S3-Compatible Storage Market Outlook

According to our latest research, the global S3-Compatible Storage market size reached USD 7.4 billion in 2024. The market is experiencing robust momentum and is projected to expand at a CAGR of 16.8% from 2025 to 2033. By the end of the forecast period, the S3-Compatible Storage market is expected to reach USD 36.1 billion by 2033. This impressive growth is fueled by the surging demand for scalable, cost-effective storage solutions across diverse sectors, driven by exponential data generation and the increasing adoption of cloud-native architectures.

A primary growth factor for the S3-Compatible Storage market is the rapid digitization and transformation of business operations worldwide. Organizations are moving away from traditional storage solutions, seeking flexible and scalable storage platforms that can handle vast and dynamic data sets. The proliferation of IoT devices, big data analytics, and artificial intelligence applications has created an unprecedented need for robust storage infrastructures. S3-compatible storage solutions, with their seamless integration capabilities and API-driven architectures, have become the backbone for enterprises aiming to modernize their IT environments. This shift is further amplified by the growing trend of hybrid and multi-cloud deployments, where interoperability and data mobility are critical.

Another significant driver is the accelerating adoption of cloud computing across industries such as BFSI, healthcare, and media & entertainment. As organizations migrate workloads to public and private clouds, the need for storage solutions that are both highly available and easily accessible becomes paramount. S3-compatible storage offers a standardized interface, simplifying data management and access across disparate cloud environments. This compatibility is particularly valuable for businesses leveraging AWS S3 as a de facto standard, enabling them to avoid vendor lock-in and ensure business continuity. The rise in remote work and digital collaboration has also contributed to the demand, as enterprises require secure, scalable, and high-performance storage to support distributed teams and workflows.

The S3-Compatible Storage market is also benefitting from advancements in data protection, compliance, and security features. With increasing regulatory requirements such as GDPR and HIPAA, organizations are under pressure to ensure data integrity, privacy, and recoverability. S3-compatible storage solutions now offer sophisticated features like versioning, encryption, and lifecycle management, enabling organizations to address compliance mandates effectively. Moreover, the growing threat of cyberattacks and ransomware has made robust backup and disaster recovery capabilities essential. S3-compatible storage, with its immutability and cross-region replication features, is emerging as a preferred choice for organizations looking to safeguard their critical assets.

In the realm of digital transformation, Multi-Cloud Storage is becoming increasingly significant. As organizations seek to optimize their IT infrastructure, the ability to distribute data across multiple cloud platforms offers unparalleled flexibility and resilience. Multi-Cloud Storage enables businesses to avoid vendor lock-in, ensuring they can leverage the best features and pricing from different cloud providers. This approach not only enhances data availability and redundancy but also allows for strategic workload distribution, which can lead to cost savings and improved performance. As enterprises continue to adopt multi-cloud strategies, the demand for solutions that facilitate seamless data management across diverse environments is expected to grow, further driving the evolution of the S3-Compatible Storage market.

Regionally, North America dominates the S3-Compatible Storage market, accounting for the largest revenue share in 2024. The region's leadership is attributed to the early adoption of cloud technologies, a mature digital ecosystem, and the presence of major cloud service providers. Europe and Asia Pacific are also witnessing rapid growth, driven by digital transformation initiatives, increasing investments in IT infrastructure, and the expansion of data centers. Asia Pacific, in particular, is expected to register the highest CAGR during the forecast period, propelled by the burgeoning digital economy in c

Reservoir reconstruction, where parameter prediction plays a key role, constitutes an extremely important part in oil and gas reservoir exploration. With the mature development of artificial intelligence, parameter prediction methods are gradually shifting from previous petrophysical models to deep learning models, which bring about obvious improvements in terms of accuracy and efficiency. However, it is difficult to achieve large amount of data acquisition required for deep learning due to the cost of detection, technical difficulties, and the limitations of complex geological parameters. To address the data shortage problem, a transfer learning prediction model based on long short-term memory neural networks has been proposed, and the model structure has been determined by parameter search and optimization methods in this paper. The proposed approach transfers knowledge from historical data to enhance new well prediction by sharing some parameters in the neural network structure. Moreover, the practicality and effectiveness of this method was tested by comparison based on two block datasets. The results showed that this method could significantly improve the prediction accuracy of the reservoir parameters in the event of data shortage.

S3 Object Storage for DC Video Analytics Market Outlook

According to our latest research, the global S3 Object Storage for DC Video Analytics market size reached USD 2.41 billion in 2024, reflecting robust adoption across data centers and enterprises globally. The market is exhibiting a strong compound annual growth rate (CAGR) of 18.6% and is projected to soar to USD 11.81 billion by 2033. This impressive growth is primarily driven by the increasing need for scalable, cost-effective, and secure storage solutions to support the exponential rise in video data generated by digital surveillance and analytics applications.

The surge in demand for S3 Object Storage for DC Video Analytics is underpinned by the proliferation of advanced surveillance systems and the growing adoption of high-definition video cameras across data centers and enterprise environments. The market is witnessing a paradigm shift as organizations move away from traditional block and file storage architectures towards object storage solutions that offer superior scalability, flexibility, and durability. As video analytics becomes integral to security, operational efficiency, and business intelligence, the need for efficient storage and retrieval of vast volumes of unstructured video data is fueling this market’s expansion. Furthermore, the integration of artificial intelligence and machine learning into video analytics workflows necessitates rapid access to large datasets, making S3 object storage an ideal backbone for these applications.

Another significant growth driver for the S3 Object Storage for DC Video Analytics market is the rising trend of hybrid and multi-cloud deployments. Enterprises and data centers are increasingly leveraging cloud-native object storage services to ensure business continuity, disaster recovery, and seamless data mobility across on-premises and cloud environments. The pay-as-you-go pricing model and inherent elasticity of S3-compatible storage solutions enable organizations to handle unpredictable video data growth without incurring excessive capital expenditures. Additionally, regulatory requirements for long-term video retention, especially in sectors like government, banking, and healthcare, are compelling organizations to adopt object storage that can support petabyte-scale archival with robust data integrity and compliance features.

The market is also benefitting from technological advancements such as end-to-end encryption, immutability, and automated tiering, which enhance data security and optimize storage costs for video analytics workloads. Service providers are investing in intelligent data management capabilities, including metadata tagging, lifecycle policies, and integration with analytics engines, to deliver greater value to end-users. As edge computing and IoT devices proliferate, the volume and velocity of video data generated at the edge are driving further demand for scalable S3 object storage solutions that can seamlessly extend from core data centers to remote sites. These factors collectively contribute to the robust growth trajectory of the market.

Regionally, North America remains the largest market for S3 Object Storage for DC Video Analytics, accounting for over 38% of global revenue in 2024. This dominance is attributed to the early adoption of cloud-native technologies, strong presence of leading technology vendors, and high investments in smart city and security infrastructure. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid digital transformation, increasing urbanization, and the expansion of hyperscale data centers. Europe, Latin America, and the Middle East & Africa are also experiencing steady growth, supported by rising awareness of data compliance and the need for resilient storage solutions to support video analytics initiatives.

Component Analysis

The Component segment of the S3 Object Storage for DC Video

Introduction

MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.

Quick Links

• Original work and data extracted from https://github.com/alexa/massive
• MASSIVE paper
• MASSIVE Leaderboard and Massively Multilingual NLU 2022 Competition
• MASSIVE Blog Post

Accessing and Processing the Data

The dataset can be downloaded here.

$ curl https://amazon-massive-nlu-dataset.s3.amazonaws.com/amazon-massive-dataset-1.0.tar.gz --output amazon-massive-dataset-1.0.tar.gz
$ tar -xzvf amazon-massive-dataset-1.0.tar.gz
$ tree 1.0
1.0
├── LICENSE
└── data
  ├── af-ZA.jsonl
  ├── am-ET.jsonl
  ├── ar-SA.jsonl
  ...

The dataset is organized into files of JSON lines. Each locale (according to ISO-639-1 and ISO-3166 conventions) has its own file containing all dataset partitions. An example JSON line for de-DE has the following:

{
 "id": "0",
 "locale": "de-DE",
 "partition": "test",
 "scenario": "alarm",
 "intent": "alarm_set",
 "utt": "weck mich diese woche um fünf uhr morgens auf",
 "annot_utt": "weck mich [date : diese woche] um [time : fünf uhr morgens] auf",
 "worker_id": "8",
 "slot_method": [
  {
   "slot": "time",
   "method": "translation"
  },
  {
   "slot": "date",
   "method": "translation"
  }
 ],
 "judgments": [
  {
   "worker_id": "32",
   "intent_score": 1,
   "slots_score": 0,
   "grammar_score": 4,
   "spelling_score": 2,
   "language_identification": "target"
  },
  {
   "worker_id": "8",
   "intent_score": 1,
   "slots_score": 1,
   "grammar_score": 4,
   "spelling_score": 2,
   "language_identification": "target"
  },
  {
   "worker_id": "28",
   "intent_score": 1,
   "slots_score": 1,
   "grammar_score": 4,
   "spelling_score": 2,
   "language_identification": "target"
  }
 ]
}

id: maps to the original ID in the SLURP collection. Mapping back to the SLURP en-US utterance, this utterance served as the basis for this localization.

locale: is the language and country code accoring to ISO-639-1 and ISO-3166.

partition: is either train, dev, or test, according to the original split in SLURP.

scenario: is the general domain, aka "scenario" in SLURP terminology, of an utterance

intent: is the specific intent of an utterance within a domain formatted as {scenario}_{intent}

utt: the raw utterance text without annotations

annot_utt: the text from utt with slot annotations formatted as [{label} : {entity}]

worker_id: The obfuscated worker ID from MTurk of the worker completing the localization of the utterance. Worker IDs are specific to a locale and do not map across locales.

slot_method: for each slot in the utterance, whether that slot was a translation (i.e., same expression just in the target language), localization (i.e., not the same expression but a different expression was chosen more suitable to the phrase in that locale), or unchanged (i.e., the original en-US slot value was copied over without modification).

judgments: Each judgment collected for the localized utterance has 6 keys. worker_id is the obfuscated worker ID from MTurk of the worker completing the judgment. Worker IDs are specific to a locale and do not map across locales, but are consistent across the localization tasks and the judgment tasks, e.g., judgment worker ID 32 in the example above may appear as the localization worker ID for the localization of a different de-DE utterance, in which case it would be the same worker.

intent_score : "Does the sentence match the intent?"
 0: No
 1: Yes
 2: It is a reasonable interpretation of the goal

slots_score : "Do all these terms match the categories in square brackets?"
 0: No
 1: Yes
 2: There are no words in square brackets (utterance without a slot)

grammar_score : "Read the sentence out loud. Ignore any spelling, punctuation, or capitalization errors. Does it sound natural?"
 0: Completely unnatural (nonsensical, cannot be understood at all)
 1: Severe errors (the meaning cannot be understood and doesn't sound natural in your language)
 2: Some errors (the meaning can be understood but...

PUDL v2025.2.0 Data Release

This is our regular quarterly release for 2025Q1. It includes updates to all the datasets that are published with quarterly or higher frequency, plus initial verisons of a few new data sources that have been in the works for a while.

One major change this quarter is that we are now publishing all processed PUDL data as Apache Parquet files, alongside our existing SQLite databases. See Data Access for more on how to access these outputs.

Some potentially breaking changes to be aware of:

In the EIA Form 930 – Hourly and Daily Balancing Authority Operations Report a number of new energy sources have been added, and some old energy sources have been split into more granular categories. See Changes in energy source granularity over time.

We are now running the EPA’s CAMD to EIA unit crosswalk code for each individual year starting from 2018, rather than just 2018 and 2021, resulting in more connections between these two datasets and changes to some sub-plant IDs. See the note below for more details.

Many thanks to the organizations who make these regular updates possible! Especially GridLab, RMI, and the ZERO Lab at Princeton University. If you rely on PUDL and would like to help ensure that the data keeps flowing, please consider joining them as a PUDL Sustainer, as we are still fundraising for 2025.

New Data

EIA 176

Add a couple of semi-transformed interim EIA-176 (natural gas sources and dispositions) tables. They aren’t yet being written to the database, but are one step closer. See #3555 and PRs #3590, #3978. Thanks to @davidmudrauskas for moving this dataset forward.

Extracted these interim tables up through the latest 2023 data release. See #4002 and #4004.

EIA 860

Added EIA 860 Multifuel table. See #3438 and #3946.

FERC 1

Added three new output tables containing granular utility accounting data. See #4057, #3642 and the table descriptions in the data dictionary:

out_ferc1_yearly_detailed_income_statements

out_ferc1_yearly_detailed_balance_sheet_assets

out_ferc1_yearly_detailed_balance_sheet_liabilities

SEC Form 10-K Parent-Subsidiary Ownership

We have added some new tables describing the parent-subsidiary company ownership relationships reported in the SEC’s Form 10-K, Exhibit 21 “Subsidiaries of the Registrant”. Where possible these tables link the SEC filers or their subsidiary companies to the corresponding EIA utilities. This work was funded by a grant from the Mozilla Foundation. Most of the ML models and data preparation took place in the mozilla-sec-eia repository separate from the main PUDL ETL, as it requires processing hundreds of thousands of PDFs and the deployment of some ML experiment tracking infrastructure. The new tables are handed off as nearly finished products to the PUDL ETL pipeline. Note that these are preliminary, experimental data products and are known to be incomplete and to contain errors. Extracting data tables from unstructured PDFs and the SEC to EIA record linkage are necessarily probabalistic processes.

See PRs #4026, #4031, #4035, #4046, #4048, #4050 and check out the table descriptions in the PUDL data dictionary:

out_sec10k_parents_and_subsidiaries

core_sec10k_quarterly_filings

core_sec10k_quarterly_exhibit_21_company_ownership

core_sec10k_quarterly_company_information

Expanded Data Coverage

EPA CEMS

Added 2024 Q4 of CEMS data. See #4041 and #4052.

EPA CAMD EIA Crosswalk

In the past, the crosswalk in PUDL has used the EPA’s published crosswalk (run with 2018 data), and an additional crosswalk we ran with 2021 EIA 860 data. To ensure that the crosswalk reflects updates in both EIA and EPA data, we re-ran the EPA R code which generates the EPA CAMD EIA crosswalk with 4 new years of data: 2019, 2020, 2022 and 2023. Re-running the crosswalk pulls the latest data from the CAMD FACT API, which results in some changes to the generator and unit IDs reported on the EPA side of the crosswalk, which feeds into the creation of core_epa_assn_eia_epacamd.

The changes only result in the addition of new units and generators in the EPA data, with no changes to matches at the plant level. However, the updates to generator and unit IDs have resulted in changes to the subplant IDs - some EIA boilers and generators which previously had no matches to EPA data have now been matched to EPA unit data, resulting in an overall reduction in the number of rows in the core_epa_assn_eia_epacamd_subplant_ids table. See issues #4039 and PR #4056 for a discussion of the changes observed in the course of this update.

EIA 860M

Added EIA 860m through December 2024. See #4038 and #4047.

EIA 923

Added EIA 923 monthly data through September 2024. See #4038 and #4047.

EIA Bulk Electricity Data

Updated the EIA Bulk Electricity data to include data published up through 2024-11-01. See #4042 and PR #4051.

EIA 930

Updated the EIA 930 data to include data published up through the beginning of February 2025. See #4040 and PR #4054. 10 new energy sources were added and 3 were retired; see Changes in energy source granularity over time for more information.

Bug Fixes

Fix an accidentally swapped set of starting balance / ending balance column rename parameters in the pre-2021 DBF derived data that feeds into core_ferc1_yearly_other_regulatory_liabilities_sched278. See issue #3952 and PRs #3969, #3979. Thanks to @yolandazzz13 for making this fix.

Added preliminary data validation checks for several FERC 1 tables that were missing it #3860.

Fix spelling of Lake Huron and Lake Saint Clair in out_vcerare_hourly_available_capacity_factor and related tables. See issue #4007 and PR #4029.

Quality of Life Improvements

We added a sources parameter to pudl.metadata.classes.DataSource.from_id() in order to make it possible to use the pudl-archiver repository to archive datasets that won’t necessarily be ingested into PUDL. See this PUDL archiver issue and PRs #4003 and #4013.

Other PUDL v2025.2.0 Resources

PUDL v2025.2.0 Data Dictionary

PUDL v2025.2.0 Documentation

PUDL in the AWS Open Data Registry

PUDL v2025.2.0 in a free, public AWS S3 bucket: s3://pudl.catalyst.coop/v2025.2.0/

PUDL v2025.2.0 in a requester-pays GCS bucket: gs://pudl.catalyst.coop/v2025.2.0/

Zenodo archive of the PUDL GitHub repo for this release

PUDL v2025.2.0 release on GitHub

PUDL v2025.2.0 package in the Python Package Index (PyPI)

Contact Us

If you're using PUDL, we would love to hear from you! Even if it's just a note to let us know that you exist, and how you're using the software or data. Here's a bunch of different ways to get in touch:

Follow us on GitHub

Use the PUDL Github issue tracker to let us know about any bugs or data issues you encounter

GitHub Discussions is where we provide user support.

Watch our GitHub Project to see what we're working on.

Email us at hello@catalyst.coop for private communications.

On Mastodon: @CatalystCoop@mastodon.energy

On BlueSky: @catalyst.coop

On Twitter: @CatalystCoop

Connect with us on LinkedIn

Play with our data and notebooks on Kaggle

Combine our data with ML models on HuggingFace

Learn more about us on our website: https://catalyst.coop

Subscribe to our announcements list for email updates.

Newly emerging plants provide the best forage for herbivores. To exploit this fleeting resource, migrating herbivores align their movements to surf the wave of spring green-up. With new technology to track migrating animals, the Green Wave Hypothesis has steadily gained empirical support across a diversity of migratory taxa. This hypothesis assumes the green wave is controlled by variation in climate, weather, and topography, and its progression dictates the timing, pace, and extent of migrations. However, aggregate grazers that are also capable of engineering grassland ecosystems make some of the world’s most impressive migrations, and it is unclear how the green wave determines their movements. Here we show that Yellowstone’s bison (Bison bison) do not choreograph their migratory movements to the wave of spring green-up. Instead, bison modify the green wave as they migrate and graze. While most bison surfed during early spring, they eventually slowed and let the green wave pass them by. However, small-scale experiments indicated that feedback from grazing sustained forage quality. Most importantly, a 6-fold decadal shift in bison density revealed that intense grazing caused grasslands to green up faster, more intensely, and for a longer duration. Our finding broadens our understanding of the ways in which animal movements underpin the foraging benefit of migration. The widely accepted Green Wave Hypothesis needs to be revised to include large aggregate grazers that not only move to find forage, but also engineer plant phenology through grazing, thereby shaping their own migratory movements.

Methods Details of how these data were collected can be found in the Methods and Supplementary Materials of Geremia et al. 2019, Migrating bison engineer the green wave (Proceedings of the National Academy of Science). A brief summary of each dataset follows:

bisonsurfdata.csv - Data to replicate analysis of green-wave surfing in bison. See Methods and Text S1 in Supplementary text for details. Columns: id = animal id; year = year of day of animal location; jul = julian date of animal location; maxIRGdate = julian date of max IRG for the location.

fecaldata.csv - Data to replicate analysis of bison diet quality over time. See Methods for details. Columns: year = year fecal sample was collected; julianday = julian day fecal sample was collected; CP = crude protien of sample; DOM = digestible organic matter of sample.

leafdata.csv - Data to replicate anlaysis of plant-forage quality as it relates to days from peak IRG. See Methods and Text S1 for details. Columns: year = year plant tissue sample was collected; julianday = julian day plant tissue sample was collected; leafN = N of sample; leafC = C of sample; NETDFPIRG = absolute value of the number of days between the julian date the sample was collected and the julian day of peak IRG for the pixel where the sample was collected.

functionalNDVIdata.csv - Data to replicate functional NDVI analysis. See Methods and Text S3, S4, S5, and S6 for details. Columns: site = name of grazing experiment site; year = year of NDVI data; bisonuseindex = grazing intensity index; swe = Snow Water Equivelant value; precip = precipitation value; temp = temperature value; slope = slope of site in degrees; aspect = aspect of site in degrees; elev = elevation of site; columns 10 through 42 = NDVI values for julian dates 57 through 313 at 8 day intervals.

grazingexperimentdata.csv - Data to replicate grazing experiment analysis. See Methods and Text S3 and S5 for details. Columns: siteyrid = site and year combined; site = name of grazing experiment site; year = year of data collection; julianday = julian day of data collection; plottype = type of plot, control (for plots within fenced exclosure) or experimental (plots outside in grazed areas); plotnumber = both control and experimental plots were replicated with up to 6 replicates each; shootbiomass = shoot biomass of sample; leafN = N of sample; leafC = C of sample; SiteAnnualgrazingintensity = grazing intensity index for the year. Note that site grazing intensity can be slightly less than 0 due to sampling variation. See Text S3 in regards to "adding positive and negative increments."

grazingintensitydata.csv - Data to to build the linear relationship between field measured grazing intensity and landscape modeled grazing intensity. See Text S3 and S5 for details. Columns: siteyrid = site and year combined; site = name of grazing experiment site; year = year; grazingintensity = is field measured grazing intensity using the plot data; bisonuseindex = the averaged value for bison use from scaled Brownian Bridge Movement Models for the larger areas around each grazing experiment site.

fullspringbisonsurfdata.csv – Additional data to replicate analysis of green-wave surfing in bison described in Geremia et al. 2020 Response to Craine: Bison redefine what it means to move to find food. Columns are the same as described in bisonsurfdata.csv. Data file includes locations as described in the response letter.

Run analyses.R – Native R code to replicate analyses in Geremia et al. 2019 and Geremia et al. 2020. Run by downloading and unzipping files to a user defined working directory and specifying the working directory in the code header.

S3‑Compatible Object Storage Software Market Outlook

According to our latest research, the global S3-Compatible Object Storage Software market size reached USD 6.2 billion in 2024. This dynamic market is experiencing robust expansion, underpinned by a strong compound annual growth rate (CAGR) of 13.8% from 2025 to 2033. By the end of the forecast period, the market is projected to attain a value of USD 19.3 billion by 2033. The primary growth factor driving this surge is the exponential increase in unstructured data volumes across industries, necessitating scalable, flexible, and cost-effective data storage solutions that S3-compatible platforms uniquely provide.

The proliferation of digital transformation initiatives and cloud-native applications is a major catalyst for the S3-Compatible Object Storage Software market. Modern enterprises are generating unprecedented amounts of data, much of it unstructured, such as multimedia files, documents, and IoT sensor data. Traditional storage solutions struggle with the scalability and flexibility required to manage these data types efficiently. S3-compatible object storage software offers a highly scalable, cost-effective, and resilient alternative, supporting organizations in managing data growth without sacrificing performance or security. Furthermore, the adoption of hybrid and multi-cloud strategies is accelerating, as businesses seek to avoid vendor lock-in and optimize their IT infrastructure, making S3 compatibility a crucial requirement for seamless integration across platforms.

Another significant growth driver is the increasing demand for advanced data analytics, artificial intelligence, and machine learning workloads. These applications require rapid access to massive datasets, often stored in disparate locations. S3-compatible object storage software delivers robust APIs and seamless integration capabilities, enabling organizations to harness the full potential of their data assets. Enhanced support for backup and disaster recovery, archiving, and content delivery further amplifies the value proposition, making these solutions indispensable for industries with stringent compliance and data durability requirements. The market is also benefiting from the rising emphasis on data sovereignty and regulatory compliance, as S3-compatible solutions offer customizable deployment options to meet diverse governance needs.

Cloud adoption trends are reshaping the global storage landscape, with organizations increasingly migrating critical workloads to public, private, and hybrid clouds. S3-compatible object storage software is pivotal in this transition, providing a unified interface and interoperability across cloud environments. This interoperability is particularly attractive to multinational corporations and enterprises operating in regulated sectors, where data locality and cross-border data transfer regulations are paramount. The integration of advanced security features, such as encryption at rest and in transit, multi-tenancy, and access control, further enhances the appeal of S3-compatible platforms for mission-critical applications. As a result, the market is witnessing strong investments from both established players and innovative startups, fostering a competitive ecosystem that continually pushes the boundaries of performance, scalability, and cost efficiency.

Regionally, North America leads the S3-Compatible Object Storage Software market, driven by rapid technological adoption, a mature cloud computing ecosystem, and significant investments in digital infrastructure. Europe follows closely, with robust demand from BFSI, healthcare, and government sectors, all of which are subject to stringent data protection regulations. The Asia Pacific region is emerging as a high-growth market, fueled by the digitalization of enterprises, expansion of e-commerce, and increasing cloud adoption among SMEs. Latin America and the Middle East & Africa are also witnessing steady growth, supported by rising investments in IT modernization and the proliferation of digital services. Each region exhibits unique adoption patterns, influenced by local regulatory frameworks, industry verticals, and the maturity of digital infrastructure.

BatchData's property listings data provides comprehensive insights with over 140 data points and nationwide listing data inclusive of For Sale By Owner (FSBO) listings across the United States. Updated daily in most markets, the data includes:

• Listing Details: property listings descriptions, property characteristics, pricing, days on market, and more.
• Agent Information: agent names, license numbers, contact details, listing counts, and listing histories.
• Broker Information: Broker names, locations, URLs, emails, phone numbers, and licensing information.
• Additional Details: Information about schools, neighborhoods, subdivisions, and tax data.

Common Use Cases: - Recruiting Teams: Enhance talent acquisition by analyzing agents' listing counts, close rates, property types, and client profiles. - Proptech Software & Marketplaces: Integrate current and historical listings to create detailed property profiles, advanced search features, and robust analytics. - Home Service Providers: Target marketing and outreach efforts to homeowners, whether they are preparing to move or have recently relocated. - Real Estate Agents & Investors: Identify undervalued properties, connect with buyers/sellers based on activity, analyze market trends, and develop effective marketing strategies.

Our property listings data can be delivered in a variety of formats to suit your needs. Choose from API integration for seamless, real-time data access, bulk data delivery for extensive datasets, S3 bucket storage for scalable cloud solutions, and more. This flexibility ensures that you can incorporate our comprehensive property information into your systems efficiently and effectively, whether you're building a new platform, enhancing existing tools, or conducting in-depth analyses.

Comprehensive dataset of S3-compatible storage providers including pricing, performance benchmarks, regional availability, and technical specifications.

Identifying antigens that elicit protective immunity is pivotal for developing effective vaccines and therapeutics against cutaneous leishmaniasis. Dihydrolipoyl dehydrogenase (DLD), a mitochondrial enzyme involved in oxidizing lipoamides to facilitate electron transfer for energy production and metabolism, plays a critical role in virulence of fungi and bacteria. However, its function in Leishmania virulence and pathogenesis remains unexplored. Using a CRISPR-Cas9-based approach, we generated DLD-deficient Leishmania (L.) major parasites and a complementary add-back strain by episomally reintroducing DLD gene into the knockout parasites. Loss of DLD significantly impaired parasite proliferation in axenic cultures and infected macrophages compared to wild-type (WT) and add-back control parasites. These defects were linked to reduced ROS production, impaired mitochondrial permeability, an enhanced oxygen consumption rate, and alterations in mitochondrial ultrastructure. In murine models, DLD-deficient parasites failed to cause observable lesions and exhibited significantly reduced parasite burdens compared to WT and add-back control strains. Notably, mice infected with DLD-deficient parasites displayed blunted immune responses compared to their WT controls. Importantly, vaccination with DLD-deficient parasites conferred robust protection against virulent L. major challenge, characterized by a strong IFN-γ-mediated immune response. These findings establish DLD as an essential metabolic enzyme for L. major intracellular survival and pathogenesis. Targeting DLD not only impairs parasite viability but also holds promise as a novel strategy for vaccine development to combat cutaneous leishmaniasis.

This foot traffic dataset provides GPS-based mobile movement signals from across South America. It is ideal for retailers, city agencies, advertisers, and real estate professionals seeking insights into how people move through physical locations and urban spaces.

Each record includes:

Device ID (IDFA or GAID) Timestamps (in milliseconds and readable format) GPS coordinates (lat/lon) Country code Horizontal accuracy (85%) Optional IP address, mobile carrier, and device model

Access the data via polygon queries (up to 10,000 tiles), and receive files in CSV, JSON, or Parquet, delivered hourly or daily via API, AWS S3, or Google Cloud. Data freshness is strong (95% delivered within 3 days), with full historical backfill available from September 2024.

This solution supports flexible credit-based pricing and is privacy-compliant under GDPR and CCPA.

Key Attributes:

Custom POI or polygon query capability Backfilled GPS traffic available across LATAM High-resolution movement with daily/hourly cadence GDPR/CCPA-aligned with opt-out handling Delivery via API or major cloud platforms

Use Cases:

Competitive benchmarking across malls or stores Transport and infrastructure planning Advertising attribution for outdoor/DOOH campaigns Footfall modeling for commercial leases City zoning, tourism, and planning investments Telecom & tower planning across developing corridors

Major Roads In Kenya.
This dataset falls under the category Individual Transport Street Network Geometries (Geodata).
It contains the following data: Major roads in Kenya.
This dataset was scouted on 2022-02-03 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://s3.amazonaws.com/wriorg/docs/ke_major-roads.zip URL for data access and license information.

Database accompanying the manuscript"MoveTraits - A database for integrating animal behaviour into trait-based ecology"https://doi.org/10.1101/2025.03.15.643440We present a proof-of-concept ‘MoveTraits’ database with 55 mammal and 108 bird species sources from open access datasets on movebank and a large open access dataset of terrestrial mammal movements published alongside Tucker et al (2023) https://doi.org/10.1126/science.abo6499 which can be downloaded here: https://zenodo.org/records/7704108. The database contains 5 movement traits across multiple time scales: displacement distance, maximum displacement, range size (MCP), intensity of use (IoU), and diurnality.MoveTraits provides movement trait data at three hierarchical levels: (1) summarised at the species level, facilitating interoperability with other species-level trait databases (files "MoveTrait.v0.1_species.sum_20250311" in file format rds and csv), (2) summarised at the individual level for studies on between-individual variation (files "MoveTrait.v0.1_individual.sum_20250311" in file format rds and csv), and (3) the underlying repeated movement trait estimates for each individual over time to allow for research questions at the intra-individual level (file "MoveTrait.v0.1_withinindividual_20250311" nested dataframe only available in rds format). We summarised the underlying repeated movement trait estimates at the individual level as mean, median, coefficient of variation, and 5th as well as 95th percentile and provided species means of centrality and variance (e.g., the mean species 95th percentile summarized from individual 95th percentiles for a given trait).Data owners contributing their data to the database are listed in Table S3.Code to reproduce the database version 0.1 can be found under the Open Science Framework (https://doi.org/10.17605/OSF.IO/SP8Z6).