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The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    TypeError
Message:      SplitInfo.__init__() got an unexpected keyword argument 'path'
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
                  config_names = get_dataset_config_names(
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1207, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1182, in dataset_module_factory
                  ).get_module()
                    ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 612, in get_module
                  dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 389, in from_dataset_card_data
                  dataset_info_yaml_dict.get("config_name", "default"): DatasetInfo._from_yaml_dict(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 319, in _from_yaml_dict
                  yaml_data["splits"] = SplitDict._from_yaml_list(yaml_data["splits"])
                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/splits.py", line 610, in _from_yaml_list
                  return cls.from_split_dict(yaml_data)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/splits.py", line 580, in from_split_dict
                  split_info = SplitInfo(**split_info)
                               ^^^^^^^^^^^^^^^^^^^^^^^
              TypeError: SplitInfo.__init__() got an unexpected keyword argument 'path'

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LexMetrix Nearshoring Latency Feed (NSLF)

The first open weekly operational latency index for the Caribbean Nearshoring → US East Coast corridor.

Built by LexMetrix · Updated weekly · License: CC BY 4.0


What this dataset is

The NSLF measures how smoothly cargo moves through the two main Dominican Republic ports — Puerto Caucedo (DP World) and Puerto Río Haina (HIT) — relative to their historical baseline, and benchmarks them against four regional competitors every week.

As US manufacturing nearshores to the Caribbean Basin under CAFTA-DR, port operational latency becomes a critical variable for supply chain planners. A single congestion event like the January 2025 Caucedo scanner failure (anchorage duration spiked to 11.6 days vs. a 0.6-day baseline) can cascade into missed delivery promises across dozens of manufacturers.

This feed turns that risk into a quantified, trackable, weekly number.


Latest reading — Week 2026-W23

Port NSLF Index Signal Corridor Rank
Caucedo (RD) 97.40 🟢 STABLE_CORRIDOR 2 / 5
Haina (RD) 101.51 🟢 STABLE_CORRIDOR
Kingston (JM) 94.08 🟢 STABLE_CORRIDOR 1 / 5
Balboa (PA) 100.99 🟢 STABLE_CORRIDOR 3 / 5
Cristóbal (PA) 101.39 🟢 STABLE_CORRIDOR 4 / 5
Cartagena (CO) 105.82 🟡 WATCH_CORRIDOR 5 / 5

Base 100 = normal historical conditions (CEIC 2022–2025). Lower = less congestion.


Files

output/
├── weekly_feed.parquet        ← Primary dataset. One row per port per week.
│                                Includes NSLF index, component scores,
│                                and all 4 regional comparative columns.
├── comparatives_feed.parquet  ← Regional ports table (Kingston, Cristóbal,
│                                Balboa, Cartagena). Same schema as weekly_feed.
└── disruption_log.csv         ← Documented port disruption events with cause,
                                 duration, and NSLF impact. The differentiator
                                 no competitor publishes.

Schema

weekly_feed.parquet

Column Type Description
week string ISO week, e.g. 2026-W23
port string caucedo or haina
nslf_index float Composite latency index. Base 100 = normal
nearshoring_signal string FAVORABLE_CORRIDOR / STABLE_CORRIDOR / WATCH_CORRIDOR / CONGESTED_CORRIDOR
anchorage_days float Mean days vessel waits at anchor before berthing
als_score float Anchorage Latency Score (40% weight)
port_stay_days float Mean days vessel stays from berth to departure
psds_score float Port Stay Duration Score (35% weight)
vessels_waiting int Vessels at anchor at time of snapshot
vqp_score float Vessel Queue Pressure (20% weight)
ecs_score float External Congestion Signal from GoComet/Portcast CSVs (5% weight)
comp_kingston_nslf float Kingston (JM) NSLF that week
comp_cristobal_nslf float Cristóbal (PA) NSLF that week
comp_balboa_nslf float Balboa (PA) NSLF that week
comp_cartagena_nslf float Cartagena (CO) NSLF that week
rd_advantage_vs_kingston float Caucedo minus Kingston NSLF. Negative = RD has less congestion
rd_advantage_vs_balboa float Caucedo minus Balboa NSLF
corridor_rank string Caucedo's ordinal rank in corridor, e.g. 2/5
data_mode string DEMO (synthetic calibrated) or LIVE (real AIS)

disruption_log.csv

Column Description
date Event date (YYYY-MM-DD) or ISO week
port Affected port(s)
event_type EQUIPMENT_FAILURE / CONGESTION_PEAK / INVESTMENT_ANNOUNCEMENT / INFRASTRUCTURE / ECS_AUTO_SPIKE
severity LOW / MEDIUM / HIGH
duration_days Estimated operational impact duration
description Human-readable event summary
source Original source (news outlet, market report, etc.)
nslf_impact Quantified effect on NSLF index where documented

Index Methodology

NSLF Index = ALS_score × 0.40
           + PSDS_score × 0.35
           + VQP_score  × 0.20
           + ECS_score  × 0.05

Where each score = (observed_value / historical_baseline) × 100

Signal thresholds:

Range Signal Interpretation
< 90 FAVORABLE_CORRIDOR Below-normal congestion. Competitive advantage over Asia.
90–110 STABLE_CORRIDOR Normal operations. Predictable for planning.
110–125 WATCH_CORRIDOR Elevated congestion. Monitor, adjust safety stock.
> 125 CONGESTED_CORRIDOR Crisis-level. Alert clients, review shipment promises.

Historical baselines (CEIC 2022–2025):

Port Anchorage (days) Port Stay (days) Vessels Waiting
Caucedo 0.6 1.0 21
Haina 0.4 1.0 15
Kingston 1.8 2.1 28
Cristóbal 0.9 1.4 35
Balboa 1.5 1.6 40
Cartagena 0.7 1.2 18

Documented Disruptions (selected)

Date Port Event Peak NSLF Impact
2025-01-08 Caucedo Scanner failure + post-holiday backlog. Protests by transporters. Anchorage spike to 11.6 days (CEIC, baseline 0.6)
2025-05-15 Caucedo Caribbean-wide congestion peak. 91% yard utilization. 2–6h vessel wait times. VQP estimated >130
2025-05-26 Caucedo DP World + RD Gov sign US$860M expansion MoU. Target: 1.2M → 2.5M+ TEU. Structural signal. Construction disruption risk 2025–2027.
2026-02-23 Nacional National blackout (transmission fault). NAVIS SPARCS N4 impact probable. PSDS disruption, duration ~hours

Data Sources

Layer Source Frequency Coverage
Port congestion baselines CEIC Data via MarineTraffic Weekly Caucedo, Haina 2022–2025
AIS vessel arrivals/departures VesselFinder Real-time → weekly aggregate All 6 ports
External congestion signal GoComet / Portcast exports As available RD ports
Disruption events Diario Libre, K+N Market Updates, Business Insider Event-driven Manual curation
Regional comparatives CEIC Panama, K+N Seaexplorer, Panama Ship Service Weekly Kingston, Cristóbal, Balboa, Cartagena

Current Status: DEMO Mode

All rows with data_mode = "DEMO" use synthetic data calibrated to CEIC historical baselines. The index structure, schema, comparative methodology, and disruption log are production-ready.

Transition to LIVE mode requires:

  1. VesselFinder API key (free tier: 100 req/day — sufficient for weekly pipeline) → Register: https://www.vesselfinder.com/api/plans
  2. Run: python pipeline.py --week 2026-W24 --apikey YOUR_KEY
  3. data_mode field changes automatically to LIVE

Why this dataset exists

Dominican Republic's free zone sector hit 200,134 direct jobs in December 2025 — a historic milestone. DP World's US$860M expansion targets 2.5M+ TEU capacity to compete directly with Kingston. ITEK, AAFA apparel brands, and medical device manufacturers are actively nearshoring to RD under CAFTA-DR duty-free access.

Yet no public weekly index existed to tell supply chain planners: how congested are Caucedo and Haina right now, and how does that compare to the alternatives?

This feed is that index.


Related datasets


Citation

@dataset{lexmetrix_nslf_2026,
  author    = {LexMetrix AltData},
  title     = {Nearshoring Latency Feed (NSLF): Caribbean Port Operational Intelligence},
  year      = {2026},
  publisher = {HuggingFace Datasets},
  url       = {https://huggingface.co/datasets/lexmetrix/nearshoring-latency-feed},
  note      = {Weekly. Covers Puerto Caucedo (DP World) and Puerto Río Haina (HIT),
               Dominican Republic, with regional comparatives for Kingston (JM),
               Cristóbal and Balboa (PA), and Cartagena (CO).}
}

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

You may use, adapt, and redistribute this dataset for any purpose, including commercial, provided you credit LexMetrix AltData.


Maintained by LexMetrix · Santo Domingo, Dominican Republic

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