed seed availability whereas omnivores depended on the availability of both weed seeds and animal prey. Synthesis and applications: A consideration of both the direct and indirect effects of landscape and field management is necessary for predicting carabid communities and with interactions. These effects, mediated via trophic resources, supports the diversity and abundance of carabid communities and their provision of ecosystem services. Our results show that promoting crop diversity and connectivity to semi-natural habitats will directly enhance carabid communities in farmland by manipulating their migration from source habitats. A reduction in field management intensity will preserve local standing weeds and weed seeds, and indirectly support carabid communities. These local and landscape modifications could contribute to improve the natural regulation of pests and weeds by carabids.

opencc-by-4.0Jun 2021View details →

Field data surveys for D3.5

This dataset includes all the field data collected during several surveys during season 2020 according to what is explained and detailed in D3.5 “Precision agriculture strategies for improving combined stress resilience in tomato”.

opencc-by-4.0Mar 2021View details →

Coherent Electric Field Manipulation of Fe3+-spins in PbTiO3. Open data set

Data supporting figures 3 and 4 of the related publication.

opencc-zeroJul 2021View details →

Data from: Telemetry tails: A practical method for attaching animal-borne devices to small vertebrates in the field

Context. Continued miniaturisation of tracking technology increases its utility in animal applications. However, species morphology often dictates the type of animal-borne device (ABD) that can be used, and how it is attached. The morphology of species within Peramelemorphia preclude them from the standard collar attachment of ABDs for terrestrial mammals. Aims. This paper describes a method for the tail-mount attachment of ABDs, and deployment results for Peramelemorphia across arid, semi-arid, and temperate Australia to (a) test the performance of attachments and ABDs in the field, and, (b) discuss the animal welfare considerations for this attachment method. Methods. Tail-mount attachment of ABDs were field-tested on a total of 80 greater bilbies (Macrotis lagotis), and 14 long-nosed bandicoots (Perameles nasuta). Key results. Time to natural detachment (TTND) was between 2 and 52 days with 65.74% (142 of 216) remaining on until manual removal. For ABDs that were manually removed, attachments were retained for up to 94 days. The method used for tail-mount attachment of ABDs to long-nosed bandicoots resulted in significantly shorter TTND compared to the method used for bilbies, and environmental factors (high temperatures and rainfall) had a negative effect on TTND. Tail-mount attached global positioning system (GPS) sensors collected large quantities of accurate data with a maximum fix success rate of 83.38%. Damage to GPS (antenna breakage and water ingress) during deployment, however, impacted performance. In environments with frequent rainfall and waterlogged soils, the tape on a small proportion (6.25%) of (n = 192) attachments to bilbies caused tail injury. All injuries were resolvable with most requiring minimal to no veterinary intervention. Key conclusions. Attachment longevity can be affected by how the ABD is mounted to the tail, the species, and the deployment environment. The environment can also affect which adhesive tapes are suitable for ABD attachment. However, this method is highly modifiable, practical for field application, and can have long retention times relative to other temporary methods. Implications. This ABD tail-mount attachment method adds another tool to the telemetry tool-kit, with all the benefits of a low-tech, low-cost, passive drop-off type attachment. This method has demonstrated practicality for Peramelemorphia, with potential application to other suitable small vertebrates.

opencc-by-4.0Jul 2021View details →

Field Data associated with study: Investigating the water movements around a shallow shipwreck in Big Tub Harbour of Lake Huron: implications for managing underwater shipwrecks.

Field data (Water temperature, pressure, currents, summary of boat presence data) used in analysis of the study (and paper with the same name) titled: Investigating the water movements around a shallow shipwreck in Big Tub Harbour of Lake Huron: implications for managing underwater shipwrecks.

Data and Code for "Study of Solar Wind and Interplanetary Magnetic Field Features Associated with Geomagnetic Storms: The Cross Wavelet Approach"

These files are the supplementary information, including dataset, codes and plots for the research work entitled "Study of Solar Wind and Interplanetary Magnetic Field Features Associated with Geomagnetic Storms: The Cross Wavelet Approach".

Data for the paper "Magnetic fields with precise quasisymmetry"

This archive contains data and source code used for the paper "Magnetic fields with precise quasisymmetry".

Field Data Associated with Internal Swash Zone Study in Hamilton Harbour, Lake Ontario

Compiled Field data associated with the study: Flood, Bryan, Mathew Wells, Jonathan D. Midwood, Jill Brooks, Yulong Kuai, and Jingzhi Li. "Intense variability of dissolved oxygen and temperature in the internal swash zone of Hamilton Harbour, Lake Ontario." Inland Waters (2021): 1-18. Data is organized by field year, and data type. Metadata is included.

CoronaNetDataScience/corona_tscs: Releasing new data fields in event dataset

Original data collection of PHSM began on March 28, 2020. Since then, governments have implemented a wide variety of PHSM often with increasing nuance (e.g. with regards to the geographic or demographic targets of a given policy). While Version 1.0 of the dataset only released data from questions in the original survey, Version 1.1 releases data from new questions that have been added over the course of first year of data collection. For more information about the additional fields and options added to the dataset, please see our codebook <ol> <li>update_level_var: More detailed information as to what dimension of a policy is being updated (i.e., strengthened or relaxed)</li> <li>pdf_link: Link to PDF of the original source used to document a policy</li> <li>institution_cat: Information as to whether a business or government service is considered essential or non-essential according to the government entity in charge of implementing a given PHSM</li> <li>institution_conditions: Information about what conditions a school, business or government service is allowed to open under (e.g., limited number of people allowed on premises)</li> <li>type_new_admin_coop: Information about the nature of a given cooperative effort if different governments decide to cooperate with each other (e.g. country A cooperates with country B)</li> <li>COVID-19 Vaccines: We have added new questions to capture various dimensions of the global COVID-19 Vaccine rollout including information on:</li> </ol> <ul> <li>The manufacturing firm (type_vac_cat)</li> <li>Whether vaccines are allowed to be mixed and matched (type_vac_mix)</li> <li>The regulatory status of a given COVID-19 vaccine (type vac reg)</li> <li>Information on the type of purchase order for COVID-19vaccines (type_vac_purchase)</li> <li>Information on the overall criteria used for deciding how to administer COVID-19 vaccines (type vac group).</li> <li>Information on the number of priority groups for COVID-19 Vaccine distribution, given that this is the criteria used for deciding how to administer COVID-19 vaccines (type_vac_group_rank)</li> <li>Information as to where COVID-19 vaccines are being administered (type_vac_loc)</li> <li>Information as to who is responsible for the economic cost of a given COVID-19 vaccine shot (type_vac_who_pays)</li> <li>Information as to what entity has been placed in primary charge for the COVID- 19 vaccination process (type_vac_dist_admin)</li> <li>Information as to the monetary resource devoted for a given COVID-19 vaccine policy ( typ_vac_cost_num, type_vac_cost_unit, type_vac_cost_scale, type_vac_gov_perc)</li> <li>Information as the volume of COVID-19 vaccines (e.g. shots) in question for a given COVID-19 vaccine policy (typeva amt_num, type_vac_amt_unit, type_vac_amt_scale, type_vac_amt_gov_perc)</li> </ul> <ol> <li>target_init_same: Whether the geographic target of a policy is the same as the policy initiator (e.g. target init same ==0 if lockdown policy implemented by government A in country A while target init same ==1 if an external border restrictions is im- plemented by government A against country B)</li> <li>target_intl_org: Which international organization a policy is targeted to, if applicable. 11</li> <li>target_who_gen: Information as to what special populations (e.g. asylum seekers, in- digenous peoples) a policy targets, if applicable</li> <li>date_end_spec: Qualitative information on a policy's end date</li> </ol>

Gravity-Driven Grainflows on Barchan Slipfaces: Field Data

Introduction Gravity-driven grainflows, or avalanches, occur in many environments on Earth, Mars, and elsewhere. Many aspects of their characteristic behavior have been revealed through laboratory and field studies. Some findings have been, however, inconclusive or contradictory. We present the results from the first comprehensive field study of 1609, gravity-driven grainflows, with measurements of their magnitude, frequency, area, speed, and related wind speeds and sand transport rates, made on the slipface of a 21 m high barchan dune.  Key findings support previous research, with relatively small sample sizes from shorter slipfaces, indicating that grainflow frequency increases with sand transport rate and grainflow speed increases with its area. We also found that grainflow magnitude increases with transport rate, which contradicts a commonly made assertion that grainflow magnitude is independent of the sand transport rate. We found significant scaling differences between laboratory and field data concerning the relationship between grainflow area and speed, with laboratory speeds, per unit area, more than two orders of magnitude faster than speeds found in the field. The implications of this work point to the importance of additional field studies and the hazard of using laboratory data alone for modeling grainflow behavior on terrestrial and extraterrestrial dunes. Here we present the data (the four spreadsheets in Natural grainflow dataset.xlsx) and metadata used to support a manuscript reporting our results and conclusions. The study site was on the slipface of a 20+m high barchan dune near Jericoacoara, Ceará, Brazil, and measurements made on November 7 and 9, 2013. The slipface was scanned repetitively with a Leica C10 terrestrial Laser scanner (TLS) on those two days. Details of TLS data processing and limitations are reported in Pelletier et al. (2015). Wind speeds were measured with Gill-type, DC-generating, cup anemometers. Sand transport rates were measured with sets of mesh-type traps described in Sherman et al. (2014). Grainflows that occurred near the apex of the slipface were video-recorded. More detail is provided for each of the individual spreadsheets to describe the measurement or derivation of data types. Spreadsheet 1: Grainflow Area and Speed Columns A and B report the date and time of each observation. Column C reports grainflow area (m2) as derived from Difference of Digital Elevation Models using the methods described in Zhang (2021). Column D reports grainflow speeds (ms-1) derived from DEM data (grainflow length) and video recordings (grainflow duration). Spreadsheet 2: Grainflow Frequency and Magnitude Columns A and B report the date and time of each observation. Column C reports the duration of a particular scan. Most scans required 7 minutes to complete and then a new scan began. Column D reports number of grainflows detected in an individual scan. Grainflow frequency, Column E, is normalized to one-minute equivalents. Average grainflow magnitude (m3) in Column F is derived from measurements of the volumes of the accretionary tails of all grainflows divided by the number of grainflows occurring during a scan. Spreadsheet 3: Wind Speeds Wind speed was measured with a set of three anemometers installed at 20 m spacing at the center of the top of the barchan slipface. Measurements at each anemometer were made at 1 Hz frequency. Columns A and B report the date and time of each observation. Column C reports average wind speeds (ms-1) obtained by block-averaging the 1 Hz data to one minute equivalent, and then averaging those data from the three anemometers. Spreadsheet 4: Sand Transport Rates Columns A and B report the date and time of each observation and Column C reports the duration of deployments of pairs of traps. Average transport rates in Column D were obtained by first averaging the trapped sand masses of the trap pair and then converting that value to an equivalent transport rate (gm-1s-1) for each sample period. The irregularly spaced point data were linearly interpolated to produce a time series with one-minute increments. References: Pelletier, J. D., Sherman, D. J., Ellis, J. T., Farrell, E. J., Jackson, N. L., Li, B., et al. (2015). Dynamics of sediment storage and release on aeolian dune slip faces: A field study in Jericoacoara, Brazil. Journal of Geophysical Research: Earth Surface, 120(9), 1911–1934. Sherman, D. J., Swann, C., & Barron, J. D. (2014). A high-efficiency, low-cost aeolian sand trap. Aeolian Research, 13, 31–34. Zhang, P. (2021). An algorithm for objective analysis of grainflow morphology, Aeolian Research, 50, 100686.

opencc-by-4.0Sep 2021View details →

Research data supporting: "A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields"

This repository contains the data used in the paper of Capelli et al. "A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields", published on Journal of Physical Chemistry B (DOI: 0.1021/acs.jpcb.1c02503). The archive traj_processed.tar.gz contains the trajectories converted in xyz format with the dimensions of the box. The archive trajectories_xtc.tar.gz contains the raw trajectories (of the membranes without solvent) in gromacs xtc format with a .tpr binary file.

opencc-by-4.0Oct 2022View details →

The Smart City as a Field of Innovation: Effects of Public-Private Data Collaboration on the Innovative Performance of Small and Medium-Sized Enterprises in China

Data is increasingly considered to be a key component in stimulating innovation (Cockburn, Henderson, and Stern, 2019). Numerous promising possibilities have been opened up by rapidly emerging, data-intensive technologies, including the Internet of Things (IoT) and artificial intelligence. The analysis and interpretation of big data are critical in the growth of technology firms in terms of AI training and computing capabilities (Allam and Dhunny, 2019). Small and medium-sized enterprises (SMEs), with their limited resources internally, particularly face a serious challenge of implementing innovation that depends upon data. The smart city provides an important opportunity for creating data-driven innovation. Significant amounts of data are increasingly available from various sources through sophisticated devices and equipment scattered in smart cities. Many smart city projects across the globe provide rich opportunities for SMEs to explore data-driven innovation (Bresciani, Ferraris, and Del Giudice, 2018). China, in particular, has recently been active in collecting and utilizing various kinds of data in smart cities. The availability of and access to data help to improve the software development of firms in China, where massive amounts of data resources are held by the government (Beraja, Yang, and Yuchtman, 2022). In the process of smart city development, there are also many tasks that are complementary to each other, including connecting databases, building online platforms that connect different data coming from different data sources, operating online platforms, and providing products and services to citizens. These diverse kinds of tasks involved in smart city projects initiated by local governments have brought about new business opportunities for innovative SMEs in China. To implement the policies of encouraging the development of SMEs by the central government, municipal governments have introduced policies that give priority to SMEs in participating in smart city projects (Ministry of Industry and Information Technology, 2016). Those companies that have access to the data held by government agencies are expected to benefit from utilizing the rich data for creating innovative products and services. There were few empirical studies conducted, however, to examine how data are actually managed and provided in smart cities and how they affect companies’ innovative activities. It remains unclear how public agencies and private enterprises collaborate on data and how that influences the innovation performance of SMEs in China. In smart cities, different types of public-private collaboration are involved, including hardware purchase, platform building, platform operation, and data analysis. It is not yet well-understood how these different types of collaboration influence the innovative performance of SMEs. In this study, we intend to address how data are managed through collaboration between the government and companies in smart cities and how the mode of collaboration influences firms’ performance on innovation. By focusing on the case of SMEs in China, this research aims to shed light on what kinds of data are available and used in smart cities and how the government and enterprises collaborate on data to facilitate innovation. The analysis of this study utilizes data on more than eight million contracts extracted from the official procurement database of the government. Data on companies are assembled with regard to the registered capital, industry, software products, and patents in 1990-2021 from the Tianyancha website. A panel data is established with key characteristics of SMEs, software and patents outputs, and their record on obtaining different government contracts annually. The government contracts are divided into three categories, namely, data analysis, platform building, and equipment supply, based on keyword identifications. To deal with the unbalance between the treatment group (the companies that obtained government contracts) and the control group, we use propensity score matching (one-to-one nearest neighbor matching) to narrow down the sample size of the control group to that of the treatment group. Then we apply the traditional difference-in-difference (DID) and  DID with multiple time periods (Callaway & Sant’Anna, 2021) methods to examine whether there are significant differences in innovative outputs of software products and patents before and after the companies receive government contracts. We also compare how the innovation performance of companies differs based on the types of contracts these companies obtain. That makes it possible to identify what kinds of data collaboration would be effective in improving the innovative performance of SMEs. Our preliminary analysis of average treatment effect suggests that obtaining the government contracts, especially research contracts and platform building type of contracts, can effectively help improve the innovation performance when comparing between the control and treatment group. After checking the common support graph and doing a balance test, the outcome is solid. In general, based on the average treatment effect on the treated group, innovation performance, as measured by the number of patents and software productions, has shown a significant increase for those companies that obtain a research type of contract, whereas the increase is smaller for the platform building contractor and slight for the equipment purchase contractor. Government purchase of equipment that contains data-intensive technologies promotes the use of these products in smart cities. That, however, does not involve any substantive exchange or transfer of data possessed by the government and would not significantly contribute to stimulating innovation at firms. This study will have useful implications for establishing public-private collaboration on data for facilitating innovation in smart cities. Specifically, government procurement should also be viewed as a policy tool for improving China's technological innovation. According to our findings, research and platform development can successfully assist firms in improving their innovation performance. As a result, the government should grant enterprises access to additional data resources and data management related tasks if they want to see more industry structural transformation, as stated in the government documents.